Financial bill installment scheme recommendation method and device, medium and electronic equipment

Abstract

The invention discloses a financial bill installment scheme recommendation method and device, a medium and electronic equipment. The method comprises the steps that operation type data and browsing path data of a target object acting on a financial service application are determined; determining consumption behavior data, repayment capability data, historical staging data and basic attribute data of the target object; based on the operation type data, the browsing path data, the consumption behavior data, the repayment capability data, the historical staging data and the basic attribute data, determining a staging preference type to which the target object belongs; and according to the installment preference type, recommending a financial bill installment scheme for the target object. According to the technical scheme, the intelligent level of financial bill installment scheme recommendation can be improved, and the risk coverage rate of the financial bill installment scheme is increased.

Description

Technical Field

[0001] This application relates to the fields of artificial intelligence technology, big data and financial data processing, and can be applied to financial technology scenarios. This application specifically relates to a method, device, medium and electronic device for recommending a financial bill installment plan. Background Art

[0002] Financial bill installment refers to a business model in which credit card holders convert the remaining balance on their bills into installment payments through financial institutions (such as banks) after making a purchase.

[0003] Current financial bill installment plans are typically recommended based on static rules. For example, fixed installment options and rates are set for users to choose from. This leads to poor intelligence and insufficient risk coverage. Summary of the Invention

[0004] The present application provides a method, device, medium and electronic device for recommending a financial bill installment plan, which can improve the intelligence level of financial bill installment plan recommendations and improve the risk coverage of financial bill installment plans.

[0005] According to a first aspect of the present application, a method for recommending a financial bill installment plan is provided, the method comprising:

[0006] Determine the target object's operation type data and browsing path data on the financial services application;

[0007] Determine the target object's consumption behavior data, solvency data, historical installment data, and basic attribute data;

[0008] Determining the installment preference type of the target object based on the operation type data, the browsing path data, the consumption behavior data, the solvency data, the historical installment data, and the basic attribute data;

[0009] Recommend a financial bill installment plan to the target object based on the installment preference type.

[0010] According to a second aspect of the present application, a financial bill installment plan recommendation device is provided, the device comprising:

[0011] A first data determination module is used to determine operation type data and browsing path data of a target object acting on a financial service application;

[0012] A second data determination module is used to determine the consumption behavior data, solvency data, historical installment data and basic attribute data of the target object;

[0013] a preference type determination module, configured to determine the installment preference type to which the target object belongs based on the operation type data, the browsing path data, the consumption behavior data, the solvency data, the historical installment data, and the basic attribute data;

[0014] The installment plan recommendation module is used to recommend a financial bill installment plan for the target object based on the installment preference type.

[0015] According to a third aspect of the present invention, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for recommending a financial bill installment plan as described in the embodiment of the present application.

[0016] According to the fourth aspect of the present invention, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for recommending a financial bill installment plan as described in the embodiment of the present application is implemented.

[0017] According to a fifth aspect of the present application, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method for recommending a financial bill installment plan as described in the embodiment of the present application.

[0018] The technical solution of this application takes into account the target object's decision-making hesitation, decision-making logic, recent consumption ability, repayment qualifications, installment preferences and installment risks, among other preference factors that affect the target object's installment. Based on operation type data, browsing path data, consumption behavior data, solvency data, historical installment data and basic attribute data, the target object's installment preference type is determined from the risk dimension and preference dimension. Based on the target object's installment preference type, installment plans are recommended to the target object. This can improve the intelligence level of financial bill installment plan recommendations and the risk coverage of financial bill installment plans, and realize customized personalized financial bill installment plans for the target object.

[0019] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0021] Figure 1 is a flow chart of a method for recommending a financial bill installment plan according to the first embodiment;

[0022] Figure 2 This is a flow chart of a method for recommending a financial bill installment plan according to the second embodiment;

[0023] Figure 3 This is a schematic diagram of the structure of the financial bill installment plan recommendation device provided in Example 3 of the present application;

[0024] Figure 4 This is a structural diagram of an electronic device provided in Example 4 of the present application. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0026] It should be noted that the terms "first", "second", "target" and "candidate" in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] Example 1

[0028] Figure 1 This is a flowchart of a method for recommending a financial bill installment plan according to the first embodiment. This embodiment is applicable to situations where financial bill installment plans, such as credit card installment plans, are recommended. The method can be configured to be executed by a financial bill installment plan recommendation device. The financial bill installment plan recommendation device is implemented in the form of hardware and / or software and can be integrated into an electronic device that runs this financial bill installment plan recommendation system.

[0029] like Figure 1As shown, the method includes:

[0030] S110: Determine operation type data and browsing path data of the target object acting on the financial service application.

[0031] S120: Determine the consumption behavior data, solvency data, historical installment data, and basic attribute data of the target object.

[0032] S130. Determine the installment preference type of the target object based on the operation type data, the browsing path data, the consumption behavior data, the solvency data, the historical installment data, and the basic attribute data.

[0033] S140: Recommend a financial bill installment plan to the target object based on the installment preference type.

[0034] The target person is a person for whom a financial bill installment plan is recommended. The target person holds a financial credit product and is a potential customer for the bill installment service. For example, the financial credit product held by the target person may be a credit card.

[0035] A financial services application refers to an application program that provides financial services. Alternatively, the financial services application may be mobile banking or online shopping software with financial services capabilities. The target user uses the bill installment service by operating the financial services application.

[0036] Optionally, by embedding points in the financial service application, the operation type data and browsing path data of the target object acting on the financial service application are obtained. Among them, the operation type data is related to the use of installment services. Optionally, the operation type data includes the call operation of the installment calculation component and the selection operation of the installment adjustment component. The operation type data can reflect the decision-making hesitation of the target object. The browsing path data is related to the page switching operation of the financial service application, and is used to describe the page switching order and page display time. The browsing path data can reflect the decision logic of the target object. For example, the browsing path data is "Bill page → Installment page → Handling fee description page", which indicates that the target object pays attention to the handling fee in the process of making installment decisions. The decision hesitation and decision logic of the target object can be used to determine the installment preference type to which the target object belongs.

[0037] The target's consumption behavior data is used to assess their recent spending power. Optionally, this data is time-sensitive and is determined based on their monthly spending and their average monthly spending over a preset historical period. Optionally, the preset historical period is six months. Solvency data is used to assess the target's repayment capacity and is a core indicator in risk rating. Optionally, this data is based on the target's historical overdue payments, consumer loan balances, and disposable income.

[0038] Historical installment data is determined based on the target individual's history of installment selections. This data can reflect the target individual's installment preferences and serve as a basis for personalized installment plan recommendations. Optionally, basic attribute data describes the target individual's economic stability and income sustainability. Exemplary basic attribute data includes age, occupation, and cardholder tenure.

[0039] Operation type data and browsing path data are related to the target user's decision hesitation and decision logic, respectively. Historical installment data can reflect the target user's installment preference. Operation type data, browsing path data, and historical installment data are all preferred dimensions for recommending financial bill installment plans.

[0040] Consumption behavior data, solvency data, and basic attribute data are all risk dimensions used in recommending financial bill installment plans. The target customer's installment preference type is determined based on operation type data, browsing path data, consumption behavior data, solvency data, historical installment data, and basic attribute data. This integrates the target customer's installment preference and installment risk, ensuring the accuracy of the installment preference type.

[0041] The installment preference type provides the data foundation for recommending financial bill installment plans for the target audience. Optional installment preference types include: rate-sensitive, risk-sensitive, and stable repayment.

[0042] Optionally, the number elements, rate elements, incentive elements and risk control elements of the financial bill installment plan are determined based on the installment preference type of the target object.

[0043] The technical solution of this application takes into account the target object's decision-making hesitation, decision-making logic, recent consumption ability, repayment qualifications, installment preferences and installment risks, among other preference factors that affect the target object's installment. Based on operation type data, browsing path data, consumption behavior data, solvency data, historical installment data and basic attribute data, the target object's installment preference type is determined from the risk dimension and preference dimension. Based on the target object's installment preference type, installment plans are recommended to the target object. This can improve the intelligence level of financial bill installment plan recommendations and the risk coverage of financial bill installment plans, and realize customized personalized financial bill installment plans for the target object.

[0044] In an optional embodiment, the operation type data and browsing path data of the target object acting on the financial services application are determined, including: if a call operation acting on the installment calculation component in the financial services application is detected, then counting the number of component calls of the installment calculation component; if a selection operation acting on the mid-term adjustment component of the financial services application is detected, then determining the frequency of the period modification; based on the number of component calls and the frequency of the period modification, determining the operation type data; if a page switching operation acting on the financial services application is detected, determining the page switching order and page display time associated with the page switching operation; based on the page switching order and the page display time, determining the browsing path data.

[0045] Optionally, the invocation of the installment calculation component and the selection of the installment adjustment component are detected by tracking points in the financial services application. Detecting the invocation of the installment calculation component or the selection of the installment adjustment component in the financial services application indicates that the target user is using the bill installment service provided by the financial services application. The installment calculation component helps the target user accurately plan financial bill installment repayments and can be used to calculate installment costs. The installment adjustment component is used to adjust the installment period.

[0046] The number of component calls is calculated by counting the number of times the installment calculation component is called. The frequency of installment modification is calculated by counting the number of times the installment adjustment component is selected. The number of component calls and the frequency of installment modification are positively correlated with the target object's decision-making hesitation: the greater the number of component calls and the higher the frequency of installment modification, the higher the target object's decision-making hesitation. Optionally, the number of component calls and the frequency of installment modification can be used as operation type data.

[0047] Detecting a page switching operation on a financial services application indicates that the target object is using the bill installment service provided by the financial services application, and determining the page switching order and page display time associated with the page switching operation. Exemplarily, the page display order can be "billing page → installment page → handling fee description page". The page display time refers to the time a single page stays. For example, the time each page stays on the billing page, the enterprise page, and the handling fee description page. The page display order and page display time can reflect the decision logic of the target object. The page switching order and page display time are used as browsing path data.

[0048] The above technical solution provides a feasible solution for determining operation type data and browsing path data, and provides data support for using operation type data and browsing path data to determine the installment preference type of the target object.

[0049] In an optional embodiment, the consumption behavior data and solvency data of the target object are determined, including: determining the consumption volatility of the target object based on the target object's current month consumption amount and the average monthly consumption amount in a preset historical period; determining the consumption behavior data of the target object based on the current month consumption amount, the average monthly consumption and the consumption volatility; determining the target object's repayment pressure index based on the target object's consumer loan bill and disposable income; determining the target object's solvency data based on the target object's historical number of overdue payments, the consumer loan bill, the disposable income and the repayment pressure index.

[0050] Among them, the monthly consumption amount can reflect the current consumption level of the target object. The average monthly consumption amount of the preset historical period can be the average monthly consumption amount of the past six months. The monthly consumption amount and the average monthly consumption amount of the preset historical period are used to determine the consumption volatility of the target object. Among them, the consumption volatility is used to reflect the consumption stability of the target object. Optionally, based on the average monthly consumption amount of the preset historical period, the standard deviation and average value corresponding to the preset historical period are determined. The quotient obtained by dividing the difference between the monthly consumption amount and the average value by the standard deviation is used as the consumption volatility of the target object. Optionally, the monthly consumption amount, average monthly consumption and consumption volatility are used as the consumption behavior data of the target object.

[0051] The target's consumer loan bills are used to determine their current debt. These bills and disposable income are used to determine their repayment stress index, which measures the degree to which their current debt matches their income / assets. Optionally, historical overdue payments, consumer loan bills, disposable income, and the repayment stress index can be used as solvency data.

[0052] The above technical solution provides a practical solution for determining consumer behavior data and solvency data, and provides data support for using consumer behavior data and solvency data to determine the installment preference type of the target object.

[0053] Example 2

[0054] Figure 2 This is a flow chart of a method for recommending a financial bill installment plan according to Example 2. This embodiment is further optimized based on the above embodiment.

[0055] like Figure 2 As shown, the method includes:

[0056] S210: Determine the operation type data and browsing path data of the target object acting on the financial service application.

[0057] S220: Determine the consumption behavior data, solvency data, historical installment data, and basic attribute data of the target object.

[0058] S230: Perform vectorization processing on the operation type data, the browsing path data, and the historical installment data to obtain the installment decision features of the target object.

[0059] The installment decision features are derived by vectorizing operation type data, browsing path data, and historical installment data. These data are all related to the target's installment decision. Operation type data can reflect the target's decision hesitation, browsing path data can reflect the target's decision preference, and historical installment data can reflect the target's decision preference.

[0060] Among them, the installment decision features serve as the input data of the installment preference identification model to determine the installment preference type of the target object.

[0061] S240: Perform vectorization processing on the consumption behavior data, the solvency data, and the basic attribute data to obtain the installment risk characteristics of the target object.

[0062] The installment risk profile is derived by quantizing the target's consumption behavior data, solvency data, and basic attribute data. These data are related to the target's installment risk. Consumption behavior data is used to assess the target's near-term spending power. Solvency data is used to assess the target's repayment capacity and is a core indicator for risk rating. Basic attribute data describes the target's economic stability and income sustainability.

[0063] Among them, the stage risk characteristics serve as the input data of the stage preference identification model to determine the stage preference type to which the target object belongs.

[0064] S250: Input the staging decision features and the staging risk features into a pre-trained staging preference recognition model.

[0065] S260: Outputting the installment preference type to which the target object belongs through the installment preference identification model.

[0066] The installment preference recognition model is pre-trained. This model uses the installment decision-making characteristics and installment risk characteristics of sample subjects as training samples for supervised training. Training samples can be labeled manually. Optionally, the sample labels include rate-sensitive, risk-sensitive, and stable repayment.

[0067] The installment decision characteristics and installment risk characteristics are input into the installment preference identification model, and the installment preference identification model outputs the installment preference type to which the target object belongs.

[0068] S270: Recommend a financial bill installment plan to the target object based on the installment preference type.

[0069] Optionally, the number elements, rate elements, incentive elements and risk control elements of the financial bill installment plan are determined based on the installment preference type of the target object.

[0070] The technical solution of the present application pre-trains an installment preference recognition model, and outputs the installment preference type of the target object based on the installment decision characteristics and installment risk characteristics through the installment preference recognition model, thereby realizing installment preference classification of the target object from the risk dimension and preference dimension, and recommending installment plans to the target object based on the installment preference type of the target object. This can improve the intelligence level of the recommendation of financial bill installment plans and the risk coverage rate of the financial bill installment plans, and realize customization of personalized financial bill installment plans for the target object.

[0071] In an optional embodiment, the installment preference types include: rate-sensitive, risk-sensitive, and stable repayment. The rate-sensitive type refers to a user group that is highly sensitive to changes in various fees on financial bills, such as annual fees, installment rates, or liquidated damages. The risk-sensitive type refers to a user group with a high risk of overdue payments. The stable repayment type refers to a user group with a low risk of overdue payments due to consistent and stable income and good repayment habits.

[0072] The above technical solution, by dividing installment preferences into rate-sensitive, risk-sensitive and stable repayment types, recommends financial bill installment plans for target objects based on installment preference types, thereby improving the matching degree between target objects and financial bill installment plans and improving the accuracy of financial bill installment plan recommendations.

[0073] In an optional embodiment, the financial bill installment plan is recommended to the target object based on the installment preference type, including: if the installment preference type is the stable repayment type, a financial bill installment plan with a medium- to long-term installment element and associated with an incentive element is recommended to the target object; if the installment preference type is the risk-sensitive type, a financial bill installment plan with an associated risk control element is recommended to the target object; if the installment preference type is the rate-sensitive type, a financial bill installment plan with a low rate element and an ultra-short-term installment element is recommended to the target object.

[0074] A financial bill installment plan includes at least the number of installments and the rate element, and may also include incentive elements or risk control elements. The number of installments defines the number of installments, while the rate element defines the installment rate. These elements are fundamental to a financial bill installment plan. Incentive elements and risk control elements are specialized elements within a financial bill installment plan. Incentive elements reward on-time repayments, while risk control elements manage the risk of overdue payments.

[0075] If the target object is a stable repayment type, a financial bill installment plan with a medium- to long-term installment element and associated incentive elements will be recommended to the target object. For example, the incentive element can be a fee refund for early repayment or an exclusive financial management channel. If the target object is risk-sensitive, a financial bill installment plan with associated risk control elements will be recommended to the target object. For example, the risk control element can be a guaranteed installment or an installment cancellation option set for the target object within a preset time to control the overdue risk caused by impulsive consumption. If the target object is rate-sensitive, a financial bill installment plan with a low rate element and an ultra-short-term installment element will be recommended to the target object.

[0076] In the above technical solution, by setting the installment number, rate, incentive, and risk control factors in the recommended financial bill installment plan, and then determining the installment number, rate, incentive, and risk control factors based on the installment preference type of the target person, the financial bill installment plan is recommended for the target person, ensuring the accuracy of the recommended financial bill installment plan.

[0077] In an optional embodiment, the method further includes: obtaining feedback results from the target object regarding the financial bill installment plan; and based on the feedback results, adjusting at least one of the rate element, number element, incentive element and risk control element in the financial bill installment plan.

[0078] The feedback result reflects the target audience's satisfaction with the recommended financial bill installment plan. Optionally, the feedback result includes acceptance and rejection. The feedback result may also be associated with feedback opinions.

[0079] Optionally, when the feedback result is rejection, at least one of the rate element, number of installments element, incentive element and risk control element in the financial bill installment plan is adjusted based on the feedback opinions associated with the feedback result.

[0080] The above technical solution adjusts at least one of the rate element, installment number element, incentive element, and risk control element of the financial bill installment plan based on the target user's feedback on the financial bill installment plan. This helps improve the matching between the financial bill installment plan and the target user, and helps improve the accuracy of financial bill installment plan recommendations.

[0081] Example 3

[0082] Figure 3 This is a structural diagram of the financial bill installment plan recommendation device provided in Example 3 of the present application. This embodiment can be applied to situations where financial bill installment plans, such as credit card installment plans, are recommended. The device can be implemented by software and / or hardware and can be integrated into electronic devices such as smart terminals.

[0083] like Figure 3 As shown, the financial bill installment plan recommendation device 300 may include:

[0084] A first data determination module 310 is configured to determine operation type data and browsing path data of a target object acting on a financial service application;

[0085] The second data determination module 320 is used to determine the consumption behavior data, solvency data, historical installment data and basic attribute data of the target object;

[0086] a preference type determination module 330 for determining the installment preference type of the target object based on the operation type data, the browsing path data, the consumption behavior data, the solvency data, the historical installment data, and the basic attribute data;

[0087] The installment plan recommendation module 340 is configured to recommend a financial bill installment plan to the target object based on the installment preference type.

[0088] The technical solution of this application takes into account the target object's decision-making hesitation, decision-making logic, recent consumption ability, repayment qualifications, installment preferences and installment risks, among other preference factors that affect the target object's installment. Based on operation type data, browsing path data, consumption behavior data, solvency data, historical installment data and basic attribute data, the target object's installment preference type is determined from the risk dimension and preference dimension. Based on the target object's installment preference type, installment plans are recommended to the target object. This can improve the intelligence level of financial bill installment plan recommendations and the risk coverage of financial bill installment plans, and realize customized personalized financial bill installment plans for the target object.

[0089] Optionally, the first data determination module 310 includes: a call count determination submodule, which is used to count the component call count of the installment calculation component if a call operation acting on the installment calculation component in the financial service application is detected; a modification frequency determination submodule, which is used to determine the installment modification frequency if a selection operation acting on the installment adjustment component in the financial service application is detected; an operation type determination submodule, which is used to determine the operation type data based on the component call count and the installment modification frequency; a page data determination submodule, which is used to determine the page switching order and page display time associated with the page switching operation if a page switching operation acting on the financial service application is detected; and a browsing path determination submodule, which is used to determine the browsing path data based on the page switching order and the page display time.

[0090] Optionally, the second data determination module 320 includes: a consumption volatility determination submodule, which is used to determine the consumption volatility of the target object based on the target object's current month consumption amount and the average monthly consumption amount in a preset historical period; a consumption behavior data determination submodule, which is used to determine the consumption behavior data of the target object based on the current month consumption amount, the average monthly consumption and the consumption volatility; a repayment pressure index determination submodule, which is used to determine the repayment pressure index of the target object based on the target object's consumer loan bill and disposable income; and a repayment ability determination submodule, which is used to determine the target object's repayment ability data based on the target object's historical number of overdue payments, the consumer loan bill, the disposable income and the repayment pressure index.

[0091] Optionally, the preference type determination module 330 includes: a decision feature determination submodule, which is used to perform vectorized processing on the operation type data, the browsing path data and the historical installment data to obtain the installment decision features of the target object; a risk feature determination submodule, which is used to perform vectorized processing on the consumption behavior data, the solvency data and the basic attribute data to obtain the installment risk features of the target object; a risk feature input submodule, which is used to input the installment decision features and the installment risk features into a pre-trained installment preference recognition model; and a preference type determination submodule, which is used to output the installment preference type to which the target object belongs through the installment preference recognition model.

[0092] Optionally, the installment preference types include: rate-sensitive, risk-sensitive and stable repayment.

[0093] Optionally, the installment plan recommendation module 340 includes: a first plan recommendation sub-module, which is used to recommend a financial bill installment plan with a medium- to long-term installment element and an associated incentive element to the target object if the installment preference type is the stable repayment type; a second plan recommendation sub-module, which is used to recommend a financial bill installment plan with an associated risk control element to the target object if the installment preference type is the risk-sensitive type; and a third plan recommendation sub-module, which is used to recommend a financial bill installment plan with a low rate element and an ultra-short-term installment element to the target object if the installment preference type is the rate-sensitive type.

[0094] Optionally, the device also includes: a feedback result acquisition module, used to obtain the feedback result of the target object regarding the financial bill installment plan; an installment plan adjustment module, used to adjust at least one of the rate element, installment number element, incentive element and risk control element in the financial bill installment plan based on the feedback result.

[0095] The financial bill installment plan recommendation device provided in the embodiment of the invention can execute the financial bill installment plan recommendation method provided in any embodiment of the present application, and has the corresponding performance modules and beneficial effects for executing the financial bill installment plan recommendation method.

[0096] In the technical solution of this application, the user data of the target object, such as operation type data, browsing path data, consumption behavior data, solvency data, historical installment data and basic attribute data, are information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with the relevant laws, regulations and standards of the relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0097] Example 4

[0098] According to embodiments of the present application, the present application also provides an electronic device, a readable storage medium, and a computer program product.

[0099] Figure 4 The structure diagram of the electronic device 410 that can be used to implement the embodiment is shown. The electronic device 410 includes at least one processor 411, and a memory connected to the at least one processor 411 in communication, such as a read-only memory (ROM) 412, a random access memory (RAM) 413, etc., wherein the memory stores a computer program that can be executed by the at least one processor, and the processor 411 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 412 or the computer program loaded from the storage unit 418 into the random access memory (RAM) 413. Various programs and data required for the operation of the electronic device 410 can also be stored in the RAM 413. The processor 411, ROM 412 and RAM 413 are connected to each other via a bus 414. An input / output (I / O) interface 415 is also connected to the bus 414.

[0100] Multiple components in electronic device 410 are connected to I / O interface 415, including an input unit 416, such as a keyboard, mouse, etc.; an output unit 417, such as various types of displays, speakers, etc.; a storage unit 418, such as a magnetic disk, optical disk, etc.; and a communication unit 419, such as a network card, modem, wireless communication transceiver, etc. The communication unit 419 allows electronic device 410 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0101] Processor 411 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of processor 411 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any other suitable processors, controllers, microcontrollers, etc. Processor 411 executes the various methods and processes described above, such as the method for recommending financial bill installment plans.

[0102] In some embodiments, the financial bill installment plan recommendation method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 418. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 410 via ROM 412 and / or communication unit 419. When the computer program is loaded into RAM 413 and executed by processor 411, one or more steps of the financial bill installment plan recommendation method described above can be performed. Alternatively, in other embodiments, processor 411 can be configured to execute the financial bill installment plan recommendation method in any other appropriate manner (e.g., via firmware).

[0103] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0104] Computer programs for implementing the methods of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable financial bill installment plan recommendation device, such that when executed by the processor, the computer programs implement the functions / operations specified in the flowcharts and / or block diagrams. The computer programs can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0105] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. A computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0106] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0107] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a financial bill installment plan recommendation server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with embodiments of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0108] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0109] The present application also discloses a computer program product comprising a computer program that, when executed by a processor, implements the method for recommending financial bill installment plans provided in any of the embodiments of the present application. This program product shares the same inventive concept as the method for recommending financial bill installment plans disclosed in each embodiment of the present application and, therefore, is not further described here.

[0110] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this application can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of this application can be achieved. This is not limited herein.

[0111] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A method for recommending a financial bill installment plan, characterized in that: The method comprises: Determine the target object's operation type data and browsing path data on the financial services application; Determine the target object's consumption behavior data, solvency data, historical installment data, and basic attribute data; Determining the installment preference type of the target object based on the operation type data, the browsing path data, the consumption behavior data, the solvency data, the historical installment data, and the basic attribute data; Recommend a financial bill installment plan to the target object based on the installment preference type.

2. The method according to claim 1, characterized in that The determining of the operation type data and browsing path data of the target object acting on the financial service application includes: If a call operation acting on the installment calculation component in the financial service application is detected, counting the number of component calls of the installment calculation component; If a selection operation is detected on the interim number adjustment component of the financial services application, determining the frequency of the period number modification; determining the operation type data based on the number of component calls and the frequency of phase modification; If a page switching operation acting on the financial services application is detected, determining a page switching sequence and a page display time associated with the page switching operation; The browsing path data is determined based on the page switching sequence and the page display time.

3. The method according to claim 1, characterized in that Determine the target's consumer behavior data and solvency data, including: Determine the consumption volatility of the target object based on the target object's monthly consumption amount and the average monthly consumption amount in a preset historical period; Determining the consumption behavior data of the target object based on the monthly consumption amount, the average monthly consumption, and the consumption volatility; determining a repayment stress index of the target subject based on the target subject's consumer loan bill and disposable income; The target object's solvency data is determined based on the target object's historical overdue times, the consumer loan bill, the disposable income, and the solvency stress index.

4. The method according to claim 1, wherein The determining of the installment preference type of the target object based on the operation type data, the browsing path data, the consumption behavior data, the solvency data, the historical installment data, and the basic attribute data includes: Performing vectorization processing on the operation type data, the browsing path data, and the historical installment data to obtain the installment decision feature of the target object; Performing vectorization processing on the consumption behavior data, the solvency data, and the basic attribute data to obtain the installment risk characteristics of the target object; Inputting the staging decision features and the staging risk features into a pre-trained staging preference recognition model; The installment preference type to which the target object belongs is outputted through the installment preference identification model.

5. The method according to any one of claims 1 to 4, characterized in that The installment preference types include: rate-sensitive, risk-sensitive and stable repayment.

6. The method according to claim 5, characterized in that The step of recommending a financial bill installment plan to the target object based on the installment preference type includes: If the installment preference type is the stable repayment type, a financial bill installment plan with a mid- to long-term installment element and associated incentive elements is recommended to the target object; If the installment preference type is the risk-sensitive type, recommending a financial bill installment plan associated with risk control factors to the target object; If the installment preference type is the rate-sensitive type, a financial bill installment plan with a low rate element and an ultra-short term element is recommended to the target object.

7. The method according to claim 1, characterized in that The method further comprises: Obtaining feedback from the target object regarding the financial bill installment plan; Based on the feedback result, at least one of the rate element, the number of installments element, the incentive element and the risk control element in the financial bill installment plan is adjusted.

8. A financial bill installment plan recommendation device, characterized in that: The device comprises: A first data determination module is used to determine operation type data and browsing path data of a target object acting on a financial service application; A second data determination module is used to determine the consumption behavior data, solvency data, historical installment data and basic attribute data of the target object; a preference type determination module, configured to determine the installment preference type to which the target object belongs based on the operation type data, the browsing path data, the consumption behavior data, the solvency data, the historical installment data, and the basic attribute data; The installment plan recommendation module is used to recommend a financial bill installment plan for the target object based on the installment preference type.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for recommending a financial bill installment plan according to any one of claims 1 to 7 is implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein: When the processor executes the computer program, the method for recommending a financial bill installment plan according to any one of claims 1 to 7 is implemented.

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