Credit business management system and method

Abstract

The invention discloses a credit and loan business management system and method, relates to the technical field of credit and loan management, and solves the technical problems in the prior art that the business selected by a customer is difficult to undertake, repayment cannot be carried out on time, and economic loss of a financial institution is caused. The method comprises the following steps: collecting basic information of customers and grading the customers; recommending corresponding credit businesses to the clients based on the grades of the clients; the customer screens out a final business from the recommended credit business, sends an application to the final business, and approves the qualification of the customer to obtain approval information; creating a credit contract based on the approval information, determining a reminding date according to a repayment date in the credit contract, and sending reminding information to the client; calculating a risk coefficient of the customer and evaluating a risk level of the customer, and taking a corresponding monitoring measure based on the risk level of the customer; appropriate credit business can be recommended for clients, risks can be evaluated, and economic benefits of financial institutions can be guaranteed.

Description

Technical Field

[0001] The present invention belongs to the field of credit management, and relates to credit business management technology, in particular to a credit business management system and method. Background Art

[0002] Credit business refers to the loan service provided by banks or other financial institutions to customers. It is a financial service provided by financial institutions to customers and an important financial channel for customers to obtain funds. It mainly helps customers meet their funding needs and achieve financial goals. The automated and process-based credit management system can speed up loan approval and processing, reduce manual operations, and improve overall operational efficiency. Systematic data processing and analysis can quickly generate reports and statistical information to provide decision-making support for management. Through the credit management system, financial institutions can conduct a comprehensive assessment of customers' credit status and repayment ability, and reduce credit risks caused by information asymmetry.

[0003] The prior art (the invention patent application with publication number CN113538124A) discloses an integrated operation system for automated management of credit business, the system comprising an integrated operation platform, the integrated operation platform comprising a business processing system, a risk control system, a settlement system, a business operation system, a scheduling system, a reporting system, a collection system and a batch processing system, the business processing system is connected to the batch processing system and the evaluation module; the batch processing system is connected to the reporting system, the business processing system comprises a completed business module, a processing business module and a pending business module; the pending business module, the processing business module and the completed module can clearly distinguish the status of the business, the user's satisfaction with the business handling can be clearly known, and users who are not satisfied with the evaluation can be retained; however, the prior art analyzes the business status and the user's satisfaction with the business handling, but in credit services, it may happen that the business selected by the customer is difficult for the customer to bear, resulting in the inability to repay on time, causing economic losses to the financial institution.

[0004] The present invention provides a credit business management system and method to solve the above technical problems. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a credit business management system and method for solving the technical problem that the prior art analyzes the business status and user satisfaction with the business handling, but in the credit service, the customer may choose a business that is difficult for the customer to bear, resulting in the inability to repay on time, causing economic losses to the financial institution.

[0006] To achieve the above-mentioned purpose, the first aspect of the present invention provides a credit business management system, including: a business recommendation module, and an information collection module and a risk management module connected thereto;

[0007] Information collection module: used to collect basic information of customers and pre-process the basic information to obtain customer information; the basic information includes: identity proof, credit record, contact information and wealth income;

[0008] Business management module: used to classify customers according to their information and obtain their grades; recommend corresponding credit businesses to customers based on their grades; customers select final businesses from the recommended credit businesses and apply for the final businesses; based on their applications, the qualifications of customers are reviewed and approved to obtain approval information; and,

[0009] Create a credit contract based on the approval information, determine the reminder date based on the repayment date in the credit contract; send reminder information to the customer based on the reminder date;

[0010] Risk assessment module: used to obtain the customer's credit information, calculate the customer's risk coefficient based on the customer's credit information, evaluate the customer's risk level based on the customer's risk coefficient, and take corresponding monitoring measures based on the customer's risk level; among them, credit information includes: credit score, historical overdue days and income-to-liability ratio.

[0011] Preferably, the preprocessing of the basic information to obtain the customer information includes:

[0012] Retrieve the basic information of the customer and determine whether the basic information contains sub-information of all data types. If yes, retain the basic information of the corresponding customer; if no, complete the missing sub-information;

[0013] The retained basic information and the supplemented basic information are marked as customer information, and the customer information is stored in the customer database.

[0014] It should be noted that sub-information includes identity proof, credit record, contact information or wealth income; the integrity of basic information is analyzed.

[0015] The present invention judges the integrity of basic information and supplements the missing sub-information when there is missing sub-information in the basic information; the integrity of customer information can be ensured, and the customer information is stored in customer data, which is conducive to querying and managing customer information.

[0016] Preferably, the step of classifying customers according to customer information to obtain customer grades includes:

[0017] Retrieve the customer's customer information and mark the customer information as a level input sequence;

[0018] The grade classification model is called, and the grade input sequence is input into the grade classification model to obtain the customer's grade; wherein the grade classification model is constructed based on an artificial intelligence model.

[0019] The present invention classifies customers according to customer information to obtain customer grades, and can classify customers according to their actual conditions, thus laying a foundation for subsequent credit business recommendations.

[0020] Preferably, the hierarchical classification model is constructed based on an artificial intelligence model, including:

[0021] Acquire a standard data set; wherein the standard data set includes standard input data consistent with the level input sequence content attributes, and standard output data consistent with the level content attributes of the customer;

[0022] Divide the standard data set into a training set, a validation set and a test set; use the training set to train the artificial intelligence model, and use the validation set to adjust the parameters in the artificial intelligence model; use the test set to evaluate the trained and adjusted artificial intelligence model; when the parameters of the artificial intelligence model reach the standard threshold, mark the artificial intelligence model as a hierarchical classification model; otherwise, retrain the artificial intelligence model; wherein the artificial intelligence model includes a BP neural network model or a convolutional neural network model.

[0023] It should be noted that the parameters of the artificial intelligence model include: accuracy, recall rate, F1 score and stability.

[0024] Preferably, the recommending corresponding credit services to customers based on their grades includes:

[0025] Retrieve the customer's grade and obtain the grade credit database; match the customer's grade with the grade credit database to obtain a number of credit businesses corresponding to the customer's grade;

[0026] A number of credit services are integrated into a recommended credit service set, and the credit service set is sent to the customer.

[0027] The present invention matches the customer's grade with the grade credit database to obtain recommended credit services; the corresponding credit services are recommended to the customer, and suitable credit services can be provided to the customer based on the customer's basic situation, which is conducive to meeting the customer's needs and improving the customer's experience.

[0028] Preferably, the step of approving the customer's qualifications based on the customer's application to obtain approval information includes:

[0029] Obtain the customer's application information; the application information includes: personal information, financial information, and loan information; the data type of personal information is consistent with the customer information;

[0030] Retrieve the customer information database and match the personal information in the customer's application information with the customer information database; determine whether the personal information is consistent with the customer information. If yes, verify the customer's financial information and loan information; if not, verify the customer's personal information and store the personal information in the customer information database;

[0031] Approval information is generated based on the customer's verification results and loan information; the approval information includes: approval passed or approval failed, and loan information.

[0032] The present invention analyzes the qualifications of the customer based on the customer's application and verifies the customer's personal information. When the verification is successful, approval information is generated based on the verification result and the loan information. The identity of the customer applying for a loan can be verified to avoid impersonation by others, which is beneficial to safeguarding the rights and interests of the customer.

[0033] Preferably, determining the reminder date according to the repayment date in the credit contract includes:

[0034] Retrieve the credit contract and analyze the customer's repayment period according to the credit contract; determine the customer's repayment date based on the effective date of the contract and the repayment period;

[0035] The reminder threshold of the customer is analyzed based on the customer's level, and the corresponding reminder date is obtained by subtracting the reminder threshold from the repayment date.

[0036] The present invention analyzes the customer's repayment date according to the cycle in the credit contract, obtains the customer's reminder threshold according to the customer's level analysis, and calculates the reminder date according to the repayment date and the reminder threshold; the customer can be reminded according to the reminder date to avoid the customer forgetting to repay, thereby affecting the customer's reputation.

[0037] Preferably, the step of calculating the risk factor of the customer based on the customer's credit information includes:

[0038] Retrieve the customer's credit information and mark the credit score, historical overdue days, and income-to-liability ratio in the credit information as ZP, LT, and FB respectively;

[0039] The customer's risk factor is calculated by the formula FX=α / ZP+β×ln(LT+1)+γ×FB; where α, β and γ are proportional coefficients greater than 0.

[0040] It should be noted that the income-to-debt ratio is calculated by dividing the debt amount by the income amount in the same time period; the customer's risk factor is updated regularly according to the repayment cycle.

[0041] The present invention calculates the risk coefficient of the customer based on the customer's credit information, can evaluate the customer's risk, and provides data support for the subsequent classification of the customer's risk level.

[0042] Preferably, the step of evaluating the risk level of the customer according to the risk coefficient of the customer includes:

[0043] Retrieve the customer's risk factor and determine whether the risk factor is greater than the first-level factor threshold; if so, compare the risk factor with the second-level factor threshold; if not, determine the customer's risk level as low risk;

[0044] Determine whether the risk coefficient is greater than the secondary coefficient threshold; if yes, the customer's risk level is determined to be high risk; if no, the customer's risk is determined to be medium risk; the primary and secondary thresholds are assessed by experts; and the primary threshold is less than the secondary threshold.

[0045] The present invention compares the risk coefficient of a customer with the primary threshold and the secondary threshold respectively, and divides the risk level of the customer, which can facilitate financial institutions to increase their attention to high-risk customers according to the risk level of the customer, and is conducive to ensuring the economic benefits of the financial institutions.

[0046] A second aspect of the present invention provides a credit business management method, comprising:

[0047] Step S1: Collect basic information of customers and pre-process the basic information to obtain customer information;

[0048] Step S2: Classify the customer according to the customer information to obtain the customer's grade; recommend corresponding credit business to the customer based on the customer's grade; the customer selects the final business from the recommended credit business and applies for the final business; the customer's qualifications are approved based on the customer's application to obtain approval information;

[0049] Step S3: creating a credit contract based on the approval information, determining a reminder date based on the repayment date in the credit contract; and sending a reminder message to the customer based on the reminder date;

[0050] Step S4: Obtain the customer's credit information, calculate the customer's risk coefficient based on the customer's credit information, evaluate the customer's risk level based on the customer's risk coefficient, and take corresponding monitoring measures based on the customer's risk level.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] 1. The present invention judges the integrity of basic information, and when there is missing sub-information in the basic information, the missing sub-information is supplemented; it can ensure the integrity of customer information, and store customer information in customer data, which is conducive to querying and managing customer information; according to customer information, customers are classified to obtain customer grades; the customer grades are matched with grade credit databases to obtain recommended credit services; the corresponding credit services are recommended to customers, and suitable credit services can be provided to customers based on the basic conditions of customers, which is conducive to meeting customer needs and improving customer experience; the customer's qualifications are analyzed according to the customer's application, and the customer's personal information is verified. When the verification is successful, approval information is generated according to the verification results and loan information; the identity of customers applying for loans can be verified to avoid impersonation by others, which is conducive to safeguarding the rights and interests of customers.

[0053] 2. The present invention analyzes the customer's repayment date according to the cycle in the credit contract, obtains the customer's reminder threshold according to the customer's level analysis, and calculates the reminder date according to the repayment date and the reminder threshold; the customer can be reminded according to the reminder date to prevent the customer from forgetting to repay, thereby affecting the customer's reputation; the customer's risk coefficient is calculated according to the customer's credit information, and the customer's risk can be evaluated, providing data support for the subsequent classification of the customer's risk level; the customer's risk coefficient is compared with the first-level threshold and the second-level threshold respectively, and the customer's risk level is classified, which can facilitate financial institutions to increase their attention to high-risk customers according to the customer's risk level, which is beneficial to ensuring the economic benefits of financial institutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] 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.

[0055] Figure 1 A schematic diagram of the overall steps of the system of the present invention;

[0056] Figure 2 A schematic diagram of the credit business recommendation and application approval steps of the present invention;

[0057] Figure 3 A schematic diagram of the steps of customer risk assessment of the present invention;

[0058] Figure 4 It is a schematic diagram of the specific steps of the method of the present invention. DETAILED DESCRIPTION

[0059] 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, rather than 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] See also Figure 1 , the first aspect of the present invention provides a credit business management system, including: a business recommendation module, and an information collection module and a risk management module connected thereto;

[0061] Information collection module: used to collect basic information of customers and pre-process the basic information to obtain customer information; the basic information includes: identity proof, credit record, contact information and wealth income;

[0062] Business management module: used to classify customers according to their information and obtain their grades; recommend corresponding credit businesses to customers based on their grades; customers select final businesses from the recommended credit businesses and apply for the final businesses; based on their applications, the qualifications of customers are reviewed and approved to obtain approval information; and,

[0063] Create a credit contract based on the approval information, determine the reminder date based on the repayment date in the credit contract; send reminder information to the customer based on the reminder date;

[0064] Risk assessment module: used to obtain the customer's credit information, calculate the customer's risk coefficient based on the customer's credit information, evaluate the customer's risk level based on the customer's risk coefficient, and take corresponding monitoring measures based on the customer's risk level; among them, credit information includes: credit score, historical overdue days and income-to-liability ratio.

[0065] See also Figure 2 , collect the basic information of the customer, determine whether the basic information contains sub-information of all data types, if yes, retain the basic information of the corresponding customer; if no, complete the missing sub-information; mark the retained basic information and the completed basic information as customer information, and store the customer information in the customer database; retrieve the customer's customer information, mark the customer information as a grade input sequence; call the grade classification model, input the grade input sequence into the grade classification model, and obtain the customer's grade; wherein the grade classification model is built based on the artificial intelligence model;

[0066] For example: Assuming that there are three customers' basic information, the basic information of customers A and C includes all data types; customer B lacks contact information, so after the contact information is completed, the basic information will be marked as customer information and stored in the customer information library.

[0067] The customer information of the customers is marked as a grade input sequence, and the grade input sequence is input into the grade classification model, and the grades of the customers are obtained as follows: customer A is graded as third grade, customer B is graded as second grade, and customer C is graded as first grade; among which, the grades are ranked from high to low as first grade, second grade, and third grade.

[0068] Retrieve the customer's grade and obtain the grade credit database; match the customer's grade with the grade credit database to obtain several credit businesses corresponding to the customer's grade; integrate several credit businesses into a recommended credit business set, and send the credit business set to the customer; the customer selects the final business from the recommended credit businesses and applies for the final business.

[0069] For example: Assume that an existing customer B wants to conduct a credit business. According to the customer B's grade, it is second class. The customer B's grade is matched with the grade credit database, and the corresponding credit business is 4-8. Then the credit business 4-8 is recommended to customer B. Customer B selects the credit business 6 as the final credit business and submits a credit application to the system.

[0070] Obtain the customer's application information; wherein the application information includes: personal information, financial information and loan information; the data type of the personal information is consistent with the customer information; retrieve the customer information database, and match the personal information in the customer's application information with the customer information database; determine whether the personal information is consistent with the customer information, if so, verify the customer's financial information and loan information; if not, verify the customer's personal information and store the personal information in the customer information database; generate approval information based on the customer's verification results and loan information; wherein the approval information includes: approval or failure, and loan information.

[0071] For example: assuming there are applications from existing customers B, D and F, customer B's personal information can be matched with the customer information database, and the customer information is consistent, and other information is verified; if the information of customer D and customer F is inconsistent with the customer information database, the information of customer D and customer F is verified, and it is found that customer B and customer D pass the verification, while customer F fails the verification; then customer B and customer D pass the approval, while customer F fails the approval.

[0072] It should be noted that the hierarchical classification model is constructed based on an artificial intelligence model, including:

[0073] Acquire a standard data set; wherein the standard data set includes standard input data consistent with the level input sequence content attributes, and standard output data consistent with the level content attributes of the customer;

[0074] The standard data set is divided into a training set, a validation set and a test set; the training set is used to train the artificial intelligence model, and the validation set is used to adjust the parameters in the artificial intelligence model; the test set is used to evaluate the artificial intelligence model after training and adjustment; when the parameters of the artificial intelligence model reach the standard threshold, the artificial intelligence model is marked as a hierarchical classification model; otherwise, the artificial intelligence model is retrained; wherein the artificial intelligence model includes a BP neural network model or a convolutional neural network model; the parameters of the artificial intelligence model include: accuracy, recall rate, F1 score and stability.

[0075] See also Figure 3 , create a credit contract based on approval information; analyze the customer's repayment cycle according to the credit contract; determine the customer's repayment date based on the contract's effective date and the repayment cycle; analyze the customer's reminder threshold based on the customer's level, and subtract the reminder threshold from the repayment date to obtain the corresponding reminder date; send reminder information to the customer based on the reminder date.

[0076] For example, suppose that the repayment date of customer B is determined. According to the credit contract of customer B, the repayment cycle is monthly and the repayment date is the 20th of each month. Customer B's grade is second-class, and the reminder threshold is 3 days. Therefore, a reminder date for repayment will be sent to customer B on the 17th of each month.

[0077] The repayment date of customer C is determined. Through analysis of customer C's credit contract, it is found that the customer's repayment cycle is monthly and the repayment date is the 15th of each month. Customer C's grade is first-class, and the reminder threshold is 5 days. Therefore, a repayment reminder will be sent to customer C on the 10th of each month.

[0078] Obtain the credit information of the customer, and mark the credit score, historical overdue days and income-liability ratio in the credit information as ZP, LT and FB respectively; calculate the customer's risk factor through the formula FX = α / ZP + β × ln (LT + 1) + γ × FB; where α, β and γ are proportional coefficients greater than 0;

[0079] Retrieve the customer's risk coefficient and determine whether the risk coefficient is greater than the first-level coefficient threshold; if so, compare the risk coefficient with the second-level coefficient threshold; if not, determine the customer's risk level as low risk; determine whether the risk coefficient is greater than the second-level coefficient threshold; if so, determine the customer's risk level as high risk; if not, determine the customer's risk as medium risk; wherein, the first-level threshold and the second-level threshold are assessed by experts; and the first-level threshold is less than the second-level threshold; take corresponding monitoring measures based on the customer's risk level.

[0080] For example, suppose that the risk of customers A, B and C is evaluated, α = 0.5; β = 10; γ = 100; the specific analysis steps are as follows:

[0081] Customer A: Credit score ZP = 98; Historical overdue days LT = 3; Income-liability ratio FB = 0.4285; then the risk factor of customer A is FX = 1.12;

[0082] Customer B: Credit score ZP = 99; Historical overdue days LT = 0; Income-liability ratio FB = 0.25; then the risk factor of customer A is FX = 0.51;

[0083] Customer C: Credit score ZP = 94; Historical overdue days LT = 5; Income-liability ratio FB = 0.5; then the risk factor of customer A is FX = 1.45;

[0084] The first-level threshold is set to 1, and the second-level threshold is set to 1.2; therefore, customer A's risk level is medium risk; customer B's risk level is low risk, and customer C's risk level is high risk; customer B only needs to be sent one reminder SMS, customer A will be sent three repayment reminder SMS, and customer C will be supervised with a high level of attention. When the number of overdue repayments exceeds the threshold, customer C's account will be frozen and a warning message will be sent to customer C.

[0085] See also Figure 4 The second aspect of the present invention provides a credit business management method, including:

[0086] Step S1: Collect basic information of customers and pre-process the basic information to obtain customer information;

[0087] Step S2: Classify the customer according to the customer information to obtain the customer's grade; recommend corresponding credit business to the customer based on the customer's grade; the customer selects the final business from the recommended credit business and applies for the final business; the customer's qualifications are approved based on the customer's application to obtain approval information;

[0088] Step S3: creating a credit contract based on the approval information, determining a reminder date based on the repayment date in the credit contract; and sending a reminder message to the customer based on the reminder date;

[0089] Step S4: Obtain the customer's credit information, calculate the customer's risk coefficient based on the customer's credit information, evaluate the customer's risk level based on the customer's risk coefficient, and take corresponding monitoring measures based on the customer's risk level.

[0090] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0091] The working principle of the present invention is as follows: the present invention collects basic information of customers, pre-processes the basic information to obtain customer information; classifies customers according to the customer information to obtain customer grades; recommends corresponding credit services to customers based on the customer grades; customers screen out final services from the recommended credit services and apply for the final services; examines the qualifications of customers based on their applications and obtains approval information; creates a credit contract based on the approval information, and determines a reminder date based on the repayment date in the credit contract; sends reminder information to customers based on the reminder date; obtains customer credit information, calculates customer risk coefficient based on customer credit information, evaluates customer risk grade based on customer risk coefficient, and takes corresponding monitoring measures based on customer risk grade.

[0092] 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. Credit business management system, including: The business recommendation module, and the information collection module and risk management module connected thereto are characterized in that: Information collection module: used to collect basic information of customers and pre-process the basic information to obtain customer information; the basic information includes: identity proof, credit record, contact information and wealth income; Business management module: used to classify customers according to their information and obtain their grades; recommend corresponding credit businesses to customers based on their grades; customers select final businesses from the recommended credit businesses and apply for the final businesses; based on their applications, the qualifications of customers are reviewed and approved to obtain approval information; and, Create a credit contract based on the approval information, determine the reminder date based on the repayment date in the credit contract; send reminder information to the customer based on the reminder date; Risk assessment module: used to obtain the customer's credit information, calculate the customer's risk coefficient based on the customer's credit information, evaluate the customer's risk level based on the customer's risk coefficient, and take corresponding monitoring measures based on the customer's risk level; among them, credit information includes: credit score, historical overdue days and income-to-liability ratio.

2. The credit business management system according to claim 1, characterized in that: The preprocessing of basic information to obtain customer information includes: Retrieve the basic information of the customer and determine whether the basic information contains sub-information of all data types. If yes, retain the basic information of the corresponding customer; if no, complete the missing sub-information; The retained basic information and the supplemented basic information are marked as customer information, and the customer information is stored in the customer database.

3. The credit business management system according to claim 2, characterized in that: The step of classifying customers according to customer information to obtain customer grades includes: Retrieve the customer's customer information and mark the customer information as a level input sequence; The grade classification model is called, and the grade input sequence is input into the grade classification model to obtain the customer's grade; wherein the grade classification model is constructed based on an artificial intelligence model.

4. The credit business management system according to claim 3, characterized in that: The hierarchical classification model is constructed based on an artificial intelligence model, including: Acquire a standard data set; wherein the standard data set includes standard input data consistent with the level input sequence content attributes, and standard output data consistent with the level content attributes of the customer; Divide the standard data set into a training set, a validation set and a test set; use the training set to train the artificial intelligence model, and use the validation set to adjust the parameters in the artificial intelligence model; use the test set to evaluate the trained and adjusted artificial intelligence model; when the parameters of the artificial intelligence model reach the standard threshold, mark the artificial intelligence model as a hierarchical classification model; otherwise, retrain the artificial intelligence model; wherein the artificial intelligence model includes a BP neural network model or a convolutional neural network model.

5. The credit business management system according to claim 1, characterized in that: The recommending corresponding credit services to customers based on their grades includes: Retrieve the customer's grade and obtain the grade credit database; match the customer's grade with the grade credit database to obtain a number of credit businesses corresponding to the customer's grade; A number of credit services are integrated into a recommended credit service set, and the credit service set is sent to the customer.

6. The credit business management system according to claim 1, characterized in that: The client's qualifications are reviewed and approved based on the client's application, and the approval information is obtained, including: Obtain the customer's application information; the application information includes: personal information, financial information, and loan information; the data type of personal information is consistent with the customer information; Retrieve the customer information database and match the personal information in the customer's application information with the customer information database; determine whether the personal information is consistent with the customer information. If yes, verify the customer's financial information and loan information; if not, verify the customer's personal information and store the personal information in the customer information database; Approval information is generated based on the customer's verification results and loan information; the approval information includes: approval passed or approval failed, and loan information.

7. The credit business management system according to claim 1, characterized in that: The reminder date is determined based on the repayment date in the credit contract, including: Retrieve the credit contract and analyze the customer's repayment period according to the credit contract; determine the customer's repayment date based on the effective date of the contract and the repayment period; The reminder threshold of the customer is analyzed based on the customer's level, and the corresponding reminder date is obtained by subtracting the reminder threshold from the repayment date.

8. The credit business management system according to claim 1, characterized in that: The step of calculating the risk factor of a customer based on the customer's credit information includes: Retrieve the customer's credit information and mark the credit score, historical overdue days, and income-to-liability ratio in the credit information as ZP, LT, and FB respectively; The customer's risk factor is calculated by the formula FX=α / ZP+β×ln(LT+1)+γ×FB; where α, β and γ are proportional coefficients greater than 0.

9. The credit business management system according to claim 8, characterized in that: The risk level of the customer is assessed based on the customer's risk factor, including: Retrieve the customer's risk factor and determine whether the risk factor is greater than the first-level factor threshold; if so, compare the risk factor with the second-level factor threshold; if not, determine the customer's risk level as low risk; Determine whether the risk coefficient is greater than the secondary coefficient threshold; if yes, the customer's risk level is determined to be high risk; if no, the customer's risk is determined to be medium risk; the primary and secondary thresholds are assessed by experts; and the primary threshold is less than the secondary threshold.

10. A credit business management method, applied to the credit business management system according to any one of claims 1 to 9, characterized in that: include: Step S1: Collect basic information of customers and pre-process the basic information to obtain customer information; Step S2: Classify the customer according to the customer information to obtain the customer's grade; recommend corresponding credit business to the customer based on the customer's grade; the customer selects the final business from the recommended credit business and applies for the final business; the customer's qualifications are approved based on the customer's application to obtain approval information; Step S3: creating a credit contract based on the approval information, determining a reminder date based on the repayment date in the credit contract; and sending a reminder message to the customer based on the reminder date; Step S4: Obtain the customer's credit information, calculate the customer's risk coefficient based on the customer's credit information, evaluate the customer's risk level based on the customer's risk coefficient, and take corresponding monitoring measures based on the customer's risk level.

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