Financial transaction data management system and method

Abstract

The invention discloses a financial transaction data management system and method, belongs to the technical field of financial transaction data management, and aims to realize financial transaction data management of individual users and mine potential values of individual financial transaction data. Comprising a data acquisition module, a transaction statistics module and a transaction analysis module. The data acquisition module is used for acquiring financial transaction data of a user in real time; the transaction statistical module is used for performing statistical analysis on the financial transaction data to generate a transaction statistical table; performing safety management on the financial transaction data according to the transaction statistical table; the transaction analysis module is used for carrying out transaction analysis, obtaining personal application requirements of a user, determining personal application data according to the personal application requirements, and docking and inputting the personal application data into a target application; through mutual cooperation of the data acquisition module, the transaction statistics module and the transaction analysis module, intelligent management of financial transaction data of individual users is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of financial transaction data management, and in particular relates to a financial transaction data management system and method. Background Art

[0002] In today's digital age, the management of personal financial transaction data has become increasingly important. With the popularization of financial services such as e-commerce, online banking and mobile payments, individuals need to frequently conduct financial transactions, such as transfers, payments, investments, taxation, pension consumption and savings. The data generated by these transactions are of great value to individuals, because they not only record their financial status, but also reflect important information such as their consumption habits, investment preferences and credit status; however, the current utilization rate of personal financial transaction data is not high; because the existing financial transaction data management system has deficiencies in many aspects and cannot meet the needs of individual users, the current financial transaction data management system is mainly aimed at enterprises, financial institutions and other entities, and rarely involves a large number of ordinary individual users, resulting in many individual users not managing their personal financial transaction data.

[0003] Therefore, in order to realize the financial transaction data management of individual users and tap the potential value of personal financial transaction data, the present invention provides a financial transaction data management system and method. Summary of the invention

[0004] In order to solve the problems existing in the above scheme, the present invention provides a financial transaction data management system and method.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A financial transaction data management system, comprising a data collection module, a transaction statistics module and a transaction analysis module;

[0007] The data collection module is used to collect the user's financial transaction data in real time.

[0008] Furthermore, the method for collecting financial transaction data includes:

[0009] The platform establishes a collection preparation table, which is used to count the collection items corresponding to each type of financial transaction and the data collection method and sensitivity description corresponding to each collection item. The sensitivity description is used to explain the sensitivity of the data collection method to the user;

[0010] Determine a basic collection method, wherein the basic collection method is that the user inputs the corresponding financial transaction data by himself;

[0011] The user collects the user's historical financial transaction data according to the basic collection method, identifies the financial transaction type corresponding to the historical financial transaction data, and sets the corresponding target collection item according to the financial transaction type;

[0012] Input the target collection item into the collection preparation table for matching, obtain the data collection method and sensitive description corresponding to the target collection item, and integrate them into the collection description data of the target collection item;

[0013] The collection description data is displayed to the user, and the user decides whether to apply the corresponding data collection method according to the displayed collection description data;

[0014] When the user selects to apply the corresponding data collection method, it is deemed that the user authorizes the use of the data collection method, and marks the target collection item corresponding to the data collection method as the application collection item; and marks the remaining target collection items as the basic collection items;

[0015] Integrate the data collection method corresponding to the target collection item and the basic collection method corresponding to the basic collection item into a transaction collection method;

[0016] The user's financial transaction data is collected in real time according to the transaction collection method.

[0017] Furthermore, the transaction collection method is optimized and analyzed according to the collection records of the transaction collection method to obtain optimization analysis results, which are displayed to the user, and the user decides whether to optimize the transaction collection method.

[0018] Furthermore, the method for optimizing and analyzing the collected records of the transaction collection method includes:

[0019] Identify the basic collection items corresponding to the transaction collection method, extract data from the collection records according to the basic collection items, and obtain the data collection volume and missed collection rate corresponding to the basic collection items, where the data collection volume is the average number of times the user collects financial transaction data of the basic collection items within a preset time; and the missed collection rate is the probability that the user forgets to collect the financial transaction data of the basic collection items;

[0020] Estimate the user's collection time according to the data collection amount; calculate the optimization value corresponding to the basic collection item according to the optimization formula; the optimization formula is:

[0021]

[0022] Where: PA is the optimized value; CT is the acquisition time; η is the missed acquisition rate; PA 0 is a preset representative value, which is used to indicate that the leakage rate is 100%;

[0023] An optimization statistical table is generated according to the optimization values ​​of the basic collection items, and the optimization statistical table is marked as an optimization analysis result.

[0024] The transaction statistics module is used to perform statistical analysis on the collected financial transaction data, generate a transaction statistics table, and perform security management on the financial transaction data based on the transaction statistics table.

[0025] Furthermore, the method for statistically analyzing financial transaction data includes:

[0026] The platform presets the corresponding transaction statistics table template, which is marked as the initial statistics template;

[0027] The initial statistical template is displayed to the user, and the user adjusts the initial statistical template according to personalized needs, and the adjusted initial statistical template is marked as an application statistical template;

[0028] The financial transaction data is counted according to the application statistics template to obtain a transaction statistics table.

[0029] Furthermore, the method for securely managing financial transaction data based on the transaction statistics table includes:

[0030] Identify the application collection items, obtain the financial transaction data information corresponding to the application collection items, classify the financial transaction data according to the financial transaction data information and the transaction statistics table, and obtain transaction classification data;

[0031] Obtaining a user's deletion request for the transaction classification data, and performing deletion management on the transaction classification data according to the deletion request;

[0032] The remaining transaction classification data are integrated into the data to be encrypted, and the data to be encrypted is encrypted according to a preset encryption method.

[0033] Furthermore, a missed collection analysis is performed based on the transaction statistics table to determine the missed basic collection items and prompt the user.

[0034] Furthermore, the method for performing missed collection analysis based on the transaction statistics table includes:

[0035] The platform party presets the potential transaction type; identifies the existing financial transaction data according to the transaction statistics table, identifies the transaction type and the corresponding transaction time corresponding to the financial transaction data, matches the corresponding potential transaction type to the transaction time according to the transaction type, sorts the transaction time according to the corresponding potential transaction type, and obtains a transaction time set corresponding to the potential transaction type;

[0036] Generating a periodicity standard of the potential transaction type according to the transaction time set; marking the potential transaction type with the periodicity standard as a transaction periodicity type;

[0037] Conduct real-time analysis of the transaction statistics table based on the transaction cycle type and the corresponding periodic standards, identify the transaction cycle types that do not meet the periodic standards, and determine the missed basic collection items.

[0038] Furthermore, the method of generating a periodic standard of a potential type of transaction based on a transaction time set includes:

[0039] Step SA1: Setting a cycle scale, wherein the initial cycle length of the cycle scale is one day; each adjustment of the cycle scale will add one day to the original cycle length;

[0040] Step SA2: Identify the transaction time set according to the cycle scale, obtain the transaction volume corresponding to each cycle duration, and generate a scale curve according to the transaction volume of each cycle duration, wherein the horizontal axis of the scale curve is time and the vertical axis is transaction volume;

[0041] Step SA3: judging whether the transaction time set meets the periodicity requirement according to the scale curve;

[0042] When it is judged that the periodicity requirements are met, the periodicity criteria are determined;

[0043] When it is determined that the periodicity requirement is not met, the cycle length of the cycle scale is identified. When the cycle length is not greater than one month, return to step SA2; when the cycle length is greater than one month, it is evaluated that the transaction time set has no periodicity standard.

[0044] Furthermore, the method for judging whether the transaction time set meets the periodicity requirement according to the scale curve includes:

[0045] Acquire the user's association features with the potential transaction type, and adjust the scale curve according to the association features;

[0046] Fitting the scaling curve to obtain a scaling function, marking the scaling function as CD(t), where t is time; generating an initial function according to the scaling function, marking the initial function as PB(t+1);

[0047] PB(t+1)=|CD(t+1)-CD(t)|;

[0048] Calculate the corresponding cycle deviation value according to the cycle formula; the cycle formula is:

[0049]

[0050] Where: PC is the period deviation value;

[0051] Whether the transaction time set meets the periodicity requirement is determined according to the periodic deviation value.

[0052] The transaction analysis module is used to perform transaction analysis, obtain the user's personal application needs, determine the target application according to the personal application needs; determine personal application data according to the personal application needs and the target application, and connect and input the personal application data into the target application.

[0053] A financial transaction data management method, the method comprising:

[0054] Collecting financial transaction data of users in real time; performing statistical analysis on the financial transaction data, generating a transaction statistics table, and performing security management on the financial transaction data according to the transaction statistics table;

[0055] Obtain the user's personal application needs, and determine the target application according to the personal application needs; determine personal application data according to the personal application needs and the target application, and connect and input the personal application data into the target application.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] The present invention realizes intelligent management of the financial transaction data of numerous individual users through the mutual cooperation among the data acquisition module, the transaction statistics module and the transaction analysis module, so as to facilitate individual users to quickly understand the detailed transaction situation, and at the same time, to perform safe management of the financial transaction data, effectively protect the personal data and transaction information of the users, avoid the risks such as data leakage and identity theft, thereby enhancing the users' trust in the system and sense of security in use; and with the use of individual users, when the users have personal application needs, it is convenient to make full use of the accumulated financial transaction data, tap the potential value of the personal financial transaction data, and realize scientific management. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0059] Figure 1 This is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0060] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] like Figure 1 As shown, a financial transaction data management system includes a data collection module, a transaction statistics module and a transaction analysis module;

[0062] The data collection module is used to collect the user's financial transaction data in real time.

[0063] In one embodiment, the financial transaction data of users may be collected by manual input, that is, after the user generates a financial transaction, the user may record the corresponding financial transaction data by manual input or language input, etc.; that is, the data may be collected according to the existing financial transaction data collection method for individual users.

[0064] In one embodiment, the existing method for collecting financial transaction data has certain limitations in practical applications. It takes time and energy for users to enter financial transaction data, so that users need to enter financial transaction data in a timely manner. However, in reality, users often forget to enter financial transaction data, and as time goes by, users are prone to stop using the system. Therefore, in this embodiment, the following method is proposed to collect users' financial transaction data:

[0065] The platform determines the types of financial transactions that the user may have. It can conduct statistical analysis on the individual's historical financial transaction data based on existing big data technology to determine the various types of financial transactions, such as consumer payments based on a certain payment software, shopping on a certain e-commerce platform, transfers through a certain bank software, consumer expenditures for retirement, etc. Consumer payments made through the same payment software, although the consumption amounts are different, are regarded as the same type of financial transaction, which is used to subsequently determine the automatic collection method for the type of financial transaction. For example, a transaction SMS will be issued after a certain type of financial transaction, and the financial transaction information will be collected by identifying the SMS. In actual applications, the platform can generally directly set the types of financial transactions manually; the platform sets Regarding the collection items, data collection methods and sensitive descriptions of each type of financial transaction, a type of financial transaction can have multiple data collection methods based on the actual situation of existing collection technologies, such as financial transaction pop-up information identification and collection, access software collection, etc., and the specific settings need to be made based on the actual situation of the financial transaction type; the sensitive description is set for the data collection method to indicate the collection of sensitive information of the user by the data collection method. It is necessary to provide detailed descriptions based on the data that may be collected by the data collection method and the subsequent processing of the collected data to avoid infringing on user privacy and ensure that all data collection is carried out with user authorization; at the same time, the sensitive description can also include collection examples corresponding to the data collection method to facilitate users' intuitive understanding.

[0066] A collection preparation table is established according to each collection item and the corresponding data collection method and sensitive description of each collection item. One collection item corresponds to one type of financial transaction. That is, the collection preparation table is established by the platform and can also be set up by the platform's professionals. The collection preparation table is used to count each collection item and the corresponding data collection method and sensitive description of each collection item.

[0067] The method of collecting financial transaction data according to the existing method in the above embodiment is marked as a basic collection method, that is, the basic collection method is that the user inputs the corresponding financial transaction data by himself.

[0068] The user collects historical financial transaction data according to the basic collection method, identifies the financial transaction type corresponding to the historical financial transaction data, and sets the corresponding target collection items according to the identified financial transaction type;

[0069] Input the target collection item into the collection preparation table for matching, obtain the data collection method and sensitive description corresponding to the target collection item, and integrate them into the collection description data of the target collection item;

[0070] Display each collection description data to the user, and the user decides whether to apply the corresponding data collection method based on the collection description data of each target collection item. When the user chooses to apply the corresponding data collection method, it is deemed that the user authorizes the use of the data collection method, and marks the target collection item corresponding to the data collection method as an applied collection item; and marks the remaining target collection items as basic collection items;

[0071] Determine the transaction collection method according to the data collection method corresponding to the target collection item and the basic collection method corresponding to the basic collection item, the transaction collection method being a combination of the data collection method and the basic collection method;

[0072] Collect users' financial transaction data in real time according to the transaction collection method.

[0073] In one embodiment, with the application of the transaction collection method, it may be possible to more clearly analyze for the user the changes in the collection efficiency of each basic collection item applying the corresponding data collection method, and then assist the user in selecting a suitable data collection method to convert the basic collection item into an application collection item; that is, the transaction collection method is optimized and analyzed according to the collection record of the transaction collection method, and the optimization analysis result is obtained, and the optimization analysis result is displayed to the user, and the user decides whether to optimize the transaction collection method, that is, whether to replace the corresponding data collection method.

[0074] In one embodiment, the transaction collection method is optimized and analyzed according to the collection records of the transaction collection method. The optimization analysis can be performed based on existing optimization evaluation methods, such as establishing an intelligent model from the perspective of collection efficiency and performing optimization analysis through the intelligent model.

[0075] In one embodiment, a method for optimizing and analyzing a transaction collection method based on a collection record of the transaction collection method includes:

[0076] Identify the basic collection items corresponding to the transaction collection method, determine according to the corresponding basic collection method, extract data from the collection records according to the basic collection items, and obtain the data collection volume and missed collection rate corresponding to each basic collection item. The data collection volume is the average number of times the user inputs financial transaction data within the preset time. The preset time is generally one week, and can also be adjusted to two weeks, one month, etc. The initial one is one week, and the user can adjust it later, including the number of financial transaction data supplemented due to missed collection; the missed collection rate is the probability that the user forgets to record and enter, which is statistically calculated based on the later supplementary situation, or estimated in combination with historical collection data;

[0077] Estimate the user's collection time based on the amount of data collected. The collection time is the time it takes for the user to enter the financial transaction data of the above-mentioned data collection amount. If there is a recording function, statistics can be directly made based on the entry time of each financial transaction data, or the corresponding average time can be calculated and accumulated;

[0078] Calculate the optimization value corresponding to the basic collection item according to the optimization formula; the optimization formula is:

[0079]

[0080] Where: PA is the optimized value; CT is the acquisition time; η is the missed acquisition rate; PA 0 It is a preset representative value, which can be a specific value or a character, used to indicate that the missed sampling rate is 100%;

[0081] An optimization statistics table is generated according to the optimization value of each basic collection item, and the optimization statistics table is marked as an optimization analysis result. The user can then determine whether to optimize the corresponding basic collection item according to needs.

[0082] The transaction statistics module is used to perform statistical analysis on the collected financial transaction data, generate a transaction statistics table, and perform security management on the financial transaction data based on the obtained transaction statistics table.

[0083] In one embodiment, the transaction statistics table may be generated based on an existing data statistics method.

[0084] In one embodiment, because it is for individual users, and many individual users have personalized statistical needs, in this embodiment, the method for statistical analysis of financial transaction data includes:

[0085] The platform presets a corresponding transaction statistics table template, which is marked as an initial statistics template, that is, in the previous embodiment, data statistics analysis is performed according to the initial statistics template;

[0086] The initial statistical template is displayed to the user, and the user adjusts the initial statistical template according to personalized needs, such as adding new statistical items and corresponding statistical rules, adjusting the position of statistical items, adjusting the size of the table, adding a favorite picture background, etc.; the adjusted initial statistical template is marked as an applied statistical template;

[0087] Financial transaction data is counted according to the application statistics template to obtain a transaction statistics table; if the corresponding data is identified and added to the table in the corresponding application statistics template, statistics are subsequently performed based on the corresponding statistical rules.

[0088] In one embodiment, financial transaction data security management is performed based on the transaction statistics table, that is, data is encrypted according to an encryption algorithm preset by the platform to avoid data leakage, such as encrypting the transaction statistics table.

[0089] In one embodiment, because the financial transaction data of the present invention is collected by using the data collection method corresponding to the application collection item, it is also possible to achieve unified management of the corresponding financial transaction data, such as data deletion. In summary, the method for secure management of financial transaction data based on the transaction statistics table in this embodiment includes:

[0090] Identify the application collection items, obtain the financial transaction data information corresponding to the application collection items, and the financial transaction data information includes the storage location, data size, source, transaction type and other related information of the financial transaction data; classify the financial transaction data according to the financial transaction data information and the transaction statistics table to obtain the classification data of each transaction, that is, classify according to the corresponding categories in the transaction statistics table, or classify in combination with existing conventional classifications, such as pension, finance and taxation, consumption, savings and other major categories, and can also be classified in detail;

[0091] Obtain the user's deletion requirements for each transaction classification data, that is, whether the user needs to delete it, and perform deletion management on the corresponding transaction classification data according to the obtained deletion requirements, that is, delete the transaction classification data that needs to be deleted. If some financial transaction data cannot be directly deleted through this system, the user needs to delete it manually;

[0092] The remaining transaction classification data are integrated into the data to be encrypted, and the encrypted data is encrypted according to the preset encryption method. The encryption method is preset by the platform.

[0093] For the financial transaction data corresponding to the basic collection items, there is no permission to access and manage the record data generated in mobile phones and other devices, so users need to manage it themselves.

[0094] In one embodiment, based on the continuous accumulation of transaction statistics, the user's fixed transaction items can be understood, such as daily fixed consumption of meals, subway consumption, salary payment, fixed pension expenses, fiscal and tax expenditures, etc., and then it can be estimated whether the user has missed financial transaction data; that is, a missed collection analysis is performed based on the transaction statistics, the missed basic collection items are determined, and prompts are given to the user.

[0095] In one embodiment, the missed collection analysis based on the transaction statistics table can be performed based on existing methods to determine the missed basic collection items.

[0096] In one embodiment, the method for performing missed acquisition analysis based on the transaction statistics table includes:

[0097] The platform presets transaction types with periodicity and marks them as potential transaction types. Potential transaction types are transaction types of financial transactions with certain periodicity, such as dining, public transportation, charging, fixed pension expenses, fiscal and tax expenditures, etc.

[0098] Identify the existing financial transaction data according to the transaction statistics table. The identified data is the financial transaction data of a recent period of time, generally one month, one week, etc., to reduce the amount of data analysis, and the recent data is more representative; identify the transaction type and the corresponding transaction time corresponding to each financial transaction data, match the corresponding transaction potential type according to the transaction type and the transaction time of the financial transaction data, obtain the transaction time set corresponding to the transaction potential type, and generate the periodicity standard of the transaction potential type according to the transaction time set; mark the transaction potential type with the periodicity standard as the transaction cycle type;

[0099] Analyze the transaction statistics table according to each transaction cycle type and the corresponding periodicity standard to determine whether there are any omissions, that is, whether the corresponding periodicity standard is not met; and then determine the basic collection items corresponding to the transaction cycle type.

[0100] In one embodiment, a method for generating a periodicity standard for a potential type of transaction based on a transaction time set includes:

[0101] Step SA1: Set the cycle scale. The initial duration of the cycle scale is one day. Each adjustment of the cycle scale will add one day to the original cycle duration. That is, the initial duration is one day. After one adjustment, the cycle duration will be two days, and so on.

[0102] Step SA2: Identify the trading time set according to the cycle scale, obtain the trading volume corresponding to each cycle duration, that is, the number of trading times, and generate a scale curve according to the trading volume of each cycle duration. The horizontal axis of the scale curve is time; the vertical axis is trading volume; adjacent points are connected by a smooth curve;

[0103] Step SA3: judging whether the transaction time set meets the periodicity requirement according to the scale curve;

[0104] When it is judged that the periodicity requirements are met, the periodicity criteria are determined;

[0105] When it is determined that the periodicity requirement is not met, the cycle length of the cycle scale is identified. When the cycle length is not greater than one month, return to step SA2, and one month is determined according to the number of days in the month. For example, if December has 31 days, the cycle length is not greater than 31 days. When the cycle length is greater than one month, it is evaluated that the transaction time set has no periodicity standard.

[0106] In one embodiment, a method for determining whether a transaction time set meets the periodicity requirement according to a scale curve includes:

[0107] Obtain the user's associated features for the potential transaction type, mainly for the potential transaction types affected by differences such as working days and holidays. If affected by differences such as working days and holidays, the associated feature is the corresponding user's working time, such as two days off a week or one day off a week. If not affected, the associated feature is none. Adjust the scale curve according to the associated features, remove its data, and do not set the periodic standard corresponding to its time in the future.

[0108] Fit the scaling curve to obtain the scaling function, which is marked as CD(t), where t is time; generate an initial function based on the scaling function, which is PB(t+1);

[0109] PB(t+1)=|CD(t+1)-CD(t)|;

[0110] Calculate the corresponding cycle deviation value according to the cycle formula; the cycle formula is:

[0111]

[0112] Where: PC is the period deviation value;

[0113] Judging whether the transaction time set meets the periodicity requirement according to the obtained cycle deviation value, that is, the cycle deviation value cannot be greater than the preset value. The platform sets the preset value of each potential transaction type according to the analysis of each potential transaction type, and the preset value is set by technicians in this field according to the actual situation or obtained by a large amount of data simulation, such as specifying several transaction time sets that meet the periodicity requirement, calculating the cycle deviation value of each transaction time set according to the above method, and taking the largest cycle deviation value as the preset value;

[0114] The corresponding periodicity standards can be determined based on the periodic formula, such as the absolute value of the difference cannot be greater than a certain value, the periodic deviation value must not be greater than a preset value, etc.

[0115] In one embodiment, whether the transaction time set meets the periodicity requirement is judged based on the scale curve. The judgment can also be made based on existing technologies, such as various existing periodicity algorithms and models, and the periodicity standard is determined at the same time.

[0116] The transaction analysis module is used to perform transaction analysis and obtain the user's personal application needs. The personal application needs refer to the direction in which the user wants to apply, which is generally input to apply to a certain software and for what purpose, such as applying to an e-commerce platform, to accurately optimize the recommendation effect; because the current various software and the like have limitations in understanding the transaction preferences and habits of users, they can only rely on the records generated by the users in their software for analysis, and cannot obtain user data in other directions, resulting in limitations, especially for new users, a large amount of data accumulation is required to gradually improve the accuracy of recommendations;

[0117] Determine the transaction data scope according to personal application needs, that is, determine which statistical financial transaction data is needed according to personal application needs, such as the expenditure of product types, etc., which can be determined according to the data scope of the corresponding application needs; the relevant demand data of current formal applications can be determined by existing technologies, and then matched and screened according to personal application needs; mark the determined transaction data scope as personal application scope;

[0118] According to the personal application scope and transaction statistics table, data is collected to obtain personal application data, which is provided to users, who then import it into the corresponding applications. In actual applications, the platform can connect with various existing application platforms, and then directly import the corresponding personal application data into the corresponding applications. In order to distinguish, the corresponding application can be marked as the target application, and then the target application can analyze and utilize the personal application data.

[0119] In one embodiment, personal application data may contain the user's personal privacy, so the personal application data can be desensitized based on existing data desensitization technology, that is, desensitization can be performed while protecting the personal application data, such as deleting the user's personal name, transaction time, account number and other related information.

[0120] In one embodiment, with the rapid development of intelligent technology, the platform can establish an intelligent model based on intelligent technologies such as deep neural networks based on the above process, and then analyze personal application needs according to the intelligent model to directly output corresponding personal application data that meets the requirements.

[0121] A financial transaction data management method, the method comprising:

[0122] Collect users' financial transaction data in real time; conduct statistical analysis on financial transaction data, generate transaction statistics tables, and perform security management on financial transaction data based on the transaction statistics tables;

[0123] Obtain the user's personal application needs and determine the target application based on the personal application needs; determine the personal application data based on the personal application needs and the target application, and connect and input the personal application data into the target application.

[0124] The above formulas are all calculated by removing dimensions and taking numerical values. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data.

[0125] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A financial transaction data management system, characterized in that: Including data collection module, transaction statistics module and transaction analysis module; The data collection module is used to collect the user's financial transaction data in real time; The transaction statistics module is used to perform statistical analysis on the financial transaction data and generate a transaction statistics table; Identify the application collection items, obtain the financial transaction data information corresponding to the application collection items, classify the financial transaction data according to the financial transaction data information and the transaction statistics table, and obtain transaction classification data; Obtaining a user's deletion request for the transaction classification data, and performing deletion management on the transaction classification data according to the deletion request; Integrate the remaining transaction classification data into data to be encrypted, and encrypt the data to be encrypted according to a preset encryption method; The transaction analysis module is used to perform transaction analysis, obtain the user's personal application needs, determine the target application according to the personal application needs; determine personal application data according to the personal application needs and the target application, and connect and input the personal application data into the target application.

2. A financial transaction data management system according to claim 1, characterized in that: Methods for collecting financial transaction data include: The platform establishes a collection preparation table, which is used to count the collection items corresponding to each type of financial transaction and the data collection method and sensitivity description corresponding to each collection item. The sensitivity description is used to explain the sensitivity of the data collection method to the user; Determine a basic collection method, wherein the basic collection method is that the user inputs the corresponding financial transaction data by himself; The user collects the user's historical financial transaction data according to the basic collection method, identifies the financial transaction type corresponding to the historical financial transaction data, and sets the corresponding target collection item according to the financial transaction type; Input the target collection item into the collection preparation table for matching, obtain the data collection method and sensitive description corresponding to the target collection item, and integrate them into the collection description data of the target collection item; The collection description data is displayed to the user, and the user decides whether to apply the corresponding data collection method according to the displayed collection description data; When the user selects to apply the corresponding data collection method, it is deemed that the user authorizes the use of the data collection method, and marks the target collection item corresponding to the data collection method as the application collection item; and marks the remaining target collection items as the basic collection items; Integrate the data collection method corresponding to the target collection item and the basic collection method corresponding to the basic collection item into a transaction collection method; The user's financial transaction data is collected in real time according to the transaction collection method.

3. A financial transaction data management system according to claim 2, characterized in that: The transaction collection method is optimized and analyzed according to the collection records of the transaction collection method to obtain an optimization analysis result, and the optimization analysis result is displayed to the user, and the user decides whether to optimize the transaction collection method.

4. A financial transaction data management system according to claim 3, characterized in that: Methods for optimizing analysis based on the collection records of transaction collection methods include: Identify the basic collection items corresponding to the transaction collection method, extract data from the collection records according to the basic collection items, and obtain the data collection volume and missed collection rate corresponding to the basic collection items, where the data collection volume is the average number of times the user collects financial transaction data of the basic collection items within a preset time; and the missed collection rate is the probability that the user forgets to collect the financial transaction data of the basic collection items; Estimate the user's collection time according to the data collection amount; calculate the optimization value corresponding to the basic collection item according to the optimization formula; the optimization formula is: Where: PA is the optimized value; CT is the acquisition time; η is the missed acquisition rate; PA0 is the preset representative value, which is used to indicate that the missed acquisition rate is 100%; An optimization statistical table is generated according to the optimization values ​​of the basic collection items, and the optimization statistical table is marked as an optimization analysis result.

5. A financial transaction data management system according to claim 1, characterized in that: Methods for statistical analysis of financial transaction data include: The platform presets the corresponding transaction statistics table template, which is marked as the initial statistics template; The initial statistical template is displayed to the user, and the user adjusts the initial statistical template according to personalized needs, and the adjusted initial statistical template is marked as an application statistical template; The financial transaction data is counted according to the application statistics template to obtain a transaction statistics table.

6. A financial transaction data management system according to claim 1, characterized in that: Perform missed collection analysis based on the transaction statistics table, determine the missed basic collection items, and prompt the user.

7. A financial transaction data management system according to claim 6, characterized in that: Methods for leak analysis based on transaction statistics include: The platform party presets the potential transaction type; identifies the existing financial transaction data according to the transaction statistics table, identifies the transaction type and the corresponding transaction time corresponding to the financial transaction data, matches the corresponding potential transaction type to the transaction time according to the transaction type, sorts the transaction time according to the corresponding potential transaction type, and obtains a transaction time set corresponding to the potential transaction type; Generating a periodicity standard of the potential transaction type according to the transaction time set; marking the potential transaction type with the periodicity standard as a transaction periodicity type; Conduct real-time analysis of the transaction statistics table based on the transaction cycle type and the corresponding periodic standards, identify the transaction cycle types that do not meet the periodic standards, and determine the missed basic collection items.

8. A financial transaction data management system according to claim 7, characterized in that: Methods for generating periodic criteria for potential types of transactions based on a set of transaction times include: Step SA1: Setting a cycle scale, wherein the initial cycle length of the cycle scale is one day; each adjustment of the cycle scale will add one day to the original cycle length; Step SA2: Identify the transaction time set according to the cycle scale, obtain the transaction volume corresponding to each cycle duration, and generate a scale curve according to the transaction volume of each cycle duration, wherein the horizontal axis of the scale curve is time and the vertical axis is transaction volume; Step SA3: judging whether the transaction time set meets the periodicity requirement according to the scale curve; When it is judged that the periodicity requirements are met, the periodicity criteria are determined; When it is determined that the periodicity requirement is not met, the cycle length of the cycle scale is identified. When the cycle length is not greater than one month, return to step SA2; when the cycle length is greater than one month, it is evaluated that the transaction time set has no periodicity standard.

9. A financial transaction data management system according to claim 8, characterized in that: Methods for judging whether a transaction time set meets periodicity requirements based on a scale curve include: Acquire the user's association features with the potential transaction type, and adjust the scale curve according to the association features; Fitting the scaling curve to obtain a scaling function, marking the scaling function as CD(t), where t is time; generating an initial function according to the scaling function, marking the initial function as PB(t+1); PB(t+1)=|CD(t+1)-CD(t)|; Calculate the corresponding cycle deviation value according to the cycle formula; the cycle formula is: Where: PC is the period deviation value; Whether the transaction time set meets the periodicity requirement is determined according to the periodic deviation value.

10. A financial transaction data management method, characterized in that: Applied to a financial transaction data management system as claimed in any one of claims 1 to 9, the method comprising: Collecting financial transaction data of users in real time; performing statistical analysis on the financial transaction data, generating a transaction statistics table, and performing security management on the financial transaction data according to the transaction statistics table; Obtain the user's personal application needs, and determine the target application according to the personal application needs; determine personal application data according to the personal application needs and the target application, and connect and input the personal application data into the target application.

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