Personal loan business management system and method based on data sharing

Abstract

The invention relates to a personal loan business management system and method based on data sharing, and the system comprises a pre-loan management module which is used for responding to a user request to execute a personal loan pre-loan business process; the in-loan management module is used for registering user information with an advanced repayment intention in response to the advanced repayment request of the user, performing advanced repayment risk assessment and issuing a corresponding decision instruction according to a risk assessment result; the post-loan management module is used for tracking all personal loan businesses and executing a post-loan business process after the repayment of the personal loan businesses is finished; and the big data sharing module is used for calling shared data information and sending the shared data information to the pre-loan management module, the in-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] The existing personal loan process is mostly offline and paper-based, which cannot meet the requirements of intensive and refined management under the current centralized back-end operation model of personal loans. At the same time, personal loan data is scattered and not centrally managed, and cannot be effectively used. Summary of the invention

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

[0004] The technical solution of the present invention is as follows:

[0005] On the one hand, 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 business declaration, business acceptance, risk review, collateral delivery, loan issuance, and file management;

[0007] The loan management module is used to respond to users' early repayment requests, register information of users who intend to repay early, conduct early repayment risk assessment, 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. After the personal loan business repayment is completed, the post-loan business process is executed, including front desk submission, 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 implementation, in the pre-loan management module, the risk review method is specifically as follows:

[0011] The user declaration information is obtained through the big data sharing module to obtain the contract data and loan details. The loan details include the purpose of the loan, the loan amount, and the loan period. When the loan is used to purchase a property, the target property information is also obtained.

[0012] The contract data and loan details are reviewed through big data for risk. When the loan is used to purchase a property, a risk review is also conducted, including:

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

[0014]

[0015] Among them, D 1 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 the tth year obtained through the prediction model; γ is the discount rate;

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

[0017]

[0018] Among them, F 2 is the value of the commercial property, 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 implementation, when evaluating the value of a target property, the method for obtaining the rate of change of the target property price in the tth year by the prediction model is specifically as follows:

[0021] Obtain the area where the target property is located, and 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 based on the pre-trained neural network model;

[0022] Get the average rate of change of housing prices in the area where the target property is located in the past M years;

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

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

[0025] The credit score and default rate of the target user in each credit category are obtained through the big data sharing module, and the risk index of the target user for each credit category is calculated by 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 in the i-th credit category, is the credit score of the target user in 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 through all selected risk indexes.

[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 the pre-loan business process for personal loans, including loan business declaration, business acceptance, risk review, collateral delivery, loan issuance and file management;

[0031] Respond to users' early repayment requests, register information of users who intend to repay early, conduct early repayment risk assessment, and issue corresponding decision instructions based on the risk assessment results;

[0032] Track all personal loan businesses and execute post-loan business processes after the personal loan business repayment is completed, including front desk submission, 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, when executing the personal loan pre-loan business process, the risk review method is specifically as follows:

[0035] The user declaration information is obtained through the big data sharing module to obtain the contract data and loan details. The loan details include the purpose of the loan, the loan amount, and the loan period. When the loan is used to purchase a property, the target property information is also obtained.

[0036] The contract data and loan details are reviewed through big data for risk. When the loan is used to purchase a property, a risk review is also conducted, including:

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

[0038]

[0039] Among them, F 1 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 the tth year obtained through the prediction model; γ is the discount rate;

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

[0041]

[0042] Among them, F 2 is the value of the commercial property, 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 implementation, when evaluating the value of a target property, the method for obtaining the rate of change of the target property price in the tth year by the prediction model is specifically as follows:

[0045] Obtain the area where the target property is located, and 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 based on the pre-trained neural network model;

[0046] Get the average rate of change of housing prices in the area where the target property is located in the past M years;

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

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

[0049] The credit score and default rate of the target user in each credit category are obtained through the big data sharing module, and the risk index of the target user for each credit category is calculated by 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 in the i-th credit category, is the credit score of the target user in 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 through all selected risk indexes.

[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 yet 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 described in any embodiment of the present invention.

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

[0056] The present invention is based on the personal loan business ecological scenario and supported by the aggregation of core business system data. By combing and optimizing the "pre-loan + loan + post-loan" processes in the personal loan scenario, it connects the big data platform to build a full-process personal loan business management system based on data sharing, 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 employee user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a schematic diagram of the system framework of Embodiment 1 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 be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments 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.

[0060] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are 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, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

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

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

[0064] Embodiment 1:

[0065] See also Figure 1This embodiment is based on the personal loan business ecological scenario and supported by the data aggregation of the core business system. By combing and optimizing the "pre-loan + loan + post-loan" processes in the personal loan scenario, the big data platform is connected to build a full-process personal loan business management system based on data sharing, embedding data intelligence into the operation and management process of the personal loan business, personalizing the collaborative interaction mechanism with the business front desk, and improving 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 of personal loans, including loan business declaration, business acceptance, risk review, delivery of collateral, loan issuance and file management; specifically, the pre-loan management module verifies the business status of the declared loan in the business acceptance link according to the approved loan application sequence number submitted by the front desk, and registers the transfer, thereby realizing intelligent management of the entire process from acceptance, mortgage, issuance, certificate storage and file archiving.

[0068] The loan management module is used to respond to users' 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. 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 loan management module registers early repayment customer information, matches customer loans and risk information, and reasonably manages and arranges customer issues. Through this function, each grassroots bank can carry out refined management of the overall picture of the early repayment customer base, which can effectively alleviate the pressure of "bank runs" and complaints currently encountered by the early repayment business, promote the effective penetration of personal financial products to this customer base, and realize the active transformation from "overwhelmed to cope" to "marketing penetration" for this customer base.

[0069] The post-loan management module is used to track all personal loan businesses. After the repayment of personal loan business is completed, the post-loan business process is executed, including front desk submission, post-loan acceptance, cancellation review, retrieval of warrants and cancellation of collateral. Specifically, the post-loan management module realizes the paperless operation mode of the entire process of mortgage cancellation business through user extension, system solidification of application materials, and intelligent retrieval of big data platform data. After checking whether the loan has been settled and the business status of the account has been cancelled, the circulation registration of the mortgage cancellation business is carried out, and the machine control replaces the human control to improve the risk control level.

[0070] 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. During the entire life cycle of the personal loan business, the big data sharing module uses the big data platform to process and identify personal loan customers, retrieve data information, and use customer tags to run batches of data daily to generate marketing lists. It also supports data integration and customized one-click query reports. Employees with job permissions can obtain marketing and decision support by querying templates and downloading reports.

[0071] The big data sharing module identifies the "pending" loans in the second-hand housing loans during the loan issuance process of the pre-loan business process, and pushes them to the branch manager of the institution that opened the seller's payment card account, intervening in advance when the payment is received to retain the seller's funds. The big data sharing module increases the priority of the early repayment processing process in the loan process based on the information about the upcoming expiration of the appointment 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 user declaration information is obtained through the big data sharing module to obtain the contract data and loan details. The loan details include the purpose of the loan, the loan amount, and the loan period. When the loan is used to purchase a property, the target property information is also obtained.

[0074] The contract data and loan details are reviewed through big data for risk. When the loan is used to purchase a property, a risk review is also conducted, including:

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

[0076]

[0077] Among them, F 1 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 the tth year obtained through the prediction model; γ is the discount rate;

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

[0079]

[0080] Among them, F 2is the value of the commercial property, 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;

[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 the target property, the method of obtaining the rate of change of the housing price of the target property in the tth year through the prediction model is specifically as follows:

[0083] Obtain the area where the target property is located, and 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 based on the pre-trained neural network model;

[0084] Get the average rate of change of housing prices in the area where the target property is located in the past M years;

[0085] The ratio of the population change rate of the area where the target property is located in the tth year to the population change rate of the country where the target property is located in the tth year is used as the correction coefficient. The housing price change rate of the target property in the tth year is obtained by the correction coefficient and the average housing price change rate of the area 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 early repayment intention and conducting early repayment risk assessment, the method of conducting early repayment risk assessment is specifically:

[0087] The credit score and default rate of the target user in each credit category are obtained through the big data sharing module, and the risk index of the target user for each credit category is calculated by 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 in the i-th credit category, is the credit score of the target user in 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 through all selected risk indexes.

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

[0092] Embodiment 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, responding to a user's request to execute a personal loan pre-loan business process, including loan business declaration, business acceptance, risk review, collateral delivery, loan issuance, and file management; this step is used to implement the function of the pre-loan management module in the first embodiment, and will not be repeated here;

[0095] S200, responding to the user's early repayment request, registering the user information with the intention of early repayment, and conducting early repayment risk assessment, and issuing corresponding decision instructions according to the risk assessment results; this step is used to implement the function of the loan management module in the first embodiment, and will not be repeated here;

[0096] S300, tracking all personal loan businesses, and executing the post-loan business process after the personal loan business repayment is completed, including front desk submission, post-loan acceptance, cancellation review, retrieval of warrants, and collateral cancellation; this step is used to implement the function of the post-loan management module in the first embodiment, 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, retrieve shared data information, which 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 pre-loan business process of a personal loan, the risk review method is specifically as follows:

[0099] The user declaration information is obtained through the big data sharing module to obtain the contract data and loan details. The loan details include the purpose of the loan, the loan amount, and the loan period. When the loan is used to purchase a property, the target property information is also obtained.

[0100] The contract data and loan details are reviewed through big data for risk. When the loan is used to purchase a property, a risk review is also conducted, including:

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

[0102]

[0103] Among them, F 1 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 the tth year obtained through the prediction model; γ is the discount rate;

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

[0105]

[0106] Among them, F 2 is the value of the commercial property, 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 the target property, the method of obtaining the rate of change of the housing price of the target property in the tth year through the prediction model is specifically as follows:

[0109] Obtain the area where the target property is located, and 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 based on the pre-trained neural network model;

[0110] Get the average rate of change of housing prices in the area where the target property is located in the past M years;

[0111] The ratio of the population change rate of the area where the target property is located in the tth year to the population change rate of the country where the target property is located in the tth year is used as the correction coefficient. The housing price change rate of the target property in the tth year is obtained by the correction coefficient and the average housing price change rate of the area 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 early repayment intention and performing early repayment risk assessment, the method for performing early repayment risk assessment is specifically:

[0113] The credit score and default rate of the target user in each credit category are obtained through the big data sharing module, and the risk index of the target user for each credit category is calculated by 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 in the i-th credit category, is the credit score of the target user in 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 through all selected risk indexes.

[0117] Embodiment three:

[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, wherein the processor implements the method described in any embodiment of the present invention when executing the program.

[0119] Embodiment 4:

[0120] This embodiment provides a computer-readable storage medium on which a computer program is stored. 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 than one" 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 be represented by: 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 of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in 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. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0123] Those skilled in the art can 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 several embodiments provided in the present 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 the present 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, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present 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), disk or optical disk, and other media that can store program codes.

[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 specification 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 business declaration, business acceptance, risk review, collateral delivery, loan issuance, and file management; The loan management module is used to respond to users' early repayment requests, register information of users who intend to repay early, conduct early repayment risk assessment, and issue corresponding decision instructions based on the risk assessment results; The post-loan management module is used to track all personal loan businesses. After the personal loan business repayment is completed, the post-loan business process is executed, including front desk submission, 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.

2. According to claim 1, a personal loan business management system based on data sharing is characterized in that: In the pre-loan management module, the risk review method is specifically as follows: The user declaration information is obtained through the big data sharing module to obtain the contract data and loan details. The loan details include the purpose of the loan, the loan amount, and the loan period. When the loan is used to purchase a property, the target property information is also obtained. The contract data and loan details are reviewed through big data for risk. When the loan is used to purchase a property, a risk review is also conducted, including: When evaluating the value of the target property, if the target property is a residential property, the following formula is used for evaluation: 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 the tth year obtained through the prediction 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 value of the commercial property, 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.

3. A personal loan business management system based on data sharing according to claim 2, characterized in that: When evaluating the value of the target property, the method for obtaining the rate of change of the target property price in the tth year through the prediction model is specifically as follows: Obtain the area where the target property is located, and 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 based on the pre-trained neural network model; Get the average rate of change of housing prices in the area where the target property is located in the past M years; The ratio of the population change rate of the area where the target property is located in the tth year to the population change rate of the country where the target property is located in the tth year is used as the correction coefficient. The housing price change rate of the target property in the tth year is obtained by the correction coefficient and the average housing price change rate of the area where the target property is located in the historical M years.

4. According to claim 1, a personal loan business management system based on data sharing is characterized in that: In the step of registering the user information with the intention of early repayment and conducting early repayment risk assessment, the method of conducting early repayment risk assessment is specifically: The credit score and default rate of the target user in each credit category are obtained through the big data sharing module, and the risk index of the target user for each credit category is calculated by 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 in the i-th credit category, is the credit score of the target user in 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 through all selected risk indexes.

5. A personal loan business management method based on data sharing, characterized in that: The following steps are involved: 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; Respond to users' early repayment requests, register information of users who intend to repay early, conduct early repayment risk assessment, and issue corresponding decision instructions based on the risk assessment results; Track all personal loan businesses and execute post-loan business processes after the personal loan business repayment is completed, including front desk submission, post-loan acceptance, cancellation review, retrieval of warrants and cancellation of collateral; 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.

6. A personal loan business management method based on data sharing according to claim 5, characterized in that: When executing the pre-loan business process for personal loans, the specific risk review method is as follows: The user declaration information is obtained through the big data sharing module to obtain the contract data and loan details. The loan details include the purpose of the loan, the loan amount, and the loan period. When the loan is used to purchase a property, the target property information is also obtained. The contract data and loan details are reviewed through big data for risk. When the loan is used to purchase a property, a risk review is also conducted, including: When evaluating the value of the target property, if the target property is a residential property, the following formula is used for evaluation: 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 the tth year obtained through the prediction 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 value of the commercial property, 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.

7. A personal loan business management method based on data sharing according to claim 6, characterized in that: When evaluating the value of the target property, the method for obtaining the rate of change of the target property price in the tth year through the prediction model is specifically as follows: Obtain the area where the target property is located, and 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 based on the pre-trained neural network model; Get the average rate of change of housing prices in the area where the target property is located in the past M years; The ratio of the population change rate of the area where the target property is located in the tth year to the population change rate of the country where the target property is located in the tth year is used as the correction coefficient. The housing price change rate of the target property in the tth year is obtained by the correction coefficient and the average housing price change rate of the area where the target property is located in the historical M years.

8. A personal loan business management method based on data sharing according to claim 5, characterized in that: In the step of registering the user information with the intention of early repayment and conducting early repayment risk assessment, the method of conducting early repayment risk assessment is specifically: The credit score and default rate of the target user in each credit category are obtained through the big data sharing module, and the risk index of the target user for each credit category is calculated by 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 in the i-th credit category, is the credit score of the target user in 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 through all selected risk indexes.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 5 to 8 is implemented.

10. 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 5 to 8 is implemented.

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