A personal loan business management system and method based on data sharing

Abstract

The present invention relates to a personal loan business management system and method based on data sharing, wherein the system comprises: a pre-loan management module, which is used to respond to user requests and execute the personal loan pre-loan business process; a mid-loan management module, which is used to respond to user early repayment requests, register user information with early repayment intentions, and conduct early repayment risk assessment, and issue corresponding decision instructions based on the risk assessment results; a post-loan management module, which is used to track all personal loan businesses and execute the post-loan business process after the personal loan business repayment is completed; and a big data sharing module, which is used to retrieve shared data information and send it to the pre-loan management module, the mid-loan management module or the post-loan management module.

Description

Technical Field

[0001] The present invention relates to a personal loan business management system and method based on data sharing, belonging to the technical field of loan business management in the financial industry. Background Art

[0002] Existing personal loan processes are mostly offline and paper-based, making them unable to achieve the intensive and refined management required by the current centralized back-end management model for personal loans. Furthermore, personal loan data is fragmented and not centrally managed, making it difficult to effectively utilize. Summary of the Invention

[0003] In order to solve the problems existing in the above-mentioned prior art, the present invention proposes a personal loan business management system and method based on data sharing.

[0004] The technical solutions of the present invention are as follows:

[0005] In one aspect, a personal loan business management system based on data sharing includes:

[0006] The pre-loan management module is used to respond to user requests and execute the pre-loan business process for personal loans, including loan application, business acceptance, risk review, collateral delivery, loan disbursement, and file management;

[0007] The loan management module is used to respond to users' early repayment requests, register information about users with early repayment intentions, conduct early repayment risk assessments, and issue corresponding decision instructions based on the risk assessment results;

[0008] The post-loan management module is used to track all personal loan businesses and execute post-loan business processes after the personal loan business repayment is completed, including front-end reporting, post-loan acceptance, cancellation review, retrieval of warrants, and cancellation of collateral;

[0009] The big data sharing module is used to retrieve shared data information and send it to the pre-loan management module, the mid-loan management module or the post-loan management module. The shared data information includes user declaration information, user big data loan information, user risk information, user complaint information, and loan business status information.

[0010] As a preferred embodiment, in the pre-loan management module, the risk review method is specifically as follows:

[0011] The big data sharing module obtains user declaration information, contract data, and loan details. Loan details include loan purpose, loan amount, and loan period. When the loan is used to purchase real estate, the target property information is also obtained.

[0012] Big data is used to conduct risk review of contract data and loan details. When the loan is used to purchase a property, a risk review is also conducted, specifically including:

[0013] When evaluating the value of a residential property, the following formula is used:

[0014]

[0015] Where D1 is the target property value of residential type, T is the loan period, and t is the tth year in the loan period; C base is the basic housing price, obtained by querying the current time data; α t is the rate of change of the target property's housing price in year t obtained by the forecasting model; γ is the discount rate;

[0016] When the target property is a commercial property, the following formula is used for evaluation:

[0017]

[0018] Where F2 is the target property value of commercial type, T is the loan period, and t is the tth year in the loan period; CASH t is the cash flow in the tth year; θ is the discount rate; γ is the discount rate;

[0019] The assessed target property value is compared with the loan amount. When the target property value is less than the loan amount, a risk warning is issued.

[0020] As a preferred embodiment, when evaluating the value of a target property, the method for obtaining the target property's housing price change rate in the tth year by the prediction model is specifically as follows:

[0021] Obtain the area where the target property is located, and use the pre-trained neural network model to predict the population change rate of the area where the target property is located in the next t-th year and the population change rate of the country where the target property is located in the next t-th year;

[0022] Get the average housing price change rate of the target property area over the past M years;

[0023] The ratio of the population change rate of the region where the target property is located in the next t-th year to the population change rate of the country where the target property is located in the next t-th year is used as the correction coefficient. The housing price change rate of the target property in the t-th year is obtained by the correction coefficient and the average housing price change rate of the region where the target property is located in the historical M years.

[0024] As a preferred embodiment, in the step of registering user information with the intention of early repayment and performing early repayment risk assessment, the method of performing early repayment risk assessment is specifically as follows:

[0025] The target user's credit score and default rate in each credit category are obtained through the big data sharing module, and the target user's risk index for each credit category is calculated using the following formula:

[0026]

[0027] in, is the risk index of the target user for the i-th credit category, L is the total number of users, is the credit score of the jth user in the i-th credit category, is the average credit score of all users in the i-th credit category, is the default rate of the jth user in the i-th credit category, is the average default rate of all users under the i-th credit category, is the credit score of the target user under the i-th credit category, is the default rate of the target user under the i-th credit category;

[0028] When the risk index of the target user for the i-th credit category is greater than a preset threshold, the risk index is selected, and the comprehensive risk score result of the target user is calculated using all selected risk indices.

[0029] On the other hand, the present invention also provides a personal loan business management method based on data sharing, comprising the following steps:

[0030] Respond to user requests and execute pre-loan business processes for personal loans, including loan application, business acceptance, risk review, collateral delivery, loan disbursement, and file management;

[0031] Respond to user early repayment requests, register information of users with early repayment intentions, conduct early repayment risk assessments, and issue corresponding decision instructions based on the risk assessment results;

[0032] Track all personal loan transactions and execute post-loan business processes after repayment of personal loans, including front-end reporting, post-loan acceptance, cancellation review, retrieval of warrants, and cancellation of collateral;

[0033] When executing the pre-loan business process and post-loan business process of personal loans and conducting early repayment risk assessment, shared data information is retrieved. The shared data information includes user declaration information, user big data loan information, user risk information, user complaint information, and loan business status information.

[0034] As a preferred implementation method, when executing the personal loan pre-loan business process, the risk review method is specifically as follows:

[0035] The big data sharing module obtains user declaration information, contract data, and loan details. Loan details include loan purpose, loan amount, and loan period. When the loan is used to purchase real estate, the target property information is also obtained.

[0036] Big data is used to conduct risk review of contract data and loan details. When the loan is used to purchase a property, a risk review is also conducted, specifically including:

[0037] When evaluating the value of a residential property, the following formula is used:

[0038]

[0039] Where F1 is the target property value of residential type, T is the loan period, and t is the tth year in the loan period; C base is the basic housing price, obtained by querying the current time data; α t is the rate of change of the target property's housing price in year t obtained by the forecasting model; γ is the discount rate;

[0040] When the target property is a commercial property, the following formula is used for evaluation:

[0041]

[0042] Where F2 is the target property value of commercial type, T is the loan period, and t is the tth year in the loan period; CASH t is the cash flow in the tth year; θ is the discount rate; γ is the discount rate;

[0043] The assessed target property value is compared with the loan amount. When the target property value is less than the loan amount, a risk warning is issued.

[0044] As a preferred embodiment, when evaluating the value of a target property, the method for obtaining the target property's housing price change rate in the tth year by the prediction model is specifically as follows:

[0045] Obtain the area where the target property is located, and use the pre-trained neural network model to predict the population change rate of the area where the target property is located in the next t-th year and the population change rate of the country where the target property is located in the next t-th year;

[0046] Get the average housing price change rate of the target property area over the past M years;

[0047] The ratio of the population change rate of the region where the target property is located in the next t-th year to the population change rate of the country where the target property is located in the next t-th year is used as the correction coefficient. The housing price change rate of the target property in the t-th year is obtained by the correction coefficient and the average housing price change rate of the region where the target property is located in the historical M years.

[0048] As a preferred embodiment, in the step of registering user information with the intention of early repayment and performing early repayment risk assessment, the method of performing early repayment risk assessment is specifically as follows:

[0049] The target user's credit score and default rate in each credit category are obtained through the big data sharing module, and the target user's risk index for each credit category is calculated using the following formula:

[0050]

[0051] in, is the risk index of the target user for the i-th credit category, L is the total number of users, is the credit score of the jth user in the i-th credit category, is the average credit score of all users in the i-th credit category, is the default rate of the jth user in the i-th credit category, is the average default rate of all users under the i-th credit category, is the credit score of the target user under the i-th credit category, is the default rate of the target user under the i-th credit category;

[0052] When the risk index of the target user for the i-th credit category is greater than a preset threshold, the risk index is selected, and the comprehensive risk score result of the target user is calculated using all selected risk indices.

[0053] On the other hand, the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any embodiment of the present invention when executing the program.

[0054] In another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in any embodiment of the present invention.

[0055] The present invention has the following beneficial effects:

[0056] This invention is based on the personal loan business ecological scenario and is supported by the aggregation of core business system data. By sorting out and optimizing the "pre-loan + mid-loan + post-loan" processes in the personal loan scenario, connecting with the big data platform, and building a full-process personal loan business management system based on data sharing, the invention embeds data intelligence into the operation and management process of the personal loan business, personalizes the collaborative interaction mechanism with the business front desk, and improves the employee user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a schematic diagram of the system framework of the first embodiment of the present invention;

[0058] Figure 2 This is a flow chart of the method of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0060] It should be understood that the step numbers used herein are only for convenience of description and are not intended to limit the order in which the steps are to be executed.

[0061] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0062] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0063] The term "and / or" refers to and includes any and all possible combinations of one or more of the associated listed items.

[0064] Example 1:

[0065] See also Figure 1This embodiment is based on the personal loan business ecological scenario and is supported by the aggregation of core business system data. By sorting out and optimizing the "pre-loan + mid-loan + post-loan" processes in the personal loan scenario, connecting with the big data platform, and building a full-process personal loan business management system based on data sharing, data intelligence is embedded in the operation and management process of the personal loan business, and a personalized collaborative interaction mechanism with the business front desk is customized to improve the employee user experience.

[0066] The system specifically includes:

[0067] The pre-loan management module is used to respond to user requests and execute the pre-loan business process for personal loans, including loan business declaration, business acceptance, risk review, collateral delivery, loan issuance and file management; specifically, the pre-loan management module verifies the business status of the loan application according to the approved loan application sequence number reported by the front desk, and then registers the transfer after verifying the business status of the loan application in the business acceptance link, realizing intelligent management of the entire process from acceptance, mortgage, issuance, certificate storage and file archiving.

[0068] The mid-loan management module is used to respond to users' requests for early repayment, register information about users with early repayment intentions, conduct early repayment risk assessments, and issue corresponding decision instructions based on the risk assessment results. When the risk assessment results pass, a decision instruction to make early repayments according to the process is issued; when the risk assessment results fail, a decision instruction to refuse early repayment is issued. Specifically, the mid-loan management module registers early repayment customer information, matches customer loans and risk information, and reasonably manages and arranges customer issues. This function enables grassroots banks to carry out refined management of the overall picture of early repayment customer groups, effectively alleviating the pressure of "bank runs" and complaints currently faced by early repayment businesses, promoting the effective penetration of personal financial products into this customer group, and realizing the active transformation from "being exhausted to cope" to "marketing penetration" for this customer group.

[0069] The post-loan management module tracks all personal loan transactions and, after repayment, executes the post-loan process, including front-office submission, post-loan acceptance, cancellation review, retrieval of warrants, and collateral cancellation. Specifically, this module achieves a paperless operation for the entire mortgage cancellation process through user extension, system-solidified application materials, and intelligent retrieval of data from a big data platform. After verifying the loan's settlement and account closure status, the module registers the mortgage cancellation process, replacing manual control with machine control to enhance risk control.

[0070] The Big Data Sharing Module is used to retrieve and send shared data to the pre-loan management module, mid-loan management module, or post-loan management module. This shared data includes user application information, user big data loan information, user risk information, user complaint information, and loan business status information. Throughout the lifecycle of individual loan transactions, the Big Data Sharing Module utilizes the Big Data platform to process and identify individual loan customers, retrieve data information, and generate marketing lists using daily batches of data from customer tags. It also supports data integration and customized one-click query reports. Employees with appropriate job permissions can query templates and download reports to obtain marketing and decision-making support.

[0071] During the pre-loan process, the Big Data Sharing Module identifies pending disbursements for second-hand mortgages and sends them to the branch manager at the institution that opened the seller's payment card. This allows for proactive intervention upon receipt of funds to retain the seller's funds. During the loan process, the Big Data Sharing Module prioritizes early repayments based on upcoming expiration dates and user complaints.

[0072] As a preferred implementation of this embodiment, in the pre-loan management module, the risk review method is specifically as follows:

[0073] The big data sharing module obtains user declaration information, contract data, and loan details. Loan details include loan purpose, loan amount, and loan period. When the loan is used to purchase real estate, the target property information is also obtained.

[0074] Big data is used to conduct risk review of contract data and loan details. When the loan is used to purchase a property, a risk review is also conducted, specifically including:

[0075] When evaluating the value of a residential property, the following formula is used:

[0076]

[0077] Where F1 is the target property value of residential type, T is the loan period, and t is the tth year in the loan period; C base is the basic housing price, obtained by querying the current time data; α t is the rate of change of the target property's housing price in year t obtained by the forecasting model; γ is the discount rate;

[0078] When the target property is a commercial property, the following formula is used for evaluation:

[0079]

[0080] Where F2 is the target property value of commercial type, T is the loan period, and t is the tth year in the loan period; CASH tis the cash flow in the tth year; θ is the discount rate; γ is the discount rate;

[0081] The assessed target property value is compared with the loan amount. When the target property value is less than the loan amount, a risk warning is issued.

[0082] As a preferred implementation of this embodiment, when evaluating the value of a target property, the method for obtaining the target property's housing price change rate in the tth year through the prediction model is specifically as follows:

[0083] Obtain the area where the target property is located, and use the pre-trained neural network model to predict the population change rate of the area where the target property is located in the next t-th year and the population change rate of the country where the target property is located in the next t-th year;

[0084] Get the average housing price change rate of the target property area over the past M years;

[0085] The ratio of the population change rate of the region where the target property is located in the next t-th year to the population change rate of the country where the target property is located in the next t-th year is used as the correction coefficient. The housing price change rate of the target property in the t-th year is obtained by the correction coefficient and the average housing price change rate of the region where the target property is located in the historical M years.

[0086] As a preferred implementation of this embodiment, in the step of registering user information with the intention of early repayment and performing early repayment risk assessment, the method of performing early repayment risk assessment is specifically as follows:

[0087] The target user's credit score and default rate in each credit category are obtained through the big data sharing module, and the target user's risk index for each credit category is calculated using the following formula:

[0088]

[0089] in, is the risk index of the target user for the i-th credit category, L is the total number of users, is the credit score of the jth user in the i-th credit category, is the average credit score of all users in the i-th credit category, is the default rate of the jth user in the i-th credit category, is the average default rate of all users under the i-th credit category, is the credit score of the target user under the i-th credit category, is the default rate of the target user under the i-th credit category;

[0090] When the risk index of the target user for the i-th credit category is greater than a preset threshold, the risk index is selected, and the comprehensive risk score result of the target user is calculated using all selected risk indices.

[0091] When the target user's comprehensive risk score is greater than the preset threshold, the early repayment risk assessment result is failed; when it is less than the threshold, it is passed.

[0092] Example 2:

[0093] See also Figure 2 This embodiment provides a personal loan business management method based on data sharing, including the following steps:

[0094] S100: Execute the pre-loan business process for personal loans in response to a user request, including loan application, business acceptance, risk review, collateral delivery, loan issuance, and file management. This step is used to implement the functions of the pre-loan management module in Example 1 and will not be repeated here.

[0095] S200: Respond to a user's early repayment request, register information of users with early repayment intentions, conduct an early repayment risk assessment, and issue corresponding decision instructions based on the risk assessment results. This step is used to implement the functions of the loan management module in Example 1 and will not be repeated here.

[0096] S300: Track all personal loan transactions. After repayment of the personal loan transactions is completed, execute the post-loan business process, including front-end reporting, post-loan acceptance, cancellation review, retrieval of warrants, and collateral cancellation. This step is used to implement the functions of the post-loan management module in Example 1 and will not be repeated here.

[0097] S400. When executing the pre-loan business process and post-loan business process of personal loans and conducting early repayment risk assessment, shared data information is retrieved. The shared data information includes user declaration information, user big data loan information, user risk information, user complaint information, and loan business status information. This step is used to implement the function of the big data sharing module in Example 1 and will not be repeated here.

[0098] As a preferred implementation of this embodiment, when executing the personal loan pre-loan business process, the risk review method is specifically as follows:

[0099] The big data sharing module obtains user declaration information, contract data, and loan details. Loan details include loan purpose, loan amount, and loan period. When the loan is used to purchase real estate, the target property information is also obtained.

[0100] Big data is used to conduct risk review of contract data and loan details. When the loan is used to purchase a property, a risk review is also conducted, specifically including:

[0101] When evaluating the value of a residential property, the following formula is used:

[0102]

[0103] Where F1 is the target property value of residential type, T is the loan period, and t is the tth year in the loan period; C base is the basic housing price, obtained by querying the current time data; α t is the rate of change of the target property's housing price in year t obtained by the forecasting model; γ is the discount rate;

[0104] When the target property is a commercial property, the following formula is used for evaluation:

[0105]

[0106] Where F2 is the target property value of commercial type, T is the loan period, and t is the tth year in the loan period; CASH t is the cash flow in the tth year; θ is the discount rate; γ is the discount rate;

[0107] The assessed target property value is compared with the loan amount. When the target property value is less than the loan amount, a risk warning is issued.

[0108] As a preferred implementation of this embodiment, when evaluating the value of a target property, the method for obtaining the target property's housing price change rate in year t by the prediction model is specifically as follows:

[0109] Obtain the area where the target property is located, and use the pre-trained neural network model to predict the population change rate of the area where the target property is located in the next t-th year and the population change rate of the country where the target property is located in the next t-th year;

[0110] Get the average housing price change rate of the target property area over the past M years;

[0111] The ratio of the population change rate of the region where the target property is located in the next t-th year to the population change rate of the country where the target property is located in the next t-th year is used as the correction coefficient. The housing price change rate of the target property in the t-th year is obtained by the correction coefficient and the average housing price change rate of the region where the target property is located in the historical M years.

[0112] As a preferred implementation of this embodiment, in the step of registering user information with the intention of early repayment and performing early repayment risk assessment, the method of performing early repayment risk assessment is specifically as follows:

[0113] The target user's credit score and default rate in each credit category are obtained through the big data sharing module, and the target user's risk index for each credit category is calculated using the following formula:

[0114]

[0115] in, is the risk index of the target user for the i-th credit category, L is the total number of users, is the credit score of the jth user in the i-th credit category, is the average credit score of all users in the i-th credit category, is the default rate of the jth user in the i-th credit category, is the average default rate of all users under the i-th credit category, is the credit score of the target user under the i-th credit category, is the default rate of the target user under the i-th credit category;

[0116] When the risk index of the target user for the i-th credit category is greater than a preset threshold, the risk index is selected, and the comprehensive risk score result of the target user is calculated using all selected risk indices.

[0117] Example 3:

[0118] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method described in any embodiment of the present invention is implemented.

[0119] Example 4:

[0120] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method described in any embodiment of the present invention is implemented.

[0121] In the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can represent: a, b, c, a and b, a and c, b and c or a and b and c, where a, b, c can be single or multiple.

[0122] Those skilled in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented using a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0123] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0124] In the several embodiments provided in this application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of this application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), magnetic disk or optical disk, and other media that can store program code.

[0125] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention's description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A personal loan business management system based on data sharing, characterized in that: include: The pre-loan management module is used to respond to user requests and execute the pre-loan business process for personal loans, including loan application, business acceptance, risk review, collateral delivery, loan disbursement, and file management; The loan management module is used to respond to users' early repayment requests, register information about users with early repayment intentions, conduct early repayment risk assessments, and issue corresponding decision instructions based on the risk assessment results; The post-loan management module is used to track all personal loan businesses and execute post-loan business processes after the personal loan business repayment is completed, including front-end reporting, post-loan acceptance, cancellation review, retrieval of warrants, and cancellation of collateral; The big data sharing module is used to retrieve shared data information and send it to the pre-loan management module, the mid-loan management module or the post-loan management module. The shared data information includes user declaration information, user big data loan information, user risk information, user complaint information, and loan business status information; Among them, in the pre-loan management module, the risk review method is specifically as follows: The big data sharing module obtains user declaration information, contract data, and loan details. Loan details include loan purpose, loan amount, and loan period. When the loan is used to purchase real estate, the target property information is also obtained. Big data is used to conduct risk review of contract data and loan details. When the loan is used to purchase a property, a risk review is also conducted, specifically including: When evaluating the value of a residential property, the following formula is used: Where F1 is the target property value of residential type, T is the loan period, and t is the tth year in the loan period; C base is the basic housing price, obtained by querying the current time data; α t is the rate of change of the target property's housing price in year t obtained by the forecasting model; γ is the discount rate; When the target property is a commercial property, the following formula is used for evaluation: Where F2 is the target property value of commercial type, T is the loan period, and t is the tth year in the loan period; CASH t is the cash flow in the tth year; θ is the discount rate; γ is the discount rate; The assessed target property value is compared with the loan amount. When the target property value is less than the loan amount, a risk warning is issued.

2. A personal loan business management system based on data sharing according to claim 1, characterized in that: When evaluating the value of a target property, the method for obtaining the target property's price change rate in year t through the prediction model is specifically as follows: Obtain the area where the target property is located, and use the pre-trained neural network model to predict the population change rate of the area where the target property is located in the next t-th year and the population change rate of the country where the target property is located in the next t-th year; Get the average housing price change rate of the target property area over the past M years; The ratio of the population change rate of the region where the target property is located in the next t-th year to the population change rate of the country where the target property is located in the next t-th year is used as the correction coefficient. The housing price change rate of the target property in the t-th year is obtained by the correction coefficient and the average housing price change rate of the region where the target property is located in the historical M years.

3. A personal loan business management system based on data sharing according to claim 1, characterized in that: In the step of registering user information with the intention of early repayment and performing early repayment risk assessment, the method of performing early repayment risk assessment is specifically as follows: The target user's credit score and default rate in each credit category are obtained through the big data sharing module, and the target user's risk index for each credit category is calculated using the following formula: in, is the risk index of the target user for the i-th credit category, L is the total number of users, is the credit score of the jth user in the i-th credit category, is the average credit score of all users in the i-th credit category, is the default rate of the jth user in the i-th credit category, is the average default rate of all users under the i-th credit category, is the credit score of the target user under the i-th credit category, is the default rate of the target user under the i-th credit category; When the risk index of the target user for the i-th credit category is greater than a preset threshold, the risk index is selected, and the comprehensive risk score result of the target user is calculated using all selected risk indices.

4. A personal loan business management method based on data sharing, characterized in that: The following steps are involved: Respond to user requests and execute pre-loan business processes for personal loans, including loan application, business acceptance, risk review, collateral delivery, loan disbursement, and file management; Respond to user early repayment requests, register information of users with early repayment intentions, conduct early repayment risk assessments, and issue corresponding decision instructions based on the risk assessment results; Track all personal loan transactions and execute post-loan business processes after repayment of personal loans, including front-end reporting, post-loan acceptance, cancellation review, retrieval of warrants, and cancellation of collateral; When executing the pre-loan and post-loan business processes for personal loans and conducting early repayment risk assessments, shared data information is retrieved, including user declaration information, user big data loan information, user risk information, user complaint information, and loan business status information; Among them, when executing the pre-loan business process for personal loans, the specific risk review method is as follows: The big data sharing module obtains user declaration information, contract data, and loan details. Loan details include loan purpose, loan amount, and loan period. When the loan is used to purchase real estate, the target property information is also obtained. Big data is used to conduct risk review of contract data and loan details. When the loan is used to purchase a property, a risk review is also conducted, specifically including: When evaluating the value of a residential property, the following formula is used: Where F1 is the target property value of residential type, T is the loan period, and t is the tth year in the loan period; C base is the basic housing price, obtained by querying the current time data; α t is the rate of change of the target property's housing price in year t obtained by the forecasting model; γ is the discount rate; When the target property is a commercial property, the following formula is used for evaluation: Where F2 is the target property value of commercial type, T is the loan period, and t is the tth year in the loan period; CASH t is the cash flow in the tth year; θ is the discount rate; γ is the discount rate; The assessed target property value is compared with the loan amount. When the target property value is less than the loan amount, a risk warning is issued.

5. A personal loan business management method based on data sharing according to claim 4, characterized in that: When evaluating the value of a target property, the method for obtaining the target property's price change rate in year t through the prediction model is specifically as follows: Obtain the area where the target property is located, and use the pre-trained neural network model to predict the population change rate of the area where the target property is located in the next t-th year and the population change rate of the country where the target property is located in the next t-th year; Get the average housing price change rate of the target property area over the past M years; The ratio of the population change rate of the region where the target property is located in the next t-th year to the population change rate of the country where the target property is located in the next t-th year is used as the correction coefficient. The housing price change rate of the target property in the t-th year is obtained by the correction coefficient and the average housing price change rate of the region where the target property is located in the historical M years.

6. A personal loan business management method based on data sharing according to claim 4, characterized in that: In the step of registering user information with the intention of early repayment and performing early repayment risk assessment, the method of performing early repayment risk assessment is specifically as follows: The target user's credit score and default rate in each credit category are obtained through the big data sharing module, and the target user's risk index for each credit category is calculated using the following formula: in, is the risk index of the target user for the i-th credit category, L is the total number of users, is the credit score of the jth user in the i-th credit category, is the average credit score of all users in the i-th credit category, is the default rate of the jth user in the i-th credit category, is the average default rate of all users under the i-th credit category, is the credit score of the target user under the i-th credit category, is the default rate of the target user under the i-th credit category; When the risk index of the target user for the i-th credit category is greater than a preset threshold, the risk index is selected, and the comprehensive risk score result of the target user is calculated using all selected risk indices.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 4 to 6 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 4 to 6 is implemented.

Images