SYSTEM, PROCESSOR, AND METHOD FOR MANAGING CUSTOMER FINANCIAL INFORMATION - Patent application

Abstract

An embodiment relates to a method and system for managing user financial information. The method includes performing a primary financial institution process, which includes generating inflows and outflows of value to and from all financial accounts held by the user at at least a first and a second financial institution, generating inflow / outflow ratios, identifying all scheduled and non-scheduled payments made by all financial accounts held at the first and second financial institutions, and generating an financial institution score based on at least one of the above. The method includes determining an financial institution ranking, determined by comparing the financial institution scores and ranking based on the comparison. The method includes generating product recommendations based on the financial institution score and / or the financial institution ranking.

Description

[Technical field]

[0001] FIELD OF THE DISCLOSURE This disclosure relates generally to systems, processors, and methods for managing user financial information. More specifically, the exemplary embodiments relate to systems, processors, and methods for generating personalized financial product recommendations and top picks for users, among other things. [Background technology]

[0002] Many first world countries are facing an ageing population. With human life expectancy increasing and many hit hard by various setbacks such as the pandemic and economic downturn, more and more people, regardless of socio-economic status, need assistance to achieve financial independence and improve their financial literacy. It is difficult for many to get a complete picture of their finances, ensure their needs are protected, and have adequate and adequate coverage, which makes them unable to plan for retirement. In addition, many people do not receive proper investment guidance, which results in them not investing or their investments not generating profits.

[0003] Various financial institutions and fintech companies have attempted to solve the aforementioned problems. One such attempt is the development of systems and methods that focus only on investment or savings. However, these systems and methods do not address both investment and savings, and fail to address issues across the board in financial planning, including budgeting, protection, and long-term goals. Another attempt is the development of systems and techniques that focus on solutions for investment, insurance, or savings products. However, these systems and methods do not assist with financial planning. Summary of the Invention [Problem to be solved by the invention]

[0004] As inflation progresses, it is becoming increasingly important for people to have proper financial planning and coverage. Financial planning and coverage includes having a holistic view of all of one's financial information (e.g., assets and liabilities, cash flow, investment holdings, protection plans, etc.), having the right products for one's needs (e.g., insurance plans with appropriate coverage given one's life stage or situation), insight into how well one's financial planning is going, and / or the goals and plans one has made for the future. As technological advancements accelerate, so should financial planning resources and platforms. For example, currently there are many financial platforms that focus on only one or two aspects of financial planning, such as only saving or only investing. Also, many of the current avenues for accessing financial advice are confusing, complex, difficult, or the like. This makes it difficult for users to get a full picture of their finances, easily manage their finances, products, and goals, and receive financial planning advice or recommendations.

[0005] The exemplary embodiments generally relate to and / or include systems, subsystems, processors, devices, logic, methods, and processes for addressing conventional problems, including those described above and in this disclosure, and more specifically, the exemplary embodiments relate to systems, subsystems, processors, devices, logic, methods, and processes for managing user financial information, including generating personalized financial product recommendations and selections for users, among other things. [Means for solving the problem]

[0006] In one exemplary embodiment, a method for managing user financial information is described that includes performing a primary financial institution process, the primary financial institution process including searching, for a first user, all financial institutions at which the first user has at least one financial account, including a first financial institution and a second financial institution.

[0007] The primary financial institution process also includes, for each financial institution identified by the search, generating a total inflow of value to all financial accounts held by the first user during the first time period, generating a total outflow of value from all financial accounts held by the first user during the first time period, generating an inflow / outflow ratio for the first user during the first time period based on the total inflow of value generated and the total outflow of value generated, identifying all periodic payments made by all financial accounts held by the first user during the first time period, identifying all non-periodic payments made by all financial accounts held by the first user during the first time period, generating a financial institution score for the first user based on at least one of the inflow / outflow ratios, and determining an financial institution ranking for the first user during the first time period.

[0008] Generating a total inflow of value into all financial accounts held by the first user in a first time period includes a first total inflow of value and a second total inflow of value, where the first total inflow of value is the total inflow of value into all financial accounts held by the first user at the first financial institution in the first time period and the second total inflow of value is the total inflow of value into all financial accounts held by the first user at the second financial institution in the first time period. Generating a total outflow of value from all financial accounts held by the first user in a first time period includes a first total outflow of value and a second total outflow of value, where the first total outflow of value is the total outflow of value from all financial accounts held by the first user at the first financial institution in the first time period and the second total outflow of value is the total outflow of value from all financial accounts held by the first user at the second financial institution in the first time period. Generating an inflow / outflow ratio for the first user in the first time period based on the total inflows of value generated and the total outflows of value generated includes a first inflow / outflow ratio and a second inflow / outflow ratio, where the first inflow / outflow ratio is a ratio of the first total inflow of value to the first total outflow of value and the second inflow / outflow ratio is a ratio of the second total inflow of value to the second total outflow of value. Identifying all periodic payments made by all financial accounts held by the first user in the first time period includes a first set of periodic payments and a second set of periodic payments, where the first set of periodic payments are all periodic payments made by all financial accounts held by the first user with the first financial institution in the first time period and the second set of periodic payments are all periodic payments made by all financial accounts held by the first user with the second financial institution in the first time period.The identification of all non-periodic payments made by all financial accounts held by the first user in a first time period includes a first set of non-periodic payments and a second set of non-periodic payments, the first set of non-periodic payments being all non-periodic payments made by all financial accounts held by the first user at the first financial institution in the first time period, and the second set of non-periodic payments being all non-periodic payments made by all financial accounts held by the first user at the second financial institution in the first time period. The generation of a financial institution score for the first user based on at least one of the inflow / outflow ratio, the identified periodic payments, and the non-periodic payments includes a first financial institution score and a second financial institution score, the first financial institution score being generated based on at least one of the first inflow / outflow ratio, the first set of periodic payments, and the first set of non-periodic payments, and the second financial institution score being generated based on at least one of the second inflow / outflow ratio, the second set of periodic payments, and the second set of non-periodic payments. Determining a financial institution ranking for the first user for the first time period includes determining a ranking of the financial institutions for the first user for the first time period by comparing financial institution scores for each of the financial institutions identified by the search, including the first financial institution score and the second financial institution score, and ranking the financial institutions identified by the search based on the comparison.

[0009] The method of managing user financial information also includes generating one or more product recommendations for the first user, where the one or more product recommendations for the first user are generated based on one or more of the financial institution scores generated for the first user and at least one of the financial institution rankings for the first user.

[0010] In another exemplary embodiment, a method of managing user financial information is described. The method includes performing a primary financial institution process for a first user, the primary financial institution process for the first user including searching for all financial institutions for the first user at which the first user has at least one financial account, including the first financial institution and a second financial institution. The primary financial institution process also includes performing, for each financial institution identified by the search, at least one of: generating an inflow / outflow ratio for the first user in a first time period; identifying all periodic payments made by all financial accounts held by the first user in the first time period; and identifying all non-periodic payments made by all financial accounts held by the first user in the first time period.

[0011] Generating an inflow / outflow ratio for the first user in the first time period includes generating a total inflow of value into all financial accounts held by the first user in the first time period, including a first total inflow of value and a second total inflow of value, where the first total inflow of value is a total inflow of value into all financial accounts held by the first user at the first financial institution in the first time period and the second total inflow of value is a total inflow of value into all financial accounts held by the first user at the second financial institution in the first time period. Generating an inflow / outflow ratio for the first user in the first time period also includes generating a total outflow of value from all financial accounts held by the first user in the first time period, including a first total outflow of value and a second total outflow of value, where the first total outflow of value is a total outflow of value from all financial accounts held by the first user at the first financial institution in the first time period and the second total outflow of value is a total outflow of value from all financial accounts held by the first user at the second financial institution in the first time period. Generating an inflow / outflow ratio for the first user in the first time period also includes generating an inflow / outflow ratio for the first user in the first time period based on the total inflows of value generated and the total outflows of value generated, including the first inflow / outflow ratio and the second inflow / outflow ratio, where the first inflow / outflow ratio is the ratio of the first total inflow of value to the first total outflow of value and the second inflow / outflow ratio is the ratio of the second total inflow of value to the second total outflow of value.

[0012] The identification of all periodic payments made by all financial accounts held by the first user during a first time period includes a first set of periodic payments and a second set of periodic payments, the first set of periodic payments being all periodic payments made by all financial accounts held by the first user with the first financial institution during the first time period, and the second set of periodic payments being all periodic payments made by all financial accounts held by the first user with the second financial institution during the first time period.

[0013] The identification of all non-periodic payments made by all financial accounts held by the first user in the first time period includes a first set of non-periodic payments and a second set of non-periodic payments, the first set of non-periodic payments being all non-periodic payments made by all financial accounts held by the first user with the first financial institution in the first time period, and the second set of non-periodic payments being all non-periodic payments made by all financial accounts held by the first user with the second financial institution in the first time period.

[0014] The primary financial institution process for the first user also includes determining a financial institution ranking for the first user for the first time period. The financial institution ranking for the first user for the first time period is determined by comparing the financial institution scores for each of the financial institutions identified by the search, including the first financial institution score and the second financial institution score, and ranking the financial institutions identified by the search based on the comparison.

[0015] The method for managing user financial information also includes generating one or more product recommendations for the first user, where the one or more product recommendations for the first user are generated based on one or more of the financial institution scores generated for the first user and at least one of the financial institution rankings for the first user.

[0016] In another exemplary embodiment, a method of managing user financial information is described that includes receiving user data for a first user, performing a primary financial institution process, generating a digital value capture score for the first user, generating a product propensity score for the first user, generating real-time financial product information for one or more financial products, generating a financial personality for the first user, and generating one or more product recommendations for the first user.

[0017] Receiving user data for the first user includes at least one of: one or more customer declared information of the first user, including information provided by the first user; one or more financial institution information of the first user, including information obtainable from one or more financial accounts at one or more financial institutions held by the first user; one or more social media information of the first user, including information obtainable from one or more social media accounts held by the first user; and one or more personal preference information of the first user, including financial goals, financial objectives, life stage of the first user, financial stage of the first user, and financial preferences of the first user.

[0018] Executing the primary financial institution process includes searching for the first user for all financial institutions where the first user has at least one financial account, including the first financial institution and the second financial institution. Execution of the primary financial institution process includes generating, for each financial institution identified by the search, total inflows of value into all financial accounts held by the first user in a first time period, including a first total inflow of value and a second total inflow of value, where the first total inflow of value is a total inflow of value into all financial accounts held by the first user at the first financial institution in the first time period and the second total inflow of value is a total inflow of value into all financial accounts held by the first user at the second financial institution in the first time period; and generating total outflows of value from all financial accounts held by the first user in the first time period, including a first total outflow of value and a second total outflow of value, where the first total outflow of value is a total outflow of value from all financial accounts held by the first user at the first financial institution in the first time period and the second total outflow of value is a total outflow of value from all financial accounts held by the first user at the second financial institution in the first time period. generating an inflow / outflow ratio for the first user in a first time period based on the total inflows of value generated and the total outflows of value generated, including a first inflow / outflow ratio and a second inflow / outflow ratio, where the first inflow / outflow ratio is a ratio of the first total inflow of value to the first total outflow of value and the second inflow / outflow ratio is a ratio of the second total inflow of value to the second total outflow of value; identifying all periodic payments made by all financial accounts held by the first user in the first time period, including a first set of periodic payments and a second set of periodic payments, where the first set of periodic payments are all periodic payments made by all financial accounts held by the first user at the first financial institution in the first time period and the second set of periodic payments are all periodic payments made by all financial accounts held by the first user at the second financial institution in the first time period;identifying all non-periodic payments made by all financial accounts held by the first user in a first time period, including a first set of non-periodic payments and a second set of non-periodic payments, where the first set of non-periodic payments are all non-periodic payments made by all financial accounts held by the first user at the first financial institution in the first time period and the second set of non-periodic payments are all non-periodic payments made by all financial accounts held by the first user at the second financial institution in the first time period; and generating an institution score for the first user based on at least one of the inflow / outflow ratio, the identified periodic payments, and the non-periodic payments, including a first institution score and a second institution score, where the first institution score is generated based on at least one of the first inflow / outflow ratio, the first set of periodic payments, and the first set of non-periodic payments and the second institution score is generated based on at least one of the second inflow / outflow ratio, the second set of periodic payments, and the second set of non-periodic payments.

[0019] Performing the primary financial institution process also includes determining a financial institution ranking for the first user for the first time period, where the ranking of the financial institutions for the first user for the first time period is determined by comparing the financial institution scores for each of the financial institutions identified by the search, including the first financial institution score and the second financial institution score, and ranking the financial institutions identified by the search based on the comparison.

[0020] Generating a digital value capture score for the first user, representative of the first user's preferences for engaging digitally, includes generating a digital value capture score for the first user based on at least one of: one or more transactions made by the first user, one or more channels used by the first user, one or more investment financial products purchased by the first user, a number of times the first user used digital channels, a number of times the first user used non-digital channels, a number of times the first user interacted interactively based on digital communications, and a number of times the first user interacted interactively based on non-digital communications.

[0021] Generating a product propensity score for the first user representative of the first user's likely interest in one or more financial products includes generating a product propensity score for the first user based on at least one of the CASA balance, the one or more financial institution scores, the balance of one or more financial accounts held by the first user, and the first user's income.

[0022] Generating the real-time financial product information for the one or more financial products includes generating the real-time financial product information based on at least one of a product tenor, a product risk rating, a sophistication level of the financial product, a financial objective of the financial product, a risk capacity assessment of the financial product, and a conviction rating of the financial product.

[0023] Generating a financial personality for the first user, which is a psychometric assessment of the first user to determine personality and / or behavioral characteristics related to different aspects of the first user's financial planning, includes generating a financial personality for the first user based on at least one of the first user's savings personality, the first user's spending personality, the first user's investment personality, the first user's protection personality, and the first user's debt personality.

[0024] Generating one or more financial product recommendations for the first user includes generating based on one or more of the financial institution scores generated for the first user and / or the financial institution rankings for the first user, the user data for the first user, the digital value capture score for the first user, the product propensity score for the first user, the real-time financial product information for the one or more financial products, and a financial personality of the first user.

[0025] For a more complete understanding of the present disclosure, example embodiments, and advantages thereof, reference is now made to the following description taken in conjunction with the accompanying drawings, in which like reference numerals indicate like features and in which: [Brief description of the drawings]

[0026] [Figure 1] FIG. 1 is an illustration of an example embodiment of a system for managing user financial information. [Diagram 2] FIG. 2 is an illustration of an exemplary embodiment of a processor for managing user financial information. [Diagram 3] FIG. 2 is an illustration of an exemplary embodiment of a customer data processor. [Figure 4] FIG. 2 is an illustration of an exemplary embodiment of a financial product processor. [Diagram 5] FIG. 2 is an illustration of an example embodiment of a handpicked generator. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0027] For convenience, like reference numerals may be used to refer to like elements in the figures, but it will be understood that each of the various exemplary embodiments may be considered to be different variations.

[0028] Exemplary embodiments will now be described with reference to the accompanying drawings, which form a part of this disclosure and illustrate exemplary embodiments that may be practiced. As used in this disclosure and the appended claims, the phrases "embodiment", "exemplary embodiment", "exemplary embodiment" and "present embodiment" do not necessarily refer to a single embodiment, but may, and various exemplary embodiments may be readily combined and / or interchanged without departing from the scope or spirit of the exemplary embodiment. Furthermore, the terminology as used in this disclosure and the appended claims is intended only to describe exemplary embodiments and is not intended to be limiting. In this regard, as used in this disclosure and the appended claims, "in" can include "in" and "on", and "one" (or "n") and "the" (or "n") can include singular and plural references. Additionally, as used in this disclosure and the appended claims, the term "by" can mean "from," depending on the context. Additionally, as used in this disclosure and the appended claims, the term "if" can mean "when" or "upon," depending on the context. Additionally, as used in this disclosure and the appended claims, the term "and / or" can refer to and include any and all possible combinations of one or more of the associated listed items.

[0029] These exemplary embodiments generally relate to and / or include systems, subsystems, processors, devices, logic, methods, and processes for addressing conventional problems associated with providing recommendations to users regarding financial products and services (referred to herein as "financial products", "financial services", "products", or "services"), among other things, including those described above and in the present disclosure. As used in the present disclosure, when applicable, references to "users" refer to and may apply to and / or include one or more human users, one or more businesses, companies, corporations, departments, enterprises, and / or the like.

[0030] For example, these exemplary embodiments can be configured or are configured to search, identify, select, compile, generate, transform, process, evaluate, and / or receive user data in any other form. Such user data can include, but is not limited to, customer-reported data, financial institution information (such as information regarding the financial institutions used by each user), which financial institutions may be a user's primary financial institution (as further described in the present disclosure), social media information (such as which social media platforms / services are used by each user, the social media information of each user from such platforms), digital value capture information (such as information representing each user's preferences in participating in digital, virtual, etc. forms), product trends (such as the likely interests of each user in one or more financial products, services, groups, etc.), and personal preferences (such as financial goals and objectives, life stage, financial stage, financial preference information (such as level of effort, risk capacity, etc.), geographical information, etc.).

[0031] Exemplary embodiments are also configurable or configured to search, identify, select, compile, generate, convert, process, evaluate, and / or otherwise receive financial product data. Such financial product data may include, but is not limited to, product tenor (e.g., time to maturity for a fixed life financial product, investment framework (e.g., period of active trading for a unit investment trust, etc.), product risk rating (e.g., risk of product loss and risk based on product complexity), sophistication indicator (e.g., a six-point alphabetical scale of N, A, B, C, D, and E, where "N" indicates the product is simple and has no derivatives, "A" indicates the least complex, and "E" indicates the most complex product), financial objectives for the financial product, financial preference information (e.g., level of effort, risk capacity, etc.), and conviction rating (e.g., an assessment of the likely performance of the financial product relative to peers against the same asset class and / or benchmark over the next period (e.g., 18 months, 36 months, etc.)).

[0032] Exemplary embodiments are also configurable or configured to search, identify, select, compile, generate, convert, process, evaluate, and / or otherwise receive financial personality information, which may include, but is not limited to, savings personality (e.g., attitudes toward saving, types of motivators for saving, saving tendencies and behaviors, etc.), spending personality (e.g., attitudes toward spending, types of motivators for spending, spending tendencies and behaviors, etc.), investment personality (e.g., attitudes toward investing, types of motivators for investing, investing tendencies and behaviors, etc.), protection personality (e.g., attitudes toward protection (e.g., insurance), types of motivators for protection, protection tendencies and behaviors, etc.), debt personality (e.g., attitudes toward borrowing and repaying, types of motivators for borrowing and repaying, borrowing and repaying tendencies and behaviors, etc.).

[0033] Exemplary embodiments are also configurable or configured to search, identify, compile, generate, convert, process, evaluate, and / or otherwise select product recommendations for users. Such product recommendations for each user may be generated based on, among other things, the user's customer-declared data, information from the user's financial institution, the user's social media information, a determination of the user's primary financial institution (e.g., the user's primary financial institution score, as described further herein), a determination of the user's digital value capture (e.g., the user's digital value capture score, as described further herein), a determination of the user's product propensity (e.g., the user's product propensity score, as described further herein), the user's personal preferences, financial product information (e.g., real-time financial product data), and / or the user's financial personality.

[0034] Exemplary embodiments are also configurable or configured to search for, identify, compile, generate, convert, process, evaluate, and / or otherwise select a selection of financial products for the users. Such a selection for each user may be generated based on selecting one or more product recommendations (e.g., as generated by product recommendation processor 600) based on, among other things, a customer look-like assessment (e.g., as evaluated and selected by customer look-like processor 710), a customer propensity assessment (e.g., as evaluated and selected by customer propensity processor 720), and / or a product look-like assessment (e.g., as evaluated and selected by product look-like processor 730).

[0035] To perform the operations, functions, processes, and / or methods described above and in this disclosure, an exemplary embodiment comprises a system (e.g., system 100) for managing user financial information. System 100, when configured, may include one or more elements. For example, system 100 may include one or more users 10. System 100 may also include one or more databases / data storage / blockchains / etc. 20. System 100 may also include one or more information sources 30 (e.g., government agencies 30, pseudo-government agencies 30, governing bodies of industry / finance / regulation / etc. 30, financial institutions 30 and banks 30, digital banks 30, fintech organizations 30, cryptocurrency providers / exchanges / banks / etc. 30, insurance organizations 30, guarantee organizations 30, social media platforms 30, social media systems 30, social media networks 30, search engines 30, telecommunications organizations 30, transportation organizations 30, multimedia organizations 30, etc.). System 100 may also include one or more networks / internet / cloud computing / web / public cloud / private cloud / etc. 50. System 100 may also include one or more financial processors (e.g., financial processor 200, or also referred to herein as processor 200).

[0036] Exemplary embodiments are now described below with reference to the accompanying drawings, which form a part of this disclosure.

[0037] 1 illustrates an example embodiment of a system (eg, system 100) for managing user financial information.

[0038] 1 illustrates an exemplary embodiment of a system (e.g., system 100) for managing financial information for one or more users 10. System 100 is configurable or configured to search, identify, select, compile, generate, convert, process, evaluate, and / or otherwise receive user data for each user 10. Alternatively, or in addition, system 100 is configurable or configured to search, identify, select, compile, generate, convert, process, evaluate, and / or otherwise receive financial instrument data. Alternatively, or in addition, system 100 is configurable or configured to search, identify, select, compile, generate, convert, process, evaluate, and / or otherwise receive financial personality information for each user 10. Alternatively or in addition, system 100 can be configured or configured to search, identify, compile, generate, convert, process, evaluate, and / or otherwise select product recommendations (of financial instruments) for each user 10. Alternatively or in addition, system 100 can be configured or configured to search, identify, compile, generate, convert, process, evaluate, and / or otherwise select picks (of financial instruments) for each user 10.

[0039] An exemplary embodiment of system 100 is configurable or configured to perform these and other functions, operations, and / or processes, including those described in this disclosure, via one or more elements of system 100. For example, system 100 may include one or more users 10. System 100 may also include one or more databases 20, data storage systems 20, blockchains 20, other distributed ledger technologies (DLTs) 20, etc. The system 100 may also include one or more information sources 30 (e.g., information coming directly or indirectly from government agencies 30, pseudo-government agencies 30, industry / financial / regulatory / etc. governing bodies 30, financial institutions 30 and banks 30, digital banks 30, fintech organizations 30, cryptocurrency providers / exchanges / banks / etc. 30, insurance organizations 30, guarantee organizations 30, social media platforms 30, social media systems 30, social media networks 30, search engines 30, telecommunications organizations 30, transportation organizations 30, multimedia organizations 30, artificial intelligence (AI) systems 30, quantum computing systems 30, etc.). The system 100 may also include one or more networks 50, the Internet 50, cloud computing 50, the World Wide Web 50 (including Web 1.0, Web 2.0, Web 3.0, etc.), public clouds 50, private clouds 50, etc. The system 100 may also include one or more financial processors or processors (e.g., processor 200).

[0040] Exemplary embodiments of the system 100 will now be described with reference to the accompanying drawings, which form a part of this disclosure.

[0041] A financial processor (eg, processor 200).

[0042] 1 and 2, an exemplary embodiment of system 100 includes a financial processor (e.g., processor 200, also referred to herein as a "processor"). Each processor 200 is configurable or configured to perform various operations, functions, methods, and / or processes, including managing user financial information.

[0043] Each processor 200 may comprise one or more elements configurable or configured to perform various operations, functions, methods, and / or processes, including managing user financial information. For example, each processor 200 may include one or more primary interfaces (e.g., primary interface 210) configurable or configured to search for, identify, select, compile, generate, convert, process, evaluate, and / or otherwise receive information and / or requests (e.g., requests received from one or more users 10 for product recommendations, requests for handpicking, etc.) from one or more users 10, one or more databases 20, one or more information sources 30, one or more networks 50, and / or one or more other systems 100 and / or processors 200. Such information is then provided to one or more other elements of the system 100 including, but not limited to, the user data processor 300, the financial product processor 400, the financial personality processor 500, the product recommendation processor 600, the handpicking processor 700, and / or the output interface 220.

[0044] Each processor 200 may also include one or more user data processors (e.g., user data processor 300). User data processor 300 is configurable or configured to process user data received from main interface 210. Such user data may include, but is not limited to, customer declaration data, financial institution information, which financial institution may be the primary financial institution of user 10, social media information of user 10, digital value capture information of user 10, product trends of user 10, and personal preferences of user 10. Such user data is then provided to one or more other elements of system 100, including, but not limited to, product recommendation processor 600, curation processor 700, and / or output interface 220.

[0045] Each processor 200 may also include one or more financial instrument processors (e.g., financial instrument processor 400). Financial instrument processor 400 is configurable or configured to process financial instrument data received from main interface 210. Such financial instrument data may include, but is not limited to, product tenor information, product risk assessment information, advanced product index information, financial goal information for the financial instrument, information regarding financial preference information, and confidence rating information. Such financial instrument data is then provided to one or more other elements of system 100, including, but not limited to, product recommendation processor 600, handpicking processor 700, and / or output interface 220.

[0046] Each processor 200 may also include one or more financial personality processors (e.g., financial personality processor 500). Financial personality processor 500 is configurable or configured to process financial personality data received from main interface 210. Such financial personality data may include, but is not limited to, savings personality information of user 10, spending personality information of user 10, investment personality information of user 10, protection personality information of user 10, and / or debt personality information of user 10. Such financial personality data is then provided to one or more other elements of system 100, including, but not limited to, product recommendation processor 600, selection processor 700, and / or output interface 220.

[0047] Each processor 200 may also include one or more product recommendation processors (e.g., product recommendation processor 600). Product recommendation processor 600 is configurable or configured to process information received from main interface 210, user data processor 300, financial product processor 400, financial personality processor 500, and / or handpicking processor 700. Once received, product recommendation processor 600 is configurable or configured to generate one or more product recommendations for one or more users 10. Such product recommendations are then provided to one or more other elements of system 100, including, but not limited to, handpicking processor 700, and / or output interface 220.

[0048] Each processor 200 may also include one or more handpicking processors (e.g., handpicking processor 700). Handpicking processor 700 is configurable or configured to process information received from main interface 210, user data processor 300, financial product processor 400, financial personality processor 500, and / or product recommendation processor 600. Once received, handpicking processor 700 is configurable or configured to generate one or more handpicks (of financial products) for one or more users 10. Such handpicks are then provided to one or more other elements of system 100, including, but not limited to, product recommendation processor 700, and / or output interface 220.

[0049] Although the figure may illustrate the processor 200 as having one main interface 210, one user data processor 300, one financial instrument processor 400, one financial personality processor 500, one product recommendation processor 600, one handpicked processor 700, and one output interface 220, it should be understood that the processor 200 may include more than one or less main interfaces 210, more than one user data processor 300, more than one or less financial instrument processor 400, more than one or less financial personality processor 500, more than one or less product recommendation processor 600, more than one or less handpicked processor 700, and / or more than one output interface 220 without departing from the teachings of the present disclosure. For example, processor 200 may include one or more primary interfaces 210 that are configurable or configured to perform some, most, and / or all of the functions of primary interface 210, user data processor 300, financial instrument processor 400, financial personality processor 500, product recommendation processor 600, handpicking processor 700, and / or output interface 220. As another example, processor 200 may include one or more user data processors 300 that are configurable or configured to perform some, most, and / or all of the functions of primary interface 210, user data processor 300, financial instrument processor 400, financial personality processor 500, product recommendation processor 600, handpicking processor 700, and output interface 220. As another example, the processor 200 may include one or more financial instrument processors 400 that are configurable or configured to perform some, most, and / or all of the functions of the main interface 210, the user data processor 300, the financial instrument processor 400, the financial personality processor 500, the product recommendation processor 600, the handpicking processor 700, and the output interface 220.As another example, processor 200 may include one or more financial personality processors 500 that are configurable or configured to perform some, most, and / or all of the functions of primary interface 210, user data processor 300, financial instrument processor 400, financial personality processor 500, product recommendation processor 600, handpicking processor 700, and output interface 220. As another example, processor 200 may include one or more product recommendation processors 600 that are configurable or configured to perform some, most, and / or all of the functions of primary interface 210, user data processor 300, financial instrument processor 400, financial personality processor 500, product recommendation processor 600, handpicking processor 700, and output interface 220. As yet another example, processor 200 may include one or more handpicking processors 700 that are configurable or configured to perform some, most, and / or all of the functions of primary interface 210, user data processor 300, financial instrument processor 400, financial personality processor 500, product recommendation processor 600, handpicking processor 700, and output interface 220. As another example, processor 200 may include one or more output interfaces 220 that are configurable or configured to perform some, most, and / or all of the functions of primary interface 210, user data processor 300, financial instrument processor 400, financial personality processor 500, product recommendation processor 600, handpicking processor 700, and output interface 220. Each of the elements of the processor 200 is configurable or may be configured to connect to, communicate with, and / or receive communications (including requests) from one or more users 10, one or more computing devices 10, one or more databases 20, one or more information sources 30, one or more networks 50, and / or one or more other systems 100 and / or the processor 200.

[0050] As used in this disclosure, where applicable, references to the “system”, “processor”, system 100 (and / or one of its elements), financial processor 200 (and / or one of its elements), processor 200 (and / or one of its elements), main interface 210 (and / or one of its elements), user data processor 300 (and / or one of its elements), financial product processor 400 (and / or one of its elements), financial personality processor 500 (and / or one of its elements), product recommendation processor 600 (and / or one of its elements), handpicking processor 700 (and / or one of its elements), and output interface 220 (and / or one of its elements) may also refer to, apply to, and / or include one or more computing devices, processors, servers, systems, cloud-based computing, virtual machines, AI machines, or the like, and / or the functionality of one or more processors, computing devices, servers, systems, cloud-based computing, virtual machines, AI machines, or the like. The “system”, “processor”, system 100 (and / or one of its elements), financial processor 200 (and / or one of its elements), processor 200 (and / or one of its elements), main interface 210 (and / or one of its elements), user data processor 300 (and / or one of its elements), financial product processor 400 (and / or one of its elements), financial personality processor 500 (and / or one of its elements), product recommendation processor 600 (and / or one of its elements), handpicking processor 700 (and / or one of its elements), and output interface 220 (and / or one of its elements) may be any processor, server, system, device, computing device, controller, microprocessor, microcontroller, microchip, semiconductor device, or the like that is configurable or configured to perform the operations, steps, methods, processes, and / or the like described in this disclosure.Alternatively or in addition, the "system", "processor", system 100 (and / or one of its elements), financial processor 200 (and / or one of its elements), processor 200 (and / or one of its elements), main interface 210 (and / or one of its elements), user data processor 300 (and / or one of its elements), financial product processor 400 (and / or one of its elements), financial personality processor 500 (and / or one of its elements), product recommendation processor 600 (and / or one of its elements), handpicking processor 700 (and / or one of its elements), and output interface 220 (and / or one of its elements) may include and / or be part of a virtual machine, processor, computer, node, instance, host, or machine, including those in a networked computing environment. Additionally, the terms "data" and "information" are used interchangeably in this disclosure and may refer to data and / or information without departing from the teachings of this disclosure.

[0051] As used in this disclosure, a communication channel 50, network 50, cloud 50, or the like may be or include a collection of devices and / or virtual machines connected by a communication channel that facilitates communication between the devices and allows the devices to share resources. Such resources may encompass any type of resource for running an instance, including hardware (such as servers, clients, mainframe computers, networks, network storage, data sources, memory, central processing unit time, scientific instruments, and other computing devices), as well as software, software licenses, available network services, and other non-hardware resources, or combinations thereof. A communication channel 50, network 50, cloud 50, or the like may include, but is not limited to, a computing grid system, a peer-to-peer system, a mesh system, a distributed computing environment, a cloud computing environment, a telephony system, a voice over IP (VoIP) system, an audio communication channel, an audio broadcast channel, a text-based communication channel, a video communication channel, and the like. Such a communication channel 50, network 50, cloud 50, or the like may include hardware and software infrastructure configured to form a virtual organization made up of multiple resources that may be in geographically distributed locations. A communication channel 50, network 50, cloud 50, or the like may refer to a communication medium between processes on the same device. Also, as referred to herein, a network element, node, or server is a device that is deployed to execute a program that operates as a socket listener and may include a software instance.

[0052] In this disclosure, it should be understood that one or more elements, operations, and / or aspects of the exemplary embodiments may be included and / or implemented, in part or in whole, alone and / or in conjunction with other elements, using, for example, networking technologies, cloud computing, distributed ledger technology (DLT) (e.g., blockchain), artificial intelligence (AI), machine learning, deep learning, etc. Furthermore, while the exemplary embodiments described in this disclosure may be directed to managing user financial information, it should be understood in this disclosure that the exemplary embodiments may also be directed to managing user non-financial information without departing from the teachings of the present disclosure.

[0053] These and other elements of processor 200 will now be further described with reference to the accompanying figures.

[0054] A primary interface (eg, primary interface 210).

[0055] 2, the processor 200 includes and / or communicates with one or more primary interfaces (e.g., primary interface 210). Each primary interface is configurable or configured to retrieve, identify, select, compile, generate, convert, process, evaluate, and / or otherwise receive information and / or requests (e.g., requests received from one or more users 10 for product recommendations, requests for picks, etc.) from one or more elements of the system 100, including one or more users 10, one or more databases 20, one or more information sources 30, one or more networks 50, and / or one or more other systems 100 and / or the processor 200.

[0056] Information received by primary interface 210 may include, but is not limited to, user data. User data may include customer declared data, financial institution information (e.g., information regarding financial institutions used by each user 10, etc.), which financial institutions may be the primary financial institutions of users 10 (as described further in this disclosure), social media information (e.g., which social media platforms / services are used by each user 10, each user's 10's social media information from such platforms, etc.), digital value capture information (e.g., information representative of each user's 10 preferences in engaging digitally, virtually, etc.), product trends (e.g., likely interests for each user 10 in one or more financial products, services, groups, etc.), and personal preferences (e.g., financial goals and objectives, life stage, financial stage, financial preference information, geographic information, etc.). The user-declared data may include data surrendered by the user 10, such as demographic details (e.g., gender, date of birth, marital status, residence, occupation, employer, school, etc.), social information (e.g., identities of family and friends, social media account information, etc.), banking and financial information (e.g., financial account information, preferred currency, and banking relationships, relationships with financial institutions, financial instruments held, etc.). The user-declared data may also include the user's 10 explicit consent for the system 100 and / or processor 200 to access and / or track data obtainable from the user-declared data, such as account credits and debits and / or the like.

[0057] The information received by the primary interface 210 may also include, but is not limited to, financial product data. The financial product data may include product tenor (e.g., time to maturity for a fixed life financial product, investment framework (e.g., period of active trading for a unit investment trust, etc.), product risk rating (e.g., risk of product loss and risk based on the complexity of the product), sophistication product indicator (e.g., a six-point alphabetical scale of N, A, B, C, D, and E, where "N" indicates the product is simple and has no derivatives, "A" indicates the least complex, and "E" indicates the most complex product), financial objectives of the financial product, financial preference information (e.g., level of effort, risk capacity, etc.), and conviction rating (e.g., an assessment of the likely performance of the financial product relative to peers against the same asset class and / or benchmark over the next period (e.g., 18 months, 36 months, etc.)).

[0058] Information received by the primary interface 210 may also include, but is not limited to, financial personality data. Financial personality data may include savings personality (e.g., attitudes toward saving, types of motivators for saving, saving tendencies and behaviors, etc.), spending personality (e.g., attitudes toward spending, types of motivators for spending, spending tendencies and behaviors, etc.), investment personality (e.g., attitudes toward investing, types of motivators for investing, investment tendencies and behaviors, etc.), protection personality (e.g., attitudes toward protection (e.g., insurance), types of motivators for protection, protection tendencies and behaviors, etc.), debt personality (e.g., attitudes toward borrowing and repaying, types of motivators for borrowing and repaying, borrowing and repaying tendencies and behaviors, etc.).

[0059] Information received by the main interface 210, including that described above and in this disclosure, is then provided to one or more other elements of the system 100, including, but not limited to, the user data processor 300, the financial product processor 400, the financial personality processor 500, the product recommendation processor 600, the handpicking processor 700, and / or the output interface 220.

[0060] A user data processor (eg, user data processor 300).

[0061] 2 and 3, processor 200 includes and / or communicates with one or more user data processors (e.g., user data processor 300). User data processor 300 is configurable or configured to retrieve, identify, select, compile, generate, convert, process, evaluate, and / or otherwise receive user data received from main interface 210. In an exemplary embodiment, user data processor 300 may also retrieve, identify, select, compile, generate, convert, process, evaluate, estimate, and / or otherwise receive user 10's life stage, financial stage, location information, lifestyle, assets, balance sheet, and / or the like from the received user data. In another exemplary embodiment, the user data processor 300 may also search, identify, select, compile, generate, convert, process, evaluate, estimate, and / or otherwise receive information such as frequently visited locations locally and abroad (e.g., outside the user's country of registration / home), financial transactions, behavioral patterns, online activities, mobile activities, areas of interest (e.g., weddings, real estate, social media usage), etc. The user data processor 300 may also search, identify, select, compile, generate, convert, process, evaluate, and / or otherwise receive information such as transaction data (e.g., from debit card, credit card, and / or GIRO transactions, bank transfers, and / or financial account deposits and withdrawals, payment instructions, liquidity, etc.) as well as broader data (e.g., SGFinDex data, which includes, among other information, information regarding assets and loan balances with different financial institutions or government bonds, etc.). In one exemplary embodiment, the user data processor 300 may search for, identify, select, compile, generate, convert, process, evaluate, and / or otherwise receive information related to the digital or online activities of user 10 (e.g., online browsing preferences, digital campaign responses, online financial activities, etc.).

[0062] The user data processor 300 may also search, identify, select, compile, generate, convert, process, evaluate, and / or otherwise receive information such as which financial institutions may be the user's 10's primary financial institutions, the user's 10's social media information, the user's 10's digital value capture information, the user's 10's product trends, and the user's 10's personal preferences. Such user data is then provided to one or more other elements of the system 100, including, but not limited to, the product recommendation processor 600, the selection processor 700, and / or the output interface 220.

[0063] To perform the operations, functions, processes, and / or methods described above and in this disclosure, an exemplary embodiment of the user data processor 300 comprises a user data selection processor (e.g., user data selection processor 301). The user data processor 300 also includes a primary financial institution score generator (e.g., primary financial institution score generator 310). The user data processor 300 also includes a digital value capture score generator (e.g., digital value capture score generator 320). The user data processor 300 also includes a product propensity score generator (e.g., product propensity score generator 330).

[0064] These and other elements of the user data processor 300 will now be further described with reference to the accompanying figures.

[0065] A user data selection processor (eg, user data selection processor 301).

[0066] 2 and 3, the processor 200 includes and / or communicates with one or more user data selection processors (e.g., user data selection processor 301). The user data selection processor 301 is configurable or configured to receive user relationship data from the main interface 210. Once received, the user data selection processor 301 may be configurable or configured to select, identify, generate, derive, conclude, estimate, and / or otherwise provide information for further processing by the primary financial institution score generator 310, the digital value capture score generator 320, and / or the product propensity score generator 330.

[0067] In one exemplary embodiment, the user data selection processor 301 is configurable or configured to search, identify, select, compile, derive, generate, transform, process, evaluate, estimate, conclude, and / or otherwise receive user data.

[0068] Such user data may include user (or customer) declared data and / or information that is generable, derivable, inferable, inferable, and / or otherwise identifiable or obtainable from the user declared data. Examples of such information may include information related to date of birth (DOB), other relevant dates of the user 10, gender, marital status, residence, location-based information, occupation, employment information, education information, frequently visited places (domestic), frequently visited places (international), lifestyle, preferred currency, family, friends, inferred assets, preferred retailers, demographics, emotions, preferences, behavioral patterns, etc. User declared data may also include explicit or explicit consent of the user 10 to the system 100 and / or processor 200 to access and / or track data obtainable from the user declared data, such as account deposits and withdrawals and / or the like.

[0069] The user data that the user data selection processor 301 searches for, identifies, selects, compiles, derives, generates, converts, processes, evaluates, estimates, concludes, and / or otherwise receives may also include financial institution information and / or information generateable, derivable, concludeable, inferable, and / or otherwise identifiable or obtainable from financial institution information. Examples of such information may include information related to which financial institutions the user 10 uses, financial account information at one or more financial institutions used by the user 10 (e.g., personal balance sheets, account types, bank account transactions, bank account balances, credit card transactions, credit card balances, credit records, liquidity, retirement planning, which financial institutions may be the primary financial institutions of the user 10 (as described further in this disclosure), relationships with other financial institutions, corporate financial relationships, etc.), preferred currencies, financial instruments held by the user 10.

[0070] The user data that the user data selection processor 301 searches for, identifies, selects, compiles, derives, generates, converts, processes, evaluates, estimates, concludes, and / or otherwise receives may also include social media information and / or information that is generateable, derivable, concludeable, inferable, and / or otherwise identifiable or obtainable from social media information. Examples of such information may include information related to which social media platforms, networks, and / or services are used by user 10, social media information for user 10 from such platforms, networks, and / or services (e.g., user 10's areas of interest, level of usage, and / or activity on the social media platforms, networks, and / or services), friends, relationships, and / or connections with others on the social media platforms, networks, and / or services, social media information for friends, relationships, and / or connections on the social media platforms, networks, and / or services (e.g., level of relationship with friends, relationships, and / or connections on the social media platforms, networks, and / or services, geographic location of friends, relationships, and / or connections on the social media platforms, networks, and / or services, areas of interest of friends, relationships, and / or connections on the social media platforms, networks, and / or services).

[0071] The user data that the user data selection processor 301 searches for, identifies, selects, compiles, derives, generates, converts, processes, evaluates, estimates, concludes, and / or otherwise receives may also include digital value capture information, information generateable, derivable, concludeable, inferable, and / or otherwise identifiable or obtainable from the digital value capture information, and / or information representing each user 10's preferences in engaging digitally, virtually, etc. Examples of such information may include information related to transactions conducted by user 10, digital channels used by user 10, non-digital channels used by user 10, investment products purchased by user 10, frequency of using digital channels (e.g., online banking, etc.), frequency of using non-digital channels (e.g., physical visits to a bank branch, etc.), comparison of digital channel usage to non-digital channel usage, interactions and / or responses to digital communications (e.g., email, online advertisements, SMS, or text messages, etc.), interactions and / or responses to non-digital communications (e.g., mail, flyers, product brochures, etc.), comparison of interactive interactions with digital communications to interactions with non-digital communications, etc.

[0072] The user data that the user data selection processor 301 retrieves, identifies, selects, compiles, derives, generates, converts, processes, evaluates, estimates, concludes, and / or otherwise receives may also include product trend information, information generateable, derivable, concludeable, inferable, and / or otherwise identifiable or obtainable from product trend information, and / or information related to the user's 10 likely interests in one or more financial products, services, groups, etc. Examples of such information may include information related to CASA balances, the user's 10 financial institution information, balances, income, payroll credit amounts, etc.

[0073] The user data that the user data selection processor 301 retrieves, identifies, selects, compiles, derives, generates, converts, processes, evaluates, estimates, concludes, and / or otherwise receives may also include personal preferences of the user 10 and / or information that is generateable, derivable, concludeable, inferable, and / or otherwise identifiable or obtainable from the personal preferences of the user 10. Examples of such information may include information regarding financial goals, financial objectives, life stage, financial stage, risk capacity assessment, level of effort for financial products, geographic information, etc.

[0074] Information related to financial goals and objectives may include and / or be determined based on, among other things, capital preservation, asset accumulation, income distribution, retirement, asset disposition, education, and / or the like. Financial objectives may be based on product-specific assessments. For example, when the financial product is a unit investment trust, the financial product (held by user 10) may be classified into one of various share classes including an "accumulation" class, a "unit" class, a "cash" class, and / or a "disposition" class. This classification determines the financial objectives for which the particular product is classified (for example, a unit investment trust may be classified as a "cash" class if the unit investment trust distributes dividends in cash, which may be useful to a user 10 interested in building a recurring income flow, among other things).

[0075] Information related to the life stage of user 10 may include and / or be determined based on, among other things, the age and / or network of user 10 (e.g., family, marital status, presence and / or age of children, etc.). The life stage of user 10 may be generated as part of a set of personal preferences of user 10 since financial activities and / or habits (e.g., spending, investments, protection plans, etc.) are highly correlated with important life events, particularly family changes (e.g., getting married, having (more) children).

[0076] Table 1 below illustrates one example of a life stage that may be generated for a user 10 by the user data selection processor 301.

[0077] [Table 1]

[0078] The information related to the financial stage of the user 10 may be information indicative of the financial maturity of the user 10. The financial stage information of the user 10 may include and / or be determined based on key individual financial metrics including cash flow, savings amount, investment holdings, past investment activity, and / or the like. These may be exemplified in the form of financial wellness (e.g., cash flow, amount of electronic funds available), age, investment or growth insurance products held, and / or whether there is a gap in protection. Investment or growth insurance products may include unit trusts, savings plans with built-in investment options, robo or partial robo investments, growth insurance plans, and / or the like. The protection gap may be defined as an indication of how much more protection may be needed for the user, and is calculated by aggregating the user's information (e.g., life stage, expenditures and / or needs, years of support needed, financial obligations such as loans) against the user's existing (i.e., purchased) protection coverage, thereby determining the coverage gap. The financial stage information of the user 10 may be classified by the user data selection processor 301 into one or more categories.

[0079] Table 2 below illustrates an example of financial stage information generated by the user data selection processor 301 and illustrates an exemplary profile of a user 10 in such a financial stage.

[0080] [Table 2A] [Table 2B]

[0081] Information related to the risk capacity of the user 10 may include and / or be determined based on, among other things, information obtained through a questionnaire answered by the user 10, or the like. The answer to each question may comprise a score, a weighted score, or the like, and after the questionnaire is completed, the aggregated scores may be tabulated by the user data selection processor 301. Based on the aggregated scores, the user 10 may be classified into one of a number of risk profiles. For example, the user 10 may be classified into one of six risk profiles, ranging from C0 to C5. Each answer to each question may be accompanied by a risk rating indicating the maximum risk appetite of the user 10.

[0082] Table 3 below illustrates one example of how the user data selection processor 301 may assign a risk rating to answers to a question relating to the average level of potential investment loss that the user 10 can tolerate.

[0083] [Table 3]

[0084] Information related to user 10's level of effort may include and / or be determined based on, among other things, information related to and / or indicative of the level of effort user 10 is willing to expend in managing their investments, including monitoring, reviewing, researching, interacting, etc. Information related to the level of effort may be determined through questionnaires answered by user 10, or the like.

[0085] After the user data selection processor 301 retrieves, identifies, selects, compiles, derives, generates, transforms, processes, evaluates, estimates, concludes, and / or otherwise receives user data, including as described above and in this disclosure, the user data selection processor 301 provides such user data to the primary financial institution score generator 310, the digital value capture score generator 320, the product propensity score generator 330, and / or one or more other elements of the system 100 or processor 200 for further processing. For example, as described in this disclosure, the user data selection processor 301 provides the user data to the primary financial institution score generator 310 to generate a primary financial institution score for the user 10. The user data selection processor 301 also provides the user data to the digital value capture score generator 320 to generate a digital value capture score for the user 10. The user data selection processor 301 also provides the user data to the product propensity score generator 320 to generate a product propensity score for the user 10.

[0086] A major financial institution score generator (eg, major financial institution score generator 310).

[0087] 2, the processor 200 includes and / or communicates with a primary financial institution generator (e.g., primary financial institution generator 310), which can be configured or configured to communicate with the user data selection processor 301 and / or one or more other elements of the system 100.

[0088] In one exemplary embodiment, the primary financial institution generator 310 is configurable or configured to generate a financial institution score for each financial institution used by the user 10 (as identified by the user data selection processor 301 and / or one or more other elements of the system 100 and / or processor 200). The primary financial institution generator 310 is also configurable or configured to generate a financial institution ranking, or the like, by comparing the financial institution scores generated for each financial institution and ranking the financial institutions based on a comparison of the financial institutions' financial institution scores. In one exemplary embodiment, the primary financial institution generator 310 identifies, sets, designates, and / or otherwise designates the financial institution having the highest or top rank as the primary (or primary) financial institution of the user 10.

[0089] In one exemplary embodiment, the primary financial institution generator 310 does the above based on user data and / or information received from the user data selection processor 301 and / or one or more other elements of the system 100, including financial information, financial institution information, and / or information that is generable, derivable, concludeable, inferable, and / or otherwise identifiable or obtainable from the financial information, financial institution information, and / or any other information received from the user data selection processor 301. For example, the received information may include financial interaction information, including, but not limited to, deposits, investments, channels used, liabilities (e.g., loans), assets, products, balances in one or more financial accounts held by user 10, usage of each financial institution (e.g., usage of financial institution products (e.g., credit cards, unsecured loans, mortgages), investments through each financial institution, purchasing insurance through each financial institution, usage of each financial institution's payment products, usage of each financial institution's services (e.g., participation in a particular cash back program, holding a financial account with each financial institution), personal balance sheets, account types, bank account transactions, credit card transactions, credit card balances, credit record, liquidity, retirement planning, which financial institutions may be user 10's primary financial institution (as described further in this disclosure), relationships with other financial institutions, corporate financial relationships, etc.), preferred currency, financial instruments held by user 10, and / or the like. The received user data may also include non-financial interaction information including, but not limited to, usage of various types of services (e.g., fund transfers, payments, withdrawals, logins, inquiries, updates of certain details, etc.) offered through various financial institution touchpoints (e.g., automated teller machines (ATMs), self-service financial service machines, brick-and-mortar branches, cash points, internet banking portals, mobile banking portals, digital wallets, telephone banking).

[0090] The primary financial institution generator 310 is then configurable or configured to perform a search of all financial institutions where the user 10 has at least one financial account (or obtains such information from the user data selection processor 301) (e.g., where such information is not provided by the user 10 and / or identified by the user data selection processor 301). For each financial institution of the user 10 where the user has at least one financial account (e.g., savings account, checking account, etc.), the primary financial institution generator 310 is configurable or configured to generate an institution score for the financial institution.

[0091] In generating a financial institution score for each financial institution of user 10, primary financial institution generator 310 can be configured or configured to generate a total inflow of value into each of the financial accounts held by user 10 at each financial institution over a period of time. Primary financial institution generator 310 can also be configured or configured to generate a total outflow of value from each of the financial accounts held by user 10 at each financial institution over a period of time. Primary financial institution generator 310 can then be configured or configured to generate an inflow / outflow ratio for each of the financial accounts held by user 10 over a period of time based on the generated total inflows and outflows of value.

[0092] Alternatively, or in addition, the primary financial institution generator 310 can be configured or arranged to generate a total inflow of value into each financial institution of the user 10 over a period of time. The primary financial institution generator 310 can also be configured or arranged to generate a total outflow of value from each financial institution of the user 10 over a period of time. The primary financial institution generator 310 can then be configured or arranged to generate an inflow / outflow ratio for each financial period of the user 10 over a period of time based on the total inflows and outflows of value generated.

[0093] In generating a financial institution score for each financial institution of user 10, primary financial institution generator 310 can be configured or is configured to identify all periodic payments made by each financial account held by user 10 at each financial institution over the time period. Primary financial institution processor 310 can also be configured or is configured to identify all non-periodic payments made by each financial account held by user 10 at each financial institution over the time period.

[0094] Alternatively, or in addition, the primary financial institution generator 310 can be configured or arranged to identify all periodic payments made by each financial institution of the user 10 over a period of time. The primary financial institution generator 310 can also be configured or arranged to identify all non-periodic payments made by each financial institution of the user 10 over that period of time.

[0095] The primary financial institution generator 310 generates a financial institution score for each financial institution of the user 10 based on at least one or more of the inflow / outflow ratio for each financial account held by the user 10, the inflow / outflow ratio for each financial institution of the user 10, the identified periodic payments for each financial account held by the user 10, the identified periodic payments for each financial institution of the user 10, the identified non-periodic payments for each financial account held by the user 10, and / or the identified non-periodic payments for each financial institution of the user 10. The primary financial institution generator 310 then determines a financial institution ranking for the time period by comparing the financial institution scores for each of the financial institutions and ranking the financial institutions based on the comparison. Through this ranking, the primary financial institutions of the user 10 for the time period are identified, established, named, and / or otherwise designated by the primary financial institution generator 310.

[0096] In an exemplary embodiment, the primary financial institution generator 310 is configurable or configured to determine the overall banking usage of the user 10 based on, among other things, the product holdings, balances, interactions, and payments of the user 10. For example, the primary financial institution generator 310 may search for, identify, select, compile, derive, generate, convert, process, evaluate, estimate, conclude, and / or otherwise receive information regarding, based on, and / or relating to the number of the user's 10 product holdings (e.g., products held, and other services and metrics), the amount of balances (e.g., assets, liabilities, CUL ratios, inflow / outflow ratios), the number of interactions (e.g., with the primary financial institution and other financial institutions), the frequency of payments (e.g., daily payments, periodic payments, one-time payments, inflows, etc.), the frequency of inflows (e.g., salary, bonuses, refunds, fees, interest received, etc.). The lead financial institution generator 310 may combine and / or include these pieces of information in generating a financial institution score, including performing an assessment of the importance (or relative importance) of each piece of information using, for example, logistic regression, to calculate an overall weighted score for the user's 10 overall banking experience.

[0097] In one embodiment, the primary financial institution generator 310 determines the banking needs of the user 10 based on the life stage and affordability of the user 10. The life stage may be determined based on the age and network (e.g., family) of the user 10, among other things. The affordability may be determined based on the reported income and assets of the user 10, and the projected income and assets of the user 10 (as provided by the user data selection processor 301), among other things. The banking needs of the user 10 are determined by mirroring with other users 10, including normalizing the data and removing outliers, for all relevant micro-segments (e.g., different life stage and affordability combinations).

[0098] Once the primary financial institution generator 310 has performed the above, including generating a financial institution score, financial institution ranking, or the like, for each financial institution used by user 10, and the primary (or first) financial institution for user 10, the primary financial institution generator 310 is configurable or configured to then provide such information to the financial personality processor 500, the product recommendation processor 600, and / or the handpicking processor 700. For example, information related to the primary financial institutions of user 10 may be provided to the product recommendation processor 600 for use in generating product recommendations for user 10. Information related to the primary financial institutions of user 10 may also be provided to the handpicking processor for use in generating handpicking for user 10.

[0099] A digital value capture score generator (eg, digital value capture score generator 320).

[0100] 2, the processor 200 includes and / or communicates with a digital value capture score generator (e.g., digital value capture score generator 320), which can be configured or is configured to communicate with the user data selection processor 301 and / or one or more other elements of the system 100.

[0101] In an exemplary embodiment, the digital value capture score generator 320 is configurable or configured to generate a digital value capture score based on user data and / or other information received from the user data selection processor 301 and / or one or more other elements of the system 100. The digital value capture score is a score representing the user's 10 preferences in digitally engaging. The digital value capture score generator 320 generates a digital value capture score based on transactions by the user 10, channels used by the user 10, investment products purchased by the user 10, frequency of use of digital channels (e.g., online banking) versus non-digital channels (e.g., physical visits to a bank branch), and / or digital communication (e.g., email) versus non-digital communication (e.g., mail) interactions. The digital value capture score may also be generated based on other information, including, but not limited to, social media information, digital value capture information, information regarding the personal preferences of the user 10, and / or information that is generateable, derivable, concludeable, inferable, and / or in any other way identifiable or obtainable from the social media information, digital value capture information, information regarding the personal preferences of the user 10, and / or any other information received from the user data selection processor 301.Examples of social media information used by the digital value capture score generator 320 may include information related to which social media platforms, networks, and / or services are used by the user 10, social media information for the user 10 from such platforms, networks, and / or services (e.g., areas of interest, the extent of the user 10's usage and / or activity on the social media platforms, networks, and / or services, etc.), friends, relationships, and / or connections with others on the social media platforms, networks, and / or services, social media information for the friends, relationships, and / or connections on the social media platforms, networks, and / or services (e.g., the degree of relationship with the friends, relationships, and / or connections on the social media platforms, networks, and / or services, the geographic location of the friends, relationships, and / or connections on the social media platforms, networks, and / or services, the areas of interest of the friends, relationships, and / or connections on the social media platforms, networks, and / or services, etc.). Examples of digital value capture information used by the digital value capture score generator 320 may include information related to transactions conducted by the user 10, digital channels used by the user 10, non-digital channels used by the user 10, investment products purchased by the user 10, frequency of using digital channels (e.g., online banking, etc.), frequency of using non-digital channels (e.g., physical visits to a bank branch, etc.), comparison of digital channel use to non-digital channel use, interactions and / or responses with digital communications (e.g., email, online advertisements, SMS or text messages, etc.), interactions and / or responses with non-digital communications (e.g., mail, flyers, product brochures, etc.), comparison of digital communication interactions to non-digital communication interactions, etc.Examples of user's 10 personal preference information used by the digital value capture score generator 320 may include information related to financial goals, financial objectives, life stage, financial stage, financial preference information, geographic information, and the like.

[0102] Once the digital value Capture score generator 320 has performed the above, including generating a digital value Capture score for the user 10, the digital value Capture score generator 320 can then be configured or arranged to provide such information to the financial personality processor 500, the product recommendation processor 600, and / or the handpicking processor 700. For example, information relating to the digital value Capture score of the user 10 may be provided to the financial personality processor 500 for use in generating a financial personality for the user 10. Information relating to the digital value Capture score of the user 10 may also be provided to the product recommendation processor 600 for use in generating product recommendations for the user 10. Information relating to the digital value Capture score of the user 10 may also be provided to the handpicking processor for use in generating a handpicking for the user 10.

[0103] A product propensity score generator (eg, product propensity score generator 330).

[0104] 2, the processor 200 includes and / or is in communication with a product propensity score generator (e.g., product propensity score generator 330), which can be configured or is configured to communicate with the user data selection processor 301 and / or one or more other elements of the system 100.

[0105] In an exemplary embodiment, the product propensity score generator 330 is configurable or configured to generate a product propensity score for one or more financial products for the user 10. The product propensity score is a score representative of the user 10's likely interest in one or more financial products. The product propensity score generator 330 generates the product propensity score based on user data and / or information received from the user data selection processor 301 and / or one or more other elements of the system 100, including product propensity information and / or information that is generable, derivable, concludeable, estimable, and / or otherwise identifiable or obtainable. Examples of such information include, but are not limited to, CASA (current account) balance, financial institution rankings and financial institution scores, balances of one or more financial accounts held by the user 10, the user's 10 income, the user's 10 salary, demographic information, past financial products held, past financial activities (e.g., spending, saving, etc.), taxation, and / or the like. The product propensity score may also be generated based on other information, including, but not limited to, financial information, financial institution information, and / or information that is generateable, derivable, concludeable, inferable, and / or otherwise identifiable or obtainable from the financial information, financial institution information, and / or any other information received from the user data selection processor 301.For example, the received information may include financial interaction information, including, but not limited to, information regarding deposits, investments, channels used, liabilities (e.g., loans), assets, products, balances in one or more financial accounts held by user 10, usage of each financial institution (e.g., usage of financial institution products (e.g., credit cards, unsecured loans, mortgages), investments through each financial institution, purchasing insurance through each financial institution, usage of each financial institution's payment products, usage of each financial institution's services (e.g., participation in a particular cash back program, holding a financial account with each financial institution), personal balance sheets, account types, bank account transactions, credit card transactions, credit card balances, credit records, liquidity, retirement planning, which financial institutions may be user 10's primary financial institution (as described further in this disclosure), relationships with other financial institutions, corporate financial relationships, etc.), preferred currencies, financial products held by user 10, and / or the like. The received user data may also include non-financial interaction information including, but not limited to, usage of various types of services (e.g., fund transfers, payments, withdrawals, logins, inquiries, updates of certain details, etc.) offered through various financial institution touchpoints (e.g., automated teller machines (ATMs), self-service financial service machines, brick-and-mortar branches, cash points, internet banking portals, mobile banking portals, digital wallets, telephone banking).

[0106] The product propensity score generator 330 can be configured or is configured to evaluate how each of the above information affects the interest of the user 10 in a particular financial product. For example, the age of the user 10 may have a negative effect on an interest in a dependant protection scheme but a positive effect on an interest in a pension.

[0107] Once the product propensity score generator 330 has performed the above, including generating product propensity scores for one or more financial products for the user 10, the product propensity score generator 330 can then be configured or configured to provide such information to the financial personality processor 500, the product recommendation processor 600, and / or the handpicking processor 700. For example, information related to the product propensity scores of the user 10 may be provided to the financial personality processor 500 for use in generating a financial personality for the user 10. Information related to the product propensity scores of the user 10 may also be provided to the product recommendation processor 600 for use in generating product recommendations for the user 10. Information related to the product propensity scores of the user 10 may also be provided to the handpicking processor for use in generating handpicking for the user 10.

[0108] A financial instrument processor (eg, financial instrument processor 400).

[0109] 2 and 4, processor 200 includes and / or is in communication with one or more financial instrument processors (e.g., financial instrument processor 400). Financial instrument processor 400 is configurable or configured to search for, identify, select, compile, generate, convert, process, evaluate, and / or otherwise receive financial instrument information and / or other information (including information received from primary interface 210 and / or one or more other elements of system 100). For example, the financial instrument processor 400 may also search for, identify, select, compile, generate, convert, process, evaluate, estimate, and / or otherwise receive information related to the product tenor (e.g., time to maturity for a fixed life financial instrument, investment framework (e.g., period of active trading for a unit investment trust), etc.), product risk rating (e.g., risk of product loss and risk based on the complexity of the instrument), sophistication product indicator (e.g., a six-point alphabetical scale of N, A, B, C, D, and E, where "N" indicates the instrument is simple and has no derivative instruments, "A" indicates the least complex, and "E" indicates the most complex instrument), financial objectives of the financial instrument, financial preference information (e.g., level of effort, risk capacity, etc.), and conviction rating (e.g., an assessment of the likely performance of the financial instrument relative to peers against the same asset class and / or benchmark over the next period (e.g., 18 months, 36 months, etc.)).

[0110] In one exemplary embodiment, the financial instrument processor 400 identifies, selects, compiles, generates, converts, processes, evaluates, and / or otherwise provides such financial instrument information to one or more other elements of the system 100, including, but not limited to, the financial personality processor 500, the product recommendation processor 600, the handpicking processor 700, and / or the output interface 220.

[0111] To perform the operations, functions, processes, and / or methods described above and in this disclosure, an exemplary embodiment of financial instrument processor 400 comprises a financial instrument selection processor (e.g., financial instrument selection processor 401). Financial instrument processor 400 also includes an instrument tenor assessor (e.g., instrument tenor assessor 410). Financial instrument processor 400 also includes a instrument risk rating assessor (e.g., instrument risk rating assessor 420). Financial instrument processor 400 also includes an advanced instrument index assessor (e.g., advanced instrument index assessor 430). Financial instrument processor 400 also includes a financial goal assessor (e.g., financial goal assessor 440). Financial instrument processor 400 also includes a financial preference assessor (e.g., financial preference assessor 450). Financial instrument processor 400 also includes a belief rating assessor (e.g., belief rating assessor 460).

[0112] These and other elements of the financial instrument processor 400 will now be further described with reference to the accompanying figures.

[0113] A financial instrument selection processor (eg, financial instrument selection processor 401).

[0114] 2 and 4, financial instrument processor 400 includes and / or communicates with one or more user financial instrument selection processors (e.g., user financial instrument selection processor 401). Financial instrument selection processor 401 is configurable or configured to retrieve, identify, select, compile, generate, convert, process, evaluate, and / or otherwise receive financial instrument data and / or other information from primary interface 210 and / or one or more other elements of system 100 for further processing by product tenor assessor 410, product risk rating assessor 420, advanced product index assessor 430, financial goal assessor 440, financial preference assessor 450, and / or confidence rating assessor 460.

[0115] Such financial instrument data may include, but is not limited to, information related to the product tenor, product risk rating, product sophistication level (or advanced product indicator), financial objectives, financial preferences, conviction rating, and / or information generateable, derivable, concludeable, inferable, and / or otherwise identifiable or obtainable from the product tenor, product risk rating, product sophistication level (or advanced product indicator), financial objectives, financial preferences, and / or conviction rating. In an exemplary embodiment, the pool of financial instruments for which real-time data is generated by the financial instrument selection processor 401 may be loaded onto the system 100 following a manual governance assessment.

[0116] After the financial instrument selection processor 401 searches for, identifies, selects, compiles, derives, generates, converts, processes, evaluates, estimates, concludes, and / or otherwise receives financial instrument data, including as described above and in this disclosure, the financial instrument selection processor 401 provides such financial instrument data to a product tenor assessor (e.g., product tenor assessor 410), a product risk rating assessor (e.g., product risk rating assessor 420), an advanced product indicator assessor (e.g., advanced product indicator assessor 430), a financial goal assessor (e.g., financial goal assessor 440), a financial preference assessor (e.g., financial preference assessor 450), and / or a confidence rating assessor (e.g., confidence rating assessor 460) for further processing.

[0117] A product tenor assessor (eg, product tenor assessor 410).

[0118] 2 and 4, the financial instrument processor 400 includes and / or is in communication with an instrument tenor assessor (e.g., instrument tenor assessor 410), which is configurable or configured to communicate with the financial instrument selection processor 401 and / or one or more other elements of the system 100.

[0119] In an exemplary embodiment, the product tenor assessor 410 is configurable or configured to perform a product tenor assessment for one or more financial products for the user 10. Product tenors may be assigned to financial products based on, among other things, the characteristics of each product, the time period until the financial product expires, etc. In an exemplary embodiment, financial products with fixed lives (e.g., endowment plans, bonds, structured notes, etc.) may be assigned tenors based on the number of years until maturity. Financial products without fixed lives (e.g., unit investment trusts) may be assigned based on an investment framework that is manually reviewed and updated in the financial product data processor 208 periodically (e.g., annually) and / or whenever there is a significant change.

[0120] Once the product tenor assessor 410 has performed a product tenor assessment for one or more financial products for the user 10, the product tenor assessor 410 provides such product tenor assessment information to the financial personality processor 500, the product recommendation processor 600, and / or the handpicking processor 700 for further processing.

[0121] A commodity risk assessor (eg, commodity risk assessor 420).

[0122] 2 and 4, the financial instrument processor 400 includes and / or communicates with a product risk assessment assessor (e.g., product risk assessment assessor 420), which can be configured or is configured to communicate with the financial instrument selection processor 401 and / or one or more other elements of the system 100.

[0123] In one embodiment, the commodity risk assessment assessor 420 is configurable or configured to perform a commodity risk assessment assessment for one or more financial instruments for the user 10. The commodity risk assessment assessment may include, but is not limited to, a two-dimensional framework in which a first dimension reflects the potential loss of the instrument and a second dimension reflects the complexity of the instrument (as reflected by the advanced commodity indicator). The commodity risk assessment assessor 420 is configurable or configured to consider both price risk and issuer risk based on peak pre-settlement credit exposure (PPCE) in calculating the potential loss and calibrating the methodology. The PPCE represents the maximum loss for a trade (e.g., loss in a near worst-case scenario) predicted at a pre-specified confidence level of 97.5%. To calibrate the PPCE for the commodity risk assessment, the PPCE is first calculated based on a pre-selected set of benchmark asset classes with long-term historical behavior to check that the financial instrument remains fairly stable over a market cycle.

[0124] In an exemplary embodiment, the PPCE has the formula

[0125]

number

[0126] where "r" is the risk-free rate, "σ" is the 260-day annualized volatility, "t" is the time horizon, and "z" is 1.96 (representing the 97.5% confidence level).

[0127] In an exemplary embodiment, the product risk assessment may be classified into one or more categories. For example, the product risk assessment may be classified into five categories (e.g., P1 to P5) based on the PPCE range.

[0128] Table 4 below illustrates, as an example, how product risk assessments map to PPCE scope.

[0129] [Table 4]

[0130] Once the product risk assessment assessor 420 performs a product risk assessment on one or more financial products for the user 10, the product risk assessment assessor 420 provides such product risk assessment information to the financial personality processor 500, the product recommendation processor 600, and / or the handpicking processor 700 for further processing.

[0131] An advanced product index assessor (eg, advanced product index assessor 430).

[0132] 2 and 4, financial instrument processor 400 includes and / or communicates with an advanced instrument index assessor (e.g., advanced instrument index assessor 430). Advanced instrument index assessor 430 can be configured or is configured to communicate with financial instrument selection processor 401 and / or one or more other elements of system 100.

[0133] In one embodiment, the advanced product index assessor 430 is configurable or configured to perform an assessment of the level of sophistication for one or more financial instruments to allow the user 10 to arrive at an advanced product index. The advanced product index is indicative of the complexity or sophistication of the financial instrument. In an exemplary embodiment, the complexity or sophistication of the financial instrument may be rated on a point scale. For example, the complexity or sophistication of the financial instrument may be rated on a six-point scale of N, A, B, C, D, E, with an "N" rating indicating that the instrument is simple and has no derivatives, and instruments that include derivatives and / or complex product features may fall between "A" and "E," where an "A" rating indicates the least complex and an "E" rating indicates the most complex. The advanced product index may be used to identify investment instruments that are generally non-traditional and highly complex.

[0134] After the advanced product index assessor 430 performs an assessment of the level of sophistication of the financial products, including arriving at an advanced product index for one or more financial products for the user 10, the advanced product index assessor 430 provides such advanced product index information to the financial personality processor 500, the product recommendation processor 600, and / or the selection processor 700 for further processing.

[0135] A financial goal assessor (eg, financial goal assessor 440).

[0136] 2 and 4, financial instrument processor 400 includes and / or communicates with a financial goal assessor (e.g., financial goal assessor 440). Financial goal assessor 440 can be configured or is configured to communicate with financial instrument selection processor 401 and / or one or more other elements of system 100.

[0137] In one embodiment, financial goal assessor 440 is configurable or configured to perform an assessment of financial goals for one or more financial instruments for user 10. Each financial instrument is assigned a primary financial goal from a selection of financial goals that the financial instrument can best achieve, depending on the features and functionality of the financial instrument.

[0138] After the financial goal assessor 440 performs an assessment of the financial goals of a financial product, the financial goal assessor 440 provides such financial goal information to the financial personality processor 500, the product recommendation processor 600, and / or the handpicking processor 700 for further processing.

[0139] A financial preference assessor (e.g., financial preference assessor 450).

[0140] 2 and 4, financial instrument processor 400 includes and / or communicates with a financial preference assessor (e.g., financial preference assessor 450). Financial preference assessor 450 can be configured or is configured to communicate with financial instrument selection processor 401 and / or one or more other elements of system 100.

[0141] In one embodiment, financial preference assessor 450 is configurable or configured to retrieve, identify, select, compile, generate, convert, process, evaluate, and / or otherwise receive the level of effort actively expended by user 10 to determine an appropriate financial product. For example, the level of effort actively expended by user 10 may be determined based on how often user 10 reviews or monitors their investment holdings and / or based on user 10's responses to a survey asking questions to that effect.

[0142] In another embodiment, financial preferences assessor 450 is configurable or configured to search, identify, select, compile, generate, convert, process, evaluate, and / or otherwise receive the risk capacity of user 10. Risk capacity may be determined based on user information (e.g., demographics), behavior (e.g., purchased items), financial wellness (e.g., account balances), and / or the like, or based on user 10's responses to questionnaires measuring risk tolerance, user 10's knowledge and beliefs regarding the user's future financial situation, etc. For example, a user 10 with a high risk tolerance and sound financial wellness, among other things, may be assessed as having a high risk capacity.

[0143] In another embodiment, the financial preference assessor 450 is configurable or configured to search, identify, select, compile, generate, convert, process, evaluate, and / or otherwise receive preferences of the user 10 with respect to investments, insurance, savings, and other aspects of financial planning. Preferences with respect to investments, insurance, savings, and other aspects of financial planning may be determined based on customer-reported data, behavior (e.g., products purchased), financial wellness (e.g., account balances), risk capacity, and / or the like. For example, a user 10 with a lower risk capacity and lower spending behavior, among other things, may be assessed as preferring more savings-related products.

[0144] In another embodiment, financial preference assessor 450 can be configured or configured to search, identify, select, compile, generate, convert, process, evaluate, and / or otherwise receive financial product suitability and / or preference determinations based on a profile (e.g., user information, financial wellness, investment preferences, etc.) of user 10. For example, a financial product with high risk and high reward may be assessed as suitable for a user 10 who has sound financial wellness and prefers volatile investments.

[0145] After the financial preference assessor 450 performs the assessment and obtains financial preference information for financial products, the preference assessor 450 provides such financial preference information to the financial personality processor 500, the product recommendation processor 600, and / or the handpicking processor 700 for further processing.

[0146] A confidence rating assessor (eg, confidence rating assessor 460).

[0147] 2 and 4, the financial instrument processor 400 includes and / or is in communication with a confidence rating assessor (e.g., confidence rating assessor 460), which can be configured or is configured to communicate with the financial instrument selection processor 401 and / or one or more other elements of the system 100.

[0148] In an exemplary embodiment, the confidence rating assessor 460 is configurable or configured to search, identify, select, compile, generate, convert, process, evaluate, and / or otherwise receive confidence ratings for financial instruments. The confidence ratings represent a determination of the likelihood of the performance of the financial instrument relative to its peers, against asset classes and / or benchmarks, etc., over the next 18 to 36 months. In an exemplary embodiment, each financial instrument is rated based on its qualitative aspects, such as its fund manager's track record, experience, return-maximizing investment strategy, past and current performance, long-term return potential, and / or the like. This evaluation may be performed manually and / or via one or more elements of the processor 200.

[0149] In an exemplary embodiment, the confidence rating may be assigned in the form of a strength or the like based on a range of assessed confidence levels. For example, the confidence rating may be assigned based on a range from "low" to "strong positive."

[0150] Table 5 below illustrates an example of a conviction assessment for a financial instrument.

[0151] [Table 5]

[0152] After the confidence rating assessor 460 performs an assessment and obtains a confidence rating for a financial product, the confidence rating assessor 460 provides such confidence rating information to the financial personality processor 500, the product recommendation processor 600, and / or the handpicking processor 700 for further processing.

[0153] A financial personality processor (eg, financial personality processor 500).

[0154] 2, the processor 200 includes and / or communicates with one or more financial personality processors (e.g., financial personality processor 500). The financial personality processor 500 can be configured or is configured to communicate with the main interface 210 and / or one or more other elements of the system 100.

[0155] In an exemplary embodiment, the financial personality processor 500 is configurable or configured to search, identify, select, compile, generate, convert, process, evaluate, and / or otherwise receive a financial personality of the user 10. The financial personality may be based on a psychometric assessment of the user 10, or the like, for use in determining personality and / or behavioral characteristics related to various aspects of financial planning and / or management. The financial personality of the user 10 may be generated based on multiple aspects, including, but not limited to, a saving personality, a spending personality, an investment personality, a protection personality, a debt personality, and / or the like. For example, the psychometric assessment may measure the user's attitudes, motivations, tendencies, and behaviors surrounding various financial aspects. Additionally, the user's past behaviors and attitudes may be used alone or in conjunction with the psychometric assessment to predict or adjust the user's financial personality.

[0156] After the financial personality processor 500 performs the assessment to obtain the financial personality of the user 10, the financial personality processor 500 provides such financial personality information to the product recommendation processor 600 and / or the handpicking processor 700 for further processing.

[0157] A product recommendation processor (eg, product recommendation processor 600).

[0158] As illustrated at least in FIG. 2 , the processor 200 includes and / or communicates with a product recommendation processor (e.g., product recommendation processor 600 or product recommendation processor 600). The product recommendation processor 600 can be configured or configured to perform various functions. For example, the product recommendation processor 600 can be configured or configured to search for, identify, select, compile, generate, convert, process, evaluate, and / or otherwise receive one or more product recommendations (e.g., financial product recommendations) for the user 10. The product recommendation processor 600 can also be configured or configured to communicate with the main interface 210, the user data processor 300, the financial product processor 400, the financial personality processor, the handpicking processor 700, and / or one or more other elements of the system 100.

[0159] For example, the product recommendation processor 600 can be configured or configured to receive user data and / or other information from the user data processor 300. More specifically, the product recommendation processor 600 can be configured or configured to receive user declaration data, financial institution information, social media information, digital value capture information, product trend information, and / or personal preference information (e.g., information related to financial goals, financial objectives, life stage, financial stage, risk capacity assessment, financial preferences, geographic information, etc.) as processed by the user data selection processor 301 (as described in this disclosure). The product recommendation processor 600 can also be configured or configured to receive financial institution scores for each financial institution used by the user 10, financial institution rankings of financial institutions used by the user 10, and the primary (or primary) financial institution of the user 10 as processed by the primary financial institution generator 310 (as described in this disclosure). The product recommendation processor 600 is also configurable or configured to receive the digital value capture score of the user 10 to be processed by the digital value capture score generator 320 (as described in this disclosure). The product recommendation processor 600 is also configurable or configured to receive the product propensity score of the user 10 to be processed by the product propensity score generator 330 (as described in this disclosure). The product recommendation processor 600 is also configurable or configured to receive financial product data to be processed by the financial product selection processor 401 (as described in this disclosure). The product recommendation processor 600 is also configurable or configured to receive product tenor assessment information to be processed by the product tenor assessor 410 (as described in this disclosure). The product recommendation processor 600 is also configurable or configured to receive product risk assessment information to be processed by the product risk assessment assessor 420 (as described in this disclosure).The product recommendation processor 600 is also configurable or configured to receive advanced product indicator information to be processed by the advanced product indicator assessor 430 (as described in this disclosure). The product recommendation processor 600 is also configurable or configured to receive financial preference information to be processed by the financial preference assessor 450 (as described in this disclosure). The product recommendation processor 600 is also configurable or configured to receive belief rating information to be processed by the belief rating assessor 460 (as described in this disclosure). The product recommendation processor 600 is also configurable or configured to receive financial personality information to be processed by the financial personality processor 500 (as described in this disclosure).

[0160] In one exemplary embodiment, the product recommendation processor 600 is configurable or configured to generate product recommendations for the user 10. Such product recommendations are generated based on one or more of information received or receivable by the product recommendation processor 600 including, but not limited to, one or more of user declared data, financial institution information, social media information, digital value capture information, product propensity information, personal preference information (e.g., information related to financial goals, financial objectives, life stage, financial stage, financial preferences, geographic information, etc.), financial institution scores for each financial institution used by the user 10, financial institution rankings for financial institutions used by the user 10, the primary (or first order) financial institution of the user 10, the digital value capture score of the user 10, the product propensity score of the user 10, financial product data (as processed by the financial product selection processor 401), product tenor assessment information, product risk assessment information, advanced product indicator information, financial preference information, confidence rating information, and / or financial personality information.

[0161] In some exemplary embodiments, the product recommendation processor 600 is also configurable or configured to generate product recommendations for the user 10 based on the handpicks generated by the handpicks generator 700 .

[0162] After the product recommendation processor 600 generates one or more product recommendations for the user 10, the product recommendation processor 600 provides the one or more product recommendations for the user 10 to the output interface 220. In an exemplary embodiment, the product recommendation processor 600 may also provide the one or more product recommendations for the user 10 to the handpicking processor 700 for further processing.

[0163] A handpicked generator (eg, handpicked generator 700).

[0164] As illustrated at least in Figures 2 and 5, the processor 200 includes and / or communicates with one or more pick generators (e.g., pick generator 700 or pick generator 700). The pick generator 700 can be configured or configured to perform various functions. For example, the pick processor 700 can be configured or configured to search for, identify, select, compile, generate, convert, process, evaluate, and / or otherwise receive one or more picks (of financial instruments) for the user 10. The pick processor 700 can also be configured or configured to communicate with the main interface 210, the user data processor 300, the financial instrument processor 400, the financial personality processor, the product recommendation processor 600, and / or one or more other elements of the system 100.

[0165] The selection processor 700 is configurable or configured to receive user data and / or other information from the user data processor 300. For example, the selection processor 700 may be configurable or configured to receive user declaration data, financial institution information, social media information, digital value capture information, product trend information, and / or personal preference information (e.g., information related to financial goals, financial objectives, life stage, financial stage, financial preferences, geographic information, etc.) to be processed by the user data selection processor 301 (as described in this disclosure). The selection processor 700 may also be configurable or configured to receive a financial institution score for each financial institution used by the user 10, a financial institution ranking of financial institutions used by the user 10, and the primary (or primary) financial institution of the user 10 to be processed by the primary financial institution generator 310 (as described in this disclosure). The selection processor 700 may also be configurable or configured to receive a digital value capture score of the user 10 to be processed by the digital value capture score generator 320 (as described in this disclosure). The handpicking processor 700 may also be configurable or configured to receive product propensity scores of the user 10 to be processed by the product propensity score generator 330 (as described in this disclosure). The handpicking processor 700 may also be configurable or configured to receive financial product data to be processed by the financial product selection processor 401 (as described in this disclosure). The handpicking processor 700 may also be configurable or configured to receive product tenor assessment information to be processed by the product tenor assessor 410 (as described in this disclosure). The handpicking processor 700 may also be configurable or configured to receive product risk rating information to be processed by the product risk rating assessor 420 (as described in this disclosure).The handpicking processor 700 may also be configurable or configured to receive advanced product index information to be processed by the advanced product index assessor 430 (as described in this disclosure). The handpicking processor 700 may also be configurable or configured to receive financial preference information to be processed by the financial preference assessor 450 (as described in this disclosure). The handpicking processor 700 may also be configurable or configured to receive confidence rating information to be processed by the confidence rating assessor 460 (as described in this disclosure). The handpicking processor 700 may also be configurable or configured to receive financial personality information to be processed by the financial personality processor 500 (as described in this disclosure).

[0166] In one exemplary embodiment, the handpicked processor 700 is configurable or configured to generate handpicked selections for the user 10 . Such selections may be generated based on one or more of the following information received or receivable by the selection processor 700: customer look-like assessment, customer propensity assessment, product look-like assessment, and / or information received or receivable by the selection processor 700 including, but not limited to, one or more of user declared data, financial institution information, social media information, digital value capture information, product propensity information, personal preference information (e.g., information related to financial goals, financial objectives, life stage, financial stage, financial preferences, geographical information, etc.), financial institution scores for each financial institution used by the user 10, financial institution rankings for financial institutions used by the user 10, the primary (or first) financial institution of the user 10, the digital value capture score of the user 10, the product propensity score of the user 10, financial product data (as processed by the financial product selection processor 401), product tenor assessment information, product risk rating information, advanced product indicator information, financial preference information, confidence rating information, financial personality information, and / or product recommendations (as generated by the product recommendation processor 600). For example, the handpicks generated by handpicking generator 700 may be financial instruments selected based on two or more of a customer look-like assessment, a customer propensity assessment, and a product look-like assessment.

[0167] To perform the operations, functions, processes, and / or methods described above and in this disclosure, an exemplary embodiment of handpicked generator 700 comprises a handpicked interface (e.g., handpicked interface 701). Handpicked generator 700 also includes a customer look-like processor (e.g., customer look-like processor 710). Handpicked generator 700 also includes a customer propensity processor (e.g., customer propensity processor 720). Handpicked generator 700 also includes a product look-like processor (e.g., product look-like processor 730).

[0168] These and other elements of handpicked generator 700 will now be further described with reference to the accompanying figures.

[0169] A select interface (eg, select interface 701).

[0170] 2 and 5 , the handpicked generator 700 includes and / or communicates with one or more user handpicking interfaces (e.g., handpicked interface 701). Handpicked interface 701 is configurable or configured to retrieve, identify, select, compile, generate, convert, process, evaluate, and / or otherwise receive user data (e.g., from user data processor 300), financial instrument data (e.g., from financial instrument processor 400), financial personality information (e.g., from financial personality processor 500), product recommendations (e.g., from product recommendation processor 600), and / or one or more other elements of system 100 for further processing by customer look-like processor 710, customer propensity processor 720, and product look-like processor 730.

[0171] A customer look-like processor (eg, customer look-like processor 710).

[0172] 5, an exemplary embodiment of the handpicked generator 700 includes one or more customer look-like processors (e.g., customer look-like processor 710). The customer look-like processor 710 is configurable or configured to generate a handpicked list of financial instruments based on one or more other users 10 who are “look-alikes” of, resemble or are identical to, have similar user data (as generated by user data processor 300), have similar financial instrument data (as generated by financial instrument processor 400), have similar financial personality information (as generated by financial personality processor 500), and / or have similar product recommendations (as generated by product recommendation processor 600) (also referred to herein as “customer look-like models”).

[0173] The user 10 is initially classified into one of two categories: Existing-to-Products (ETP) and New-to-Products (NTP). For example, if the user 10 is in the ETP category, the Customer Look-Like Processor 710 may process other users who are also classified as ETPs. Other attributes used to determine other users who are "just like" the user 10 may include, but are not limited to, demographics (e.g., life stage, age, etc.), banking relationships (e.g., financial institution rankings, balances of accounts held by the user 10, etc.), product trading behavior (e.g., trading frequency, recency, value, etc.), product holdings, risk capacity ratings, and / or the like.

[0174] Similarity (or look-like) among users 10 classified as ETPs may be determined primarily by past related product-taking behavior. For example, users 10 may be clustered with other users 10 with most similar behavior, thereby forming one or more user clusters. In each cluster, the most popular financial products purchased by other users in that cluster may be identified as the picks for user 10.

[0175] The similarity (or look-like) among users 10 classified as NTPs may be determined primarily by commonality of demographic profiles and / or other financial metrics including assets and liabilities, cash flows, and / or the like. Users 10 are clustered into one or more user clusters similar to ETP users. Financial instruments with the highest trading volumes and / or revenues within a cluster of users may be identified as top picks for users 10.

[0176] A customer trend processor (eg, customer trend processor 720).

[0177] As illustrated in at least FIGURE 3, an exemplary embodiment of the pick generator 700 includes one or more customer propensity processors (e.g., customer propensity processor 720). The customer propensity processor 720 is configurable or configured to generate a pick list of financial instruments based on a user's 10 likelihood of taking up available funds (also referred to herein as a "customer propensity model").

[0178] The customer propensity processor 720 is configurable or configured to cluster funds into two or more fund clusters, which may additionally incorporate user information (e.g., from the user data processor 300 and / or the financial personality processor 500) to determine the likelihood that a user 10 will take any particular fund from each fund cluster.

[0179] A product look-like processor (eg, product look-like processor 730).

[0180] 3, an exemplary embodiment of the handpicked generator 700 includes one or more product look-like processors (e.g., product look-like processor 730). The product look-like processor 730 is configurable or configured to generate a handpicked list of financial instruments based on similarities between financial instruments previously purchased by the user 10, financial instruments that the user 10 has previously expressed interest in purchasing, and / or product recommendations from the product recommendation generator 600 (also referred to herein as a “product look-like model”).

[0181] In an exemplary embodiment, the product look-like processor 730 is configurable or may be configured to select handpicked items for users classified as ETPs (e.g., due to prior purchases or interest in financial products). Product similarity may be determined primarily based on product meta-attributes (e.g., for unit investment trusts, meta-attributes may be fund house, estimated price, asset type, asset sub-type, tenor, minimum investment amount, Regular Savings Plan (RSP) index (e.g., an indication of whether the product offers a monthly subscription option), etc.).

[0182] Each product can be matched with a selection of its most "look-alike" products based on similarities in meta attributes. For example, if a user has purchased a particular product in the past, up to five of the most relevant products can be prioritized as a pick for the user under a product look-like model.

[0183] A handpicked generator (eg, handpicked generator 740).

[0184] 5, an example embodiment of the handpicked generator 700 includes one or more handpicked generators (e.g., handpicked generator 740). The handpicked generator 740 is configurable or configured to generate a final list of handpicked financial instruments based on the list of handpicked items generated by the customer look-like processor 710, the customer propensity processor 720, and / or the product look-like processor 730.

[0185] For example, the handpicked generator 740 may receive lists of handpicked items from the customer look-like processor 710, the customer propensity processor 720, and the product look-like processor 730 and generate a final list of handpicked items based on all three lists of handpicked items. Alternatively, the handpicked generator 740 may receive lists of handpicked items from the customer look-like processor 710, the customer propensity processor 720, and the product look-like processor 730 and generate a final list of handpicked items based on at least two of the lists of handpicked items.

[0186] An output interface (eg, output interface 220).

[0187] As illustrated in at least FIG. 2, the processor 200 includes and / or is in communication with one or more output interfaces (eg, output interface 220).

[0188] In an exemplary embodiment, the output interface 220 is configurable or configured to receive product recommendations for the user 10 from the product recommendation processor 600. The output interface 220 is then configurable or configured to transmit, make available, display, store, and / or otherwise provide the product recommendations to the user 10. Alternatively, or in addition, the output interface 220 is configurable or configured to store the product recommendations for the user 10 in the database 20.

[0189] In an exemplary embodiment, the output interface 220 is also configurable or configured to receive a top list of financial instruments for the user 10 from the top list processor 700. The output interface 220 is then configurable or configured to transmit, make available, display, store, and / or otherwise provide the top list of financial instruments to the user 10. Alternatively, or in addition, the output interface 220 is configurable or configured to store the top list of financial instruments for the user 10 in the database 20.

[0190] In an exemplary embodiment, output interface 220 is also configurable or configured to receive other information from other elements of system 100, including from main interface 210, user data processor 300, financial instrument processor 400, and / or financial personality processor 500. Output interface 220 is then configurable or configured to transmit, make available, display, store, and / or otherwise provide such information to user 10. Alternatively, or in addition, output interface 220 is configurable or configured to store such information in database 20.

[0191] While various embodiments according to the disclosed principles have been described above, it will be understood that they have been presented by way of example only and not by way of limitation. Thus, the breadth and scope of the exemplary embodiments described in this disclosure should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the claims and their equivalents derived from this disclosure. Moreover, the above-mentioned advantages and features are provided in the described embodiments, but should not limit the application of the scope of such issued claims to processes and structures that achieve any or all of the above-mentioned advantages.

[0192] Also, as referred to in this specification, a processor, device, computing device, telephone, mobile phone, mobile device, server, generator, subsystem, and / or controller may be any processor, computing device, and / or communication device, and may include a virtual machine, computer, node, instance, host, or machine in a networked computing environment.

[0193] Various terms used herein have specific meanings within the technical field. Whether a particular term should be interpreted as such a "terminology" depends on the context in which the term is used. Such terms should be interpreted in light of the context in which they are used in this disclosure and as a person skilled in the art would understand these terms in the disclosed context. The above definitions do not exclude other meanings that may be given to those terms based on the disclosed context.

[0194] Furthermore, the section and topic headings herein are provided to provide consistency with suggestions based on various patent rules and practices or to provide some other organizational clue. These headings are not intended to limit or characterize the embodiments described in any claim that may be issued from this disclosure. For example, a description of a technology in the "Background" or the like is not intended to be construed as an admission that such technology is prior art to the exemplary embodiments in this disclosure. Furthermore, references to the singular "invention" in this disclosure should not be used to assert that there is only a single point of novelty in this disclosure. Multiple inventions may be defined according to the limitations of the claims that issue from this disclosure, and such claims accordingly define the inventions and their equivalents that are protected thereby. In all cases, the scope of such claims shall be considered on their own merits in light of this disclosure, but should not be constrained by the headings herein. [Explanation of symbols]

[0195] 10 users 20 Database / Data storage / Blockchain / etc. 20 Database 20 Data Storage Systems 20. Blockchain 20 Distributed Ledger Technology (DLT) 30 Sources of information 30 Government Agencies 30 Pseudo-Government Agencies 30 Industry / financial / regulatory / etc governing bodies 30 Financial Institutions 30 Bank 30 Digital Banking 30 Fintech Organizations 30 Cryptocurrency Providers / Exchanges / Banks / etc. 30 Insurance Organizations 30 Guarantor 30 Social Media Platforms 30 Social Media Systems 30 Social Media Networks 30 Search Engines 30 Telecommunications Organization 30 Transport organization 30 Multimedia Organization 30 Artificial Intelligence (AI) Systems 30 Quantum Computing Systems 50 Network / Internet / Cloud Computing / Web / Public Cloud / Private Cloud / etc. 50 Network 50 Internet 50 Cloud Computing 50 World Wide Web 50 Public Cloud 50 Private Cloud 100 Systems 200 Financial Processors 200 processors 210 Main Interface 220 Output Interface 300 User Data Processor 301 User Data Selection Processor 310 Major Financial Institutions Score Generator 320 Digital Value Captcha Score Generator 330 Product Propensity Score Generator 400 Financial Instruments Processors 401 Financial Instruments Selection Processor 410 Product Tenor Assessor 420 Risk Assessment Assessor 430 Advanced Product Indicator Assessor 440 Financial Goals Assessor 450 Financial Preference Assessor 460 Confidence Assessment Assessor 500 Financial Personality Processor 600 Product Recommendation Processor 700 carefully selected processors 701 carefully selected interface 710 Customer Look Like Processor 720 Customer Trend Processor 730 Product Look Like Processor 740 Carefully Selected Generator

Claims

1. 1. A method for managing user financial information, comprising: performing a primary financial institution transaction; generating one or more product recommendations for the first user; Including, The major financial institution processing is retrieving, for the first user, all financial institutions at which the first user has at least one financial account, the financial institutions including a first financial institution and a second financial institution; For each of the financial institutions identified by the search, generating a total inflow of value into all financial accounts held by the first user during a first time period, the total inflow of value including a first total inflow of value and a second total inflow of value, the first total inflow of value being a total inflow of value into all financial accounts held by the first user at the first financial institution during the first time period, and the second total inflow of value being a total inflow of value into all financial accounts held by the first user at the second financial institution during the first time period; generating a total outflow of value from all financial accounts held by the first user during the first time period, the total outflow of value including a first total outflow of value and a second total outflow of value, the first total outflow of value being a total outflow of value from all financial accounts held by the first user to the first financial institution during the first time period, and the second total outflow of value being a total outflow of value from all financial accounts held by the first user to the second financial institution during the first time period; generating an inflow / outflow ratio for the first user for the first time period based on the total inflow of value generated and the total outflow of value generated, the inflow / outflow ratio comprising a first inflow / outflow ratio and a second inflow / outflow ratio, the first inflow / outflow ratio being a ratio of the first total inflow of value to the first total outflow of value, and the second inflow / outflow ratio being a ratio of the second total inflow of value to the second total outflow of value; identifying all periodic payments made by all financial accounts held by the first user during the first time period, the periodic payments including a first set of periodic payments and a second set of periodic payments, the first set of periodic payments being all periodic payments made by all financial accounts held by the first user with the first financial institution during the first time period, and the second set of periodic payments being all periodic payments made by all financial accounts held by the first user with the second financial institution during the first time period; identifying all non-periodic payments made by all financial accounts held by the first user during the first time period, the non-periodic payments including a first set of non-periodic payments and a second set of non-periodic payments, the first set of non-periodic payments being all non-periodic payments made by all financial accounts held by the first user with the first financial institution during the first time period, and the second set of non-periodic payments being all non-periodic payments made by all financial accounts held by the first user with the second financial institution during the first time period; generating a financial institution score for the first user based on at least one of the inflow / outflow ratio, the identified regular payments, and the non-regular payments, the financial institution scores including a first financial institution score and a second financial institution score, the first financial institution score being generated based on at least one of the first inflow / outflow ratio, the first set of regular payments, and the first set of non-regular payments, and the second financial institution score being generated based on at least one of the second inflow / outflow ratio, the second set of regular payments, and the second set of non-regular payments; determining a ranking of financial institutions for the first user in the first time period, the ranking of financial institutions for the first user in the first time period being comparing the financial institution scores for each of the financial institutions identified by the search, the financial institution scores including the first financial institution score and the second financial institution score; ranking the financial institutions identified by the search based on the comparison; and The steps and Including, The one or more product recommendations for the first user include: one or more of the financial institution scores generated for the first user; and The financial institution ranking for the first user The method is generated based on at least one of the following:

2. 2. The method of claim 1, further comprising the step of selecting a primary financial institution for the first user in the first time period, wherein the primary financial institution for the first user in the first time period is the financial institution identified by the search having a highest ranking, and wherein the generation of the one or more product recommendations is further based on the selected primary financial institution for the first user in the first time period.

3. 2. The method of claim 1, further comprising the step of selecting a primary financial institution for the first user in the first time period, wherein the primary financial institution for the first user in the first time period is the financial institution identified by the search having a highest financial institution score, and wherein the generation of the one or more product recommendations is further based on the selected primary financial institution for the first user in the first time period.

4. The method of claim 1 , wherein the first financial institution is ranked higher than the second financial institution when the first financial institution score is higher than the second financial institution score.

5. 2. The method of claim 1, wherein the financial institution score for each of the financial institutions identified by the search is further based on one or more of financial interactions of the financial account at the financial institution, deposits to the financial account at the financial institution, investments in the financial account at the financial institution, loans on the financial account at the financial institution, non-financial interactions with the financial institution, a balance of the financial account at the financial institution, and a utilization level for the financial institution.

6. receiving user data for the first user, the user data for the first user comprising: one or more customer declared information of the first user, including information provided by the first user; one or more financial institution information of the first user, including information obtainable from one or more financial accounts at one or more financial institutions held by the first user; one or more social media information of the first user, including information obtainable from one or more social media accounts held by the first user; and One or more personal preference information of the first user, including financial goals, financial objectives, a life stage of the first user, a financial stage of the first user, and a financial preference of the first user. The method of claim 1 , further comprising at least one of the steps:

7. The method of claim 6 , wherein the generating of the one or more product recommendations is further based on the user data of the first user.

8. The method of claim 1 , wherein the first financial institution score is a score representative of the first user's usage of the first financial institution during the first time period.

9. The method of claim 1 , wherein the second financial institution score is a score representative of the first user's usage of the second financial institution during the first time period.

10. 2. The method of claim 1, further comprising: generating a digital value capture score for the first user, the digital value capture score for the first user representing the first user's preferences in digitally engaging, the digital value capture score for the first user being generated based on at least one of one or more transactions made by the first user, one or more channels used by the first user, one or more investment financial products purchased by the first user, a number of times the first user used digital channels, a number of times the first user used non-digital channels, a number of times the first user interacted interactively based on digital communications, and a number of times the first user interacted interactively based on non-digital communications.

11. The method of claim 10 , wherein the generating of the one or more product recommendations is further based on the digital value capture score for the first user.

12. 2. The method of claim 1, further comprising: generating a product propensity score for the first user, the product propensity score for the first user representing the first user's likely interest in one or more financial products, the product propensity score for the first user being generated based on at least one of a CASA balance, one or more of the financial institution scores, balances of one or more financial accounts held by the first user, and an income of the first user.

13. The method of claim 12 , wherein the generating of the one or more product recommendations is further based on the product propensity score for the first user.

14. 13. The method of claim 1, further comprising: generating real-time financial product information for one or more financial instruments, wherein the real-time financial product information is generated based on at least one of a product tenor, a product risk rating, a sophistication level of the financial instrument, a financial objective of the financial instrument, a risk capacity assessment of the financial instrument, and a conviction rating of the financial instrument.

15. The method of claim 14 , wherein the generating of the one or more product recommendations is further based on the real-time financial product information.

16. 2. The method of claim 1, further comprising the step of generating a financial personality for the first user, the financial personality of the first user being a psychometric assessment of the first user to determine personality and / or behavioral characteristics related to different aspects of the first user's financial planning, and the financial personality of the first user being generated based on at least one of the first user's savings personality, the first user's spending personality, the first user's investment personality, the first user's protection personality, and the first user's debt personality.

17. The method of claim 16 , wherein the generating of the one or more product recommendations is further based on the financial personality of the first user.

18. generating one or more product picks for the first user from the one or more product recommendations generated for the first user, the one or more product picks for the first user comprising: a customer look-like model that ranks financial products based on other users that are similar to the first user; a customer propensity model that ranks financial products based on at least one of customer demographics, customer banking relationships, product trading behavior, financial product holdings, and customer risk assessment; a product look-like model that ranks financial products based on financial product similarity compared to financial products previously purchased by the first user and / or financial products in which the first user is interested. The method of claim 1 , further comprising the step of generating the signal based on at least one of:

19. 1. A method for managing user financial information, comprising: performing a primary financial institution transaction for the first user; generating one or more product recommendations for the first user; Including, The primary financial institution transaction for the first user includes: retrieving, for the first user, all financial institutions at which the first user has at least one financial account, the financial institutions including a first financial institution and a second financial institution; For each of the financial institutions identified by the search, generating an inflow / outflow ratio for the first user for a first time period, the generating of the inflow / outflow ratio for the first user for the first time period comprising: generating a total inflow of value into all financial accounts held by the first user during the first time period, the total inflow of value including a first total inflow of value and a second total inflow of value, the first total inflow of value being a total inflow of value into all financial accounts held by the first user at the first financial institution during the first time period, and the second total inflow of value being a total inflow of value into all financial accounts held by the first user at the second financial institution during the first time period; generating a total outflow of value from all financial accounts held by the first user during the first time period, the total outflow of value including a first total outflow of value and a second total outflow of value, the first total outflow of value being a total outflow of value from all financial accounts held by the first user to the first financial institution during the first time period, and the second total outflow of value being a total outflow of value from all financial accounts held by the first user to the second financial institution during the first time period; generating an inflow / outflow ratio for the first user for the first time period based on the total inflow of value generated and the total outflow of value generated, the inflow / outflow ratio comprising a first inflow / outflow ratio and a second inflow / outflow ratio, the first inflow / outflow ratio being a ratio of the first total inflow of value to the first total outflow of value, and the second inflow / outflow ratio being a ratio of the second total inflow of value to the second total outflow of value; The steps include: identifying all periodic payments made by all financial accounts held by the first user during the first time period, the periodic payments including a first set of periodic payments and a second set of periodic payments, the first set of periodic payments being all periodic payments made by all financial accounts held by the first user with the first financial institution during the first time period, and the second set of periodic payments being all periodic payments made by all financial accounts held by the first user with the second financial institution during the first time period; identifying all non-periodic payments made by all financial accounts held by the first user during the first time period, the non-periodic payments including a first set of non-periodic payments and a second set of non-periodic payments, the first set of non-periodic payments being all non-periodic payments made by all financial accounts held by the first user with the first financial institution during the first time period, and the second set of non-periodic payments being all non-periodic payments made by all financial accounts held by the first user with the second financial institution during the first time period; and performing at least one of generating a financial institution score for the first user based on at least one of the inflow / outflow ratio, the identified regular payments, and the non-regular payments, the financial institution scores including a first financial institution score and a second financial institution score, the first financial institution score being generated based on at least one of the first inflow / outflow ratio, the first set of regular payments, and the first set of non-regular payments, and the second financial institution score being generated based on at least one of the second inflow / outflow ratio, the second set of regular payments, and the second set of non-regular payments; determining a ranking of financial institutions for the first user in the first time period, the ranking of financial institutions for the first user in the first time period being comparing the financial institution scores for each of the financial institutions identified by the search, the financial institution scores including the first financial institution score and the second financial institution score; ranking the financial institutions identified by the search based on the comparison; and The steps and Including, The one or more product recommendations for the first user include: one or more of the financial institution scores generated for the first user; and The financial institution ranking for the first user The method is generated based on at least one of the following:

20. 20. The method of claim 19, further comprising the step of selecting a primary financial institution for the first user in the first time period, wherein the primary financial institution for the first user in the first time period is the financial institution identified by the search having a highest ranking, and wherein the generation of the one or more product recommendations is further based on the selected primary financial institution for the first user in the first time period.

21. 20. The method of claim 19, further comprising the step of selecting a primary financial institution for the first user in the first time period, the primary financial institution for the first user in the first time period being the financial institution identified by the search having a highest financial institution score, and the generation of the one or more product recommendations is further based on the selected primary financial institution for the first user in the first time period.

22. 20. The method of claim 19, wherein the first financial institution is ranked higher than the second financial institution when the first financial institution score is higher than the second financial institution score.

23. 20. The method of claim 19, wherein the financial institution score for each of the financial institutions identified by the search is further based on one or more of financial interactions of the financial account at the financial institution, deposits to the financial account at the financial institution, investments in the financial account at the financial institution, loans on the financial account at the financial institution, non-financial interactions with the financial institution, a balance of the financial account at the financial institution, and a utilization level for the financial institution.

24. receiving user data for the first user, the user data for the first user comprising: one or more customer declared information of the first user, including information provided by the first user; one or more financial institution information of the first user, including information obtainable from one or more financial accounts at one or more financial institutions held by the first user; one or more social media information of the first user, including information obtainable from one or more social media accounts held by the first user; and One or more personal preference information of the first user, including financial goals, financial objectives, a life stage of the first user, a financial stage of the first user, and a financial preference of the first user. The method of claim 19 , further comprising at least one of the steps of:

25. 25. The method of claim 24, wherein the generating of the one or more product recommendations is further based on the user data of the first user.

26. 20. The method of claim 19, wherein the first financial institution score is a score representative of the first user's usage of the first financial institution during the first time period.

27. 20. The method of claim 19, wherein the second financial institution score is a score representative of the first user's usage of the second financial institution during the first time period.

28. 20. The method of claim 19, further comprising: generating a digital value capture score for the first user, the digital value capture score for the first user representing the first user's preferences in digitally engaging, the digital value capture score for the first user being generated based on at least one of one or more transactions made by the first user, one or more channels used by the first user, one or more investment financial products purchased by the first user, a number of times the first user used digital channels, a number of times the first user used non-digital channels, a number of times the first user interacted interactively based on digital communications, and a number of times the first user interacted interactively based on non-digital communications.

29. 30. The method of claim 28, wherein the generating of the one or more product recommendations is further based on the digital value capture score for the first user.

30. 20. The method of claim 19, further comprising: generating a product propensity score for the first user, the product propensity score for the first user representing the first user's likely interest in one or more financial products, the product propensity score for the first user being generated based on at least one of a CASA balance, one or more of the financial institution scores, balances of one or more financial accounts held by the first user, and an income of the first user.

31. 31. The method of claim 30, wherein the generating of the one or more product recommendations is further based on the product propensity score for the first user.

32. 20. The method of claim 19, further comprising: generating real-time financial product information for one or more financial instruments, wherein the real-time financial product information is generated based on at least one of a product tenor, a product risk rating, a sophistication level of the financial instrument, a financial objective of the financial instrument, a risk capacity assessment of the financial instrument, and a conviction rating of the financial instrument.

33. 33. The method of claim 32, wherein the generating of the one or more product recommendations is further based on the real-time financial product information.

34. 20. The method of claim 19, further comprising the step of generating a financial personality for the first user, the financial personality of the first user being a psychometric assessment of the first user to determine personality and / or behavioral characteristics related to different aspects of the first user's financial planning, and the financial personality of the first user being generated based on at least one of the first user's savings personality, the first user's spending personality, the first user's investment personality, the first user's protection personality, and the first user's debt personality.

35. 35. The method of claim 34, wherein the generating of the one or more product recommendations is further based on the financial personality of the first user.

36. generating one or more product picks for the first user from the one or more product recommendations generated for the first user, the one or more product picks for the first user comprising: a customer look-like model that ranks financial products based on other users that are similar to the first user; a customer propensity model that ranks financial products based on at least one of customer demographics, customer banking relationships, product trading behavior, financial product holdings, and customer risk assessment; a product look-like model that ranks financial products based on financial product similarity compared to financial products previously purchased by the first user and / or financial products in which the first user is interested. The method of claim 19 , further comprising the step of: generating the first and second metric based on at least one of:

37. 1. A method for managing user financial information, comprising: receiving user data for a first user, the user data for the first user comprising: one or more customer declared information of the first user, including information provided by the first user; one or more financial institution information of the first user, including information obtainable from one or more financial accounts at one or more financial institutions held by the first user; one or more social media information of the first user, including information obtainable from one or more social media accounts held by the first user; One or more personal preference information of the first user, including financial goals, financial objectives, a life stage of the first user, a financial stage of the first user, and a financial preference of the first user. and executing a primary financial institution process, said primary financial institution process comprising: searching, for a first user, all financial institutions at which the first user has at least one financial account, the financial institutions including a first financial institution and a second financial institution; For each of the financial institutions identified by the search, generating a total inflow of value into all financial accounts held by the first user during a first time period, the total inflow of value including a first total inflow of value and a second total inflow of value, the first total inflow of value being a total inflow of value into all financial accounts held by the first user at the first financial institution during the first time period, and the second total inflow of value being a total inflow of value into all financial accounts held by the first user at the second financial institution during the first time period; generating a total outflow of value from all financial accounts held by the first user during the first time period, the total outflow of value including a first total outflow of value and a second total outflow of value, the first total outflow of value being a total outflow of value from all financial accounts held by the first user to the first financial institution during the first time period, and the second total outflow of value being a total outflow of value from all financial accounts held by the first user to the second financial institution during the first time period; generating an inflow / outflow ratio for the first user for the first time period based on the total inflow of value generated and the total outflow of value generated, the inflow / outflow ratio comprising a first inflow / outflow ratio and a second inflow / outflow ratio, the first inflow / outflow ratio being a ratio of the first total inflow of value to the first total outflow of value, and the second inflow / outflow ratio being a ratio of the second total inflow of value to the second total outflow of value; identifying all periodic payments made by all financial accounts held by the first user during the first time period, the periodic payments including a first set of periodic payments and a second set of periodic payments, the first set of periodic payments being all periodic payments made by all financial accounts held by the first user with the first financial institution during the first time period, and the second set of periodic payments being all periodic payments made by all financial accounts held by the first user with the second financial institution during the first time period; identifying all non-periodic payments made by all financial accounts held by the first user during the first time period, the non-periodic payments including a first set of non-periodic payments and a second set of non-periodic payments, the first set of non-periodic payments being all non-periodic payments made by all financial accounts held by the first user with the first financial institution during the first time period, and the second set of non-periodic payments being all non-periodic payments made by all financial accounts held by the first user with the second financial institution during the first time period; generating a financial institution score for the first user based on at least one of the inflow / outflow ratio, the identified regular payments, and the non-regular payments, the financial institution scores including a first financial institution score and a second financial institution score, the first financial institution score being generated based on at least one of the first inflow / outflow ratio, the first set of regular payments, and the first set of non-regular payments, and the second financial institution score being generated based on at least one of the second inflow / outflow ratio, the second set of regular payments, and the second set of non-regular payments; determining a ranking of financial institutions for the first user in the first time period, the ranking of financial institutions for the first user in the first time period being comparing the financial institution scores for each of the financial institutions identified by the search, the financial institution scores including the first financial institution score and the second financial institution score; ranking the financial institutions identified by the search based on the comparison; and The steps and performing a primary financial institution transaction, generating a digital value capture score for the first user, the digital value capture score for the first user representing the first user's preferences in digitally engaging, the digital value capture score for the first user being generated based on at least one of one or more transactions made by the first user, one or more channels used by the first user, one or more investment products purchased by the first user, a number of times the first user used digital channels, a number of times the first user used non-digital channels, a number of times the first user interacted interactively based on digital communications, and a number of times the first user interacted interactively based on non-digital communications; generating a product propensity score for the first user, the product propensity score for the first user representing the first user's likely interest in one or more financial products, the product propensity score for the first user being generated based on at least one of a CASA balance, one or more of the financial institution scores, balances of one or more financial accounts held by the first user, and an income of the first user; generating real-time financial product information for one or more financial instruments, the real-time financial product information being generated based on at least one of a product tenor, a product risk rating, a sophistication level of the financial instrument, a financial objective of the financial instrument, a risk capacity assessment of the financial instrument, and a conviction rating of the financial instrument; generating a financial personality for the first user, the financial personality of the first user being a psychometric assessment of the first user to determine personality and / or behavioral characteristics related to different aspects of the first user's financial planning, the financial personality of the first user being generated based on at least one of the first user's savings personality, the first user's spending personality, the first user's investment personality, the first user's protection personality, and the first user's debt personality; generating one or more product recommendations for the first user, the one or more product recommendations for the first user comprising: one or more of the financial institution score generated for the first user and / or the financial institution ranking for the first user; the user data for the first user; the digital value capture score for the first user; The product tendency score for the first user; said real-time financial instrument information for one or more financial instruments; and The financial personality of the first user Based on the steps and The method includes:

38. 38. The method of claim 37, further comprising the step of selecting a primary financial institution for the first user in the first time period, wherein the primary financial institution for the first user in the first time period is the financial institution identified by the search having a highest ranking, and wherein the generation of the one or more product recommendations is further based on the selected primary financial institution for the first user in the first time period.

39. 38. The method of claim 37, further comprising the step of selecting a primary financial institution for the first user in the first time period, wherein the primary financial institution for the first user in the first time period is the financial institution identified by the search having a highest financial institution score, and wherein the generation of the one or more product recommendations is further based on the selected primary financial institution for the first user in the first time period.

40. 38. The method of claim 37, wherein the financial institution score for each of the financial institutions identified by the search is further based on one or more of financial interactions of the financial account at the financial institution, deposits to the financial account at the financial institution, investments in the financial account at the financial institution, loans on the financial account at the financial institution, non-financial interactions with the financial institution, a balance of the financial account at the financial institution, and a utilization level for the financial institution.

41. generating one or more product picks for the first user from the one or more product recommendations generated for the first user, the one or more product picks for the first user comprising: a customer look-like model that ranks financial products based on other users that are similar to the first user; a customer propensity model that ranks financial products based on at least one of customer demographics, customer banking relationships, product trading behavior, financial product holdings, and customer risk assessment; a product look-like model that ranks financial products based on financial product similarity compared to financial products previously purchased by the first user and / or financial products in which the first user is interested. The method of claim 37, further comprising the step of generating the signal based on at least one of:

Images