A method for constructing a financial risk control prediction model

Abstract

The present invention discloses a method for constructing a financial risk control prediction model, which relates to the technical field of financial risk control prediction. A data collection module is used to collect and store characteristics related to the current customer in this evaluation cycle. A prediction model constructed using the customer characteristic evaluation module is used to sequentially calculate and output a risk score F, a risk warning index FY, and a dynamic risk adjustment coefficient DT. The customer characteristic evaluation module is also used to evaluate and update the interest rate adjustment coefficient V for the next evaluation cycle of the current customer. The present invention comprehensively considers multi-dimensional risk factors, introduces square root operations to enhance nonlinear relationships, flexibly adjusts the interest rate adjustment coefficient V, combines historical and current risk information, quantifies the severity of overdue payments, considers the combined impact of credit score P and repayment capacity HN, introduces the latest credit score P for dynamic adjustment, and forms a cyclical influence mechanism. These innovations are intended to improve the accuracy and flexibility of risk prediction and provide more effective risk control support for financial institutions.

Description

Technical Field

[0001] The present invention relates to the technical field of financial risk control prediction, and in particular to a method for constructing a financial risk control prediction model. Background Art

[0002] With the rapid development of financial technology, financial institutions have an increasing demand for risk management. Traditional risk management methods mainly rely on manual review and simple statistical models, which are difficult to cope with large-scale, high-frequency transactions and complex market environments. Therefore, financial institutions have begun to adopt advanced financial risk control prediction models to improve the efficiency and accuracy of risk management. The widespread application of big data and artificial intelligence technologies has provided strong support for the construction of financial risk control prediction models. Financial institutions can collect and integrate data from different channels to form huge data sets. Through big data analysis and artificial intelligence technologies, financial institutions can process and analyze these data in real time and uncover potential risk points and fraudulent behaviors.

[0003] However, some current existing technologies mainly rely on static data for analysis, but ignore the dynamic changes in customer credit status, resulting in inaccurate risk assessment results. In addition, some existing risk control models are usually based on fixed rules and linear relationships for prediction, which makes it difficult to adapt to the complex and changing market environment and customer credit status. In addition, existing technologies lack an effective risk warning mechanism and are unable to timely predict and identify potential risk events. In addition, a large amount of useful customer information is not fully utilized, resulting in limited predictive capabilities of risk control models. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for constructing a financial risk control prediction model, which solves the problems raised in the above background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solutions, and the specific implementation steps are as follows:

[0006] Step 1: Use the data collection module to collect and store the characteristics related to the current customer in this evaluation cycle;

[0007] Step 2: Use the prediction model built by the customer feature assessment module to calculate the output risk score F, risk warning index FY, and dynamic risk adjustment coefficient DT in sequence;

[0008] Step 3: Based on the dynamic risk adjustment coefficient DT and using the customer feature assessment module, evaluate and update the interest rate adjustment coefficient V for the next assessment period for the current customer;

[0009] Step 4: using the monitoring module and the customer feature evaluation module to regularly monitor and update the evaluation prediction output by the customer feature evaluation module;

[0010] The customer characteristics assessment module includes a unit for measuring the customer's credit risk level, a unit for predicting the degree of risk faced by the customer in the future, and a unit for adjusting the risk based on the customer's latest credit;

[0011] The devices used by the data collection module include storage devices;

[0012] The equipment used by the customer feature evaluation module includes a server;

[0013] The equipment used by the monitoring module includes monitoring tools.

[0014] Optionally, the calculation formula for the unit for measuring the customer credit risk level is as follows:

[0015] ;

[0016] in:

[0017] F is the risk score;

[0018] DE is the loan amount, which represents the total loan amount applied for by the customer;

[0019] YC is the number of overdue payments. YC indicates the number of times a customer has overdue payments during this evaluation period.

[0020] WD is the outstanding loan amount, which indicates the number of outstanding loans currently held by the customer;

[0021] P is the credit score;

[0022] V is the interest rate adjustment coefficient.

[0023] Optionally, the credit score P is calculated as follows:

[0024] ;

[0025] ;

[0026] HC is the total number of repayments, which indicates the total number of repayments a customer needs to make during this evaluation period.

[0027] YT is the number of days overdue, which represents the total number of days the customer is overdue on all loans;

[0028] Q is the loan term, which represents the total term of the customer's current loan amount DE;

[0029] T avg is the average repayment period in days;

[0030] n is the repayment period;

[0031] T1 is the repayment period for the first cycle, T2 is the repayment period for the second cycle, and T3 is the repayment period for the third cycle. n is the number of repayment days in the nth cycle;

[0032] Z is the total client assets;

[0033] A1, A2 and A3 are weight coefficients;

[0034] A1 focuses on the punctuality of repayment records;

[0035] A2 focuses on the length of overdue days;

[0036] A3 focuses on the matching degree between the loan amount DE and the customer's financial capacity;

[0037] A1, A2 and A3 are set according to the risk preferences and business strategies of the financial institution and satisfy the condition A1+A2+A3=1. If the risk preferences and business strategies of the financial institution are ranked as A2>A1>A3, then A1 is 0.3, A2 is 0.5 and A3 is 0.2.

[0038] Optionally, the calculation formula for the unit for predicting the degree of risk faced by the customer in the future is as follows:

[0039] ;

[0040] ;

[0041] in:

[0042] FY is the risk warning index;

[0043] HN is repayment capacity;

[0044] IN is the customer’s monthly income;

[0045] I0 is the customer's monthly expenditure;

[0046] YC / DE represents the ratio of the number of overdue payments (YC) to the loan amount (DE), and is used to measure the severity of the overdue payment.

[0047] It represents the impact of the interest rate adjustment coefficient V and the number of outstanding loans WD on the risk warning index FY. At the same time, the credit score P and repayment ability HN are introduced to conduct a comprehensive assessment of the impact.

[0048] Optionally, the calculation formula for adjusting the risk unit based on the customer's latest credit is as follows:

[0049] ;

[0050] in:

[0051] DT is the dynamic risk adjustment factor;

[0052] P lasttime Credit score for the previous assessment cycle;

[0053] YC max is the maximum number of overdue payments, YC max Indicates the maximum number of overdue payments allowed by the financial institution for the customer during this assessment period;

[0054] Indicates the degree of impact of changes in credit score P on the dynamic risk adjustment coefficient DT;

[0055] Represents the risk score F, the number of overdue payments YC relative to the maximum number of overdue payments YC max The proportion of and the impact of the loan amount DE on the dynamic risk adjustment coefficient DT.

[0056] Optionally, based on the dynamic risk adjustment coefficient DT, the calculation formula for adjusting the interest rate adjustment coefficient V for the next assessment period is as follows:

[0057] ;

[0058] V new is the adjusted interest rate adjustment factor;

[0059] a is the adjustment coefficient, which represents the degree of influence of the dynamic risk adjustment coefficient DT on the interest rate adjustment coefficient V. This coefficient needs to be set based on the risk preferences and business strategies of the financial institution.

[0060] Optionally, the adjustment coefficient a is set as follows:

[0061] If a financial institution needs to control risks more strictly, the adjustment coefficient a can be in the range of {0.5≤a≤1.0};

[0062] If a financial institution needs to flexibly adjust interest rates to attract customers, the value range of the adjustment coefficient a is {0.01≤a≤0.5}.

[0063] Optionally, the monitoring module regularly collects the same loan amount DE of m customers and the risk score F in the same assessment cycle, and uses the customer feature assessment module to sequentially calculate and evaluate the accuracy, recall rate, and F1 score indicators of the constructed prediction model;

[0064] The risk score F is divided into the risk score F calculated and output by the constructed prediction model, and the risk score F that has occurred and is calculated and output based on the actual situation.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] 1. The present invention introduces the factors of changes in credit score P, repayment capacity HN, and dynamic risk adjustment coefficient DT, so that the constructed prediction model can more comprehensively assess the customer's credit risk level, thereby improving the accuracy of risk assessment. Specifically, the result value risk score F of the unit measuring the customer's credit risk level comprehensively considers the loan amount DE, number of overdue payments YC, number of outstanding loans WD, and credit score P. Through square root operations and multiplication operations, static data and dynamic factors are combined to obtain a more accurate risk score F. The result value risk warning index FY of the unit predicting the degree of risk faced by the customer in the future further considers the ratio of the number of overdue payments YC to the loan amount DE and the impact of the interest rate adjustment coefficient V and the number of outstanding loans WD on the risk warning index FY. The accuracy of risk warning is improved through weighted sum square root operations. Finally, the result value dynamic risk adjustment coefficient DT of the unit adjusting the risk based on the customer's latest credit adjusts the risk level of the loan according to the customer's latest credit status. By introducing the changes in credit score P and the number of overdue payments YC relative to the maximum number of overdue payments YC max The proportional factor realizes the dynamic adjustment and prediction of risks.

[0067] 2. The prediction model constructed by the present invention adopts nonlinear relationships for prediction, which can adapt more flexibly to the complex and changing market environment and customer credit status. Through the cyclical influence mechanism, the constructed prediction model can adjust the risk assessment results in real time according to changes in customer credit status, thereby improving the flexibility and adaptability of the model.

[0068] 3. The present invention calculates the risk warning index FY, so that the constructed prediction model can predict and identify potential risk events in advance, providing financial institutions with timely risk warning information.

[0069] 4. The prediction model constructed by the present invention makes full use of the useful information of the customer's loan limit DE, number of overdue payments YC, number of outstanding loans WD, credit score P, repayment ability HN and the latest credit score, thereby improving the data utilization and the prediction ability of the risk control model. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A flow chart of the method for constructing this financial risk control prediction model;

[0071] Figure 2 This is a flow chart of the monitoring and evaluation method used in this financial risk control prediction model construction method;

[0072] Figure 3 Schematic diagram of the structure of the customer feature evaluation module of the present invention;

[0073] Figure 4 Schematic diagram of risk adjustment within the cyclic feedback cycle of the present invention. DETAILED DESCRIPTION

[0074] 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0075] Regarding the construction method of this financial risk control prediction model, it is different from the existing financial risk control prediction model construction method. The existing financial risk control prediction model construction method ignores the dynamic changes in the customer's credit status and is difficult to adapt to the complex and changing market environment and customer credit status. In addition, the existing technology lacks an effective risk warning mechanism, which limits the predictive ability of the risk control model. The algorithm unit comprehensively considers multi-dimensional risk factors, introduces square root operations to enhance nonlinear relationships, flexibly adjusts the interest rate adjustment coefficient V, combines historical and current risk information, quantifies the severity of overdue payments, considers the combined impact of credit score P and repayment ability HN, introduces the latest credit score P for dynamic adjustment, and forms a circular influence mechanism. These innovations are aimed at improving the accuracy and flexibility of risk prediction and providing financial institutions with more effective risk control support.

[0076] For example 1, please refer to Figures 1 to 4 This implementation provides a method for building a financial risk control prediction model. The specific implementation steps are as follows:

[0077] Step 1: Use the data collection module to collect and store the characteristics related to the current customer in this evaluation cycle;

[0078] Step 2: Use the prediction model built by the customer feature assessment module to calculate the output risk score F, risk warning index FY, and dynamic risk adjustment coefficient DT in sequence;

[0079] Step 3: Based on the dynamic risk adjustment coefficient DT and using the customer feature assessment module, evaluate and update the interest rate adjustment coefficient V for the next assessment period for the current customer;

[0080] Step 4: Use the monitoring module and the customer feature evaluation module to regularly monitor and update the evaluation forecast output by the customer feature evaluation module;

[0081] The customer characteristics assessment module includes a unit for measuring the customer's credit risk level, a unit for predicting the customer's future risk level, and a unit for adjusting risk based on the customer's latest credit.

[0082] The devices used by the data collection module include storage devices;

[0083] The equipment used in the customer characteristics assessment module includes a server;

[0084] The equipment used by the monitoring module includes monitoring tools;

[0085] The monitoring module regularly collects the same loan amount DE of m customers and the risk score F of the same assessment cycle, and uses the customer feature evaluation module to calculate and evaluate the accuracy, recall rate, and F1 score indicators of the constructed prediction model in sequence;

[0086] The risk score F is divided into the risk score F calculated and output by the constructed prediction model, and the risk score F that has occurred and is calculated and output based on the actual situation.

[0087] In this embodiment, the system, through the mutual cooperation of three algorithm units, as well as different methods and combinations, jointly affects the calculation results of the final risk score F, risk warning index FY and dynamic risk adjustment coefficient DT, and combines the three calculation results of F, FY and DT to provide financial institutions with more accurate and comprehensive customer credit risk assessment information, thereby helping financial institutions make more informed loan decisions. Specifically, F is the risk score, and the risk score F is an important basis for financial institutions to conduct risk assessment and loan approval, and this algorithm unit can generate a relatively accurate risk score F to help financial institutions judge the credit risk of customers. FY is the risk warning index, and this index can more comprehensively reflect the customer's credit status and repayment ability HN, providing financial institutions with more accurate and comprehensive customer credit risk assessment information. Provide earlier risk warning signals, so that they can take corresponding risk management measures in advance. DT is the dynamic risk adjustment coefficient, which can reflect changes in customer credit status in real time and dynamically adjust the risk level of the loan according to these changes, making the risk management of financial institutions more flexible and accurate. The calculation results of DT can also affect the calculation of F and FY, so that the three algorithms of this system together constitute a complete risk assessment system and provide financial institutions with comprehensive, accurate and flexible risk management tools. Through the comprehensive use of these three algorithm units, financial institutions can more accurately assess the credit risk level of customers, predict potential risk events, and dynamically adjust the risk level of loans according to the latest credit status of customers, thereby optimizing risk management strategies and improving risk management effects.

[0088] See also Figures 1 to 4 , the calculation formula for measuring the customer credit risk level unit is as follows:

[0089] ;

[0090] in:

[0091] F is the risk score;

[0092] DE is the loan amount, which represents the total loan amount applied for by the customer;

[0093] YC is the number of overdue payments. YC indicates the number of times a customer has overdue payments during this evaluation period.

[0094] WD is the outstanding loan amount, which indicates the number of outstanding loans currently held by the customer;

[0095] P is the credit score;

[0096] V is the interest rate adjustment coefficient.

[0097] In this embodiment: First, the calculation part "[DE × (YC + WD)" in this algorithm unit is intended to comprehensively consider the impact of the loan amount DE, the number of overdue payments YC, and the number of outstanding loans WD on the risk score. The larger the loan amount DE, the more overdue payments TC, and the more outstanding loans WD, the higher the customer's credit risk. As an important factor in the calculation of the risk score F, this calculation part combines the three key indicators of loan amount DE, overdue payments YC, and outstanding loans WD in a weighted manner to jointly influence the final risk score result;

[0098] The "SQRT(P)" calculation part takes the square root of the credit score P, which can smooth the changes in the credit score P and make the differences between different credit scores P more reasonable. At the same time, the square root operation can also maintain the positive impact of the credit score P on the risk score F. As a multiplier factor in the calculation of the risk score F, the square root of the credit score P can more accurately reflect the impact of the customer's credit status on the risk score. In addition, the interest rate adjustment coefficient V acts as a subtrahend to reflect the direct impact of the interest rate adjustment on the risk score F.

[0099] This algorithm unit adjusts the value of the interest rate adjustment coefficient V, allowing financial institutions to flexibly adjust risk scores based on changes in the market environment and customer credit status, thereby more accurately reflecting the customer's credit risk level. The introduction of the interest rate adjustment coefficient V also allows the risk score F to be flexibly adjusted based on changes in market interest rates and customer credit status, improving the timeliness and accuracy of risk assessment.

[0100] The expression for measuring a customer's credit risk level is concise and easy to understand and apply, making it easier for financial institutions to calculate the risk score F in real-world operations. The use of the square root operation "SQRT(P)" enhances the model's ability to handle nonlinearities in the credit score P. In practice, the relationship between the credit score P and the risk level is often nonlinear, so this approach can improve the model's accuracy.

[0101] Based on the above, the unit for measuring customer credit risk level can more comprehensively assess the customer's credit risk level and avoid assessment bias caused by a single indicator.

[0102] See also Figures 1 to 4 , the calculation formula for predicting the risk level of customers in the future is as follows:

[0103] ;

[0104] ;

[0105] in:

[0106] FY is the risk warning index;

[0107] HN is repayment capacity;

[0108] IN is the customer’s monthly income;

[0109] I0 is the customer's monthly expenditure;

[0110] YC / DE represents the ratio of the number of overdue payments (YC) to the loan amount (DE), and is used to measure the severity of the overdue payment.

[0111] It represents the impact of the interest rate adjustment coefficient V and the number of outstanding loans WD on the risk warning index FY. At the same time, the credit score P and repayment ability HN are introduced to conduct a comprehensive assessment of the impact.

[0112] In this embodiment, the "YC / DE" calculation section first calculates the ratio of the number of overdue payments YC to the loan amount DE, which is used to measure the severity of the overdue payment. The higher this ratio is, the higher the customer's credit risk is. As a multiplier factor in the calculation of the risk warning index FY, "YC / DE" can more accurately reflect the impact of the number of overdue payments YC on the risk warning index FY.

[0113] The calculation section aims to comprehensively consider the impact of credit score P, repayment capacity HN, interest rate adjustment coefficient V, and outstanding loan amount WD on the risk warning index FY. Through square root operation and smoothing, this calculation section can more accurately reflect the customer's overall credit status and repayment ability. As a subtracting factor in the calculation of the risk warning index FY, this calculation section combines the four key indicators of credit score P, repayment capacity HN, interest rate adjustment coefficient V, and outstanding loan amount WD through weighted and smoothing methods, thereby jointly influencing the final risk warning index FY result.

[0114] In this embodiment, the algorithm unit, by comprehensively considering multiple factors, can provide early warning of possible future risk events facing customers, providing financial institutions with sufficient time to respond to the risks. Furthermore, the interest rate adjustment coefficient V and the outstanding loan amount WD factors in the unit for predicting the degree of risk faced by customers in the future enable risk assessments to be dynamically adjusted as the customer's credit status changes, thereby improving the flexibility and accuracy of risk assessments.

[0115] In addition, by introducing the key factor of repayment capacity HN, the unit for predicting the degree of risk faced by customers in the future can more accurately assess the customer's repayment ability, thereby enhancing the reliability of risk warning;

[0116] It is worth noting that in the unit for predicting the degree of risk faced by customers in the future, the severity of overdue payments is quantified by calculating the ratio of the number of overdue payments YC to the loan amount DE. This quantification method enables the model to more accurately assess the impact of overdue payments on customer credit risk. In addition, the combined impact of credit score P and repayment ability HN on the risk warning index FY is also considered. This processing method enables the model to more comprehensively evaluate the impact of customers' repayment ability and credit status on risk warning.

[0117] See also Figures 1 to 4 , the calculation formula for adjusting the risk unit based on the customer's latest credit is as follows:

[0118] ;

[0119] in:

[0120] DT is the dynamic risk adjustment factor;

[0121] P lasttime Credit score for the previous assessment cycle;

[0122] YC max is the maximum number of overdue payments, YC max Indicates the maximum number of overdue payments allowed by the financial institution for the customer during this assessment period;

[0123] Indicates the degree of impact of changes in credit score P on the dynamic risk adjustment coefficient DT;

[0124] Represents the risk score F, the number of overdue payments YC relative to the maximum number of overdue payments YC max The proportion of and the impact of the loan amount DE on the dynamic risk adjustment coefficient DT.

[0125] In this embodiment, the algorithm unit first The calculation part is to measure the change of credit score P by comparing the latest credit score P with the credit score P of the previous assessment cycle.lasttime , which can reflect changes in the customer's credit status. As a multiplier factor in the calculation of the dynamic risk adjustment coefficient DT, this calculation part can more accurately reflect the impact of changes in the customer's credit status on the dynamic risk adjustment coefficient DT;

[0126] The calculation part is to comprehensively consider the loan amount DE, risk score F and the number of overdue payments YC relative to the maximum number of overdue payments YC max The impact of the proportion of the loan amount DE, risk score F and number of overdue payments YC on the dynamic risk adjustment coefficient DT. By adjusting the value of this calculation part, the changes in the customer's credit risk can be more flexibly reflected. As an addend factor in the calculation of the dynamic risk adjustment coefficient DT, this calculation part combines the three key indicators of loan amount DE, risk score F and number of overdue payments YC through weighted and proportional adjustment, and jointly affects the final dynamic risk adjustment coefficient DT result;

[0127] In this algorithm unit, the latest credit score P and the credit score P of the previous evaluation cycle are introduced. lasttime Key factors enable the risk unit adjusted according to the customer's latest credit to reflect changes in the customer's credit status in real time, providing financial institutions with the latest risk assessment results. By comprehensively considering multiple factors, the risk unit adjusted according to the customer's latest credit can more accurately assess the customer's risk level and dynamically adjust the loan risk level based on the assessment results, thereby optimizing the effectiveness of risk management;

[0128] It is worth noting that the risk unit is calculated based on the customer's latest credit adjustment. , and then considers the impact of changes in credit score P on the dynamic risk adjustment coefficient DT. This processing method enables the model to more comprehensively assess the impact of historical changes in the customer's credit status and the current status on risk. The result value DT of the customer's latest credit adjustment risk unit can be used to adjust the interest rate adjustment coefficient V in the unit measuring the customer's credit risk level, thus forming a circular influence mechanism. This mechanism enables the model to more flexibly adapt to changes in the customer's credit status and improve the accuracy and flexibility of risk prediction;

[0129] To sum up, the innovations in the methods of constructing financial risk control prediction models, namely, the unit for measuring customer credit risk level, the unit for predicting the degree of risk faced by customers in the future, and the unit for adjusting risk based on customers' latest credit, are mainly reflected in the comprehensive consideration of multi-dimensional risk factors, the introduction of square root operations to enhance nonlinear relationships, the flexible adjustment of interest rate adjustment coefficients, the combination of historical and current risk information, the quantification of the severity of overdue payments, the consideration of the combined impact of credit ratings and repayment ability, the introduction of the latest credit ratings for dynamic adjustment, and the formation of a cyclical influence mechanism. These innovations aim to improve the accuracy and flexibility of risk prediction and provide financial institutions with more effective risk control support.

[0130] See also Figures 1 to 4 Based on the dynamic risk adjustment coefficient DT, the calculation formula for adjusting the interest rate adjustment coefficient V for the next assessment period is as follows:

[0131] ;

[0132] V new is the adjusted interest rate adjustment factor;

[0133] a is the adjustment coefficient, which represents the degree of influence of the dynamic risk adjustment coefficient DT on the interest rate adjustment coefficient V. This coefficient needs to be set according to the risk appetite and business strategy of the financial institution.

[0134] The setting of adjustment coefficient a is as follows:

[0135] If a financial institution needs to control risks more strictly, the adjustment coefficient a can be in the range of {0.5≤a≤1.0};

[0136] If a financial institution needs to flexibly adjust interest rates to attract customers, the value range of the adjustment coefficient a is {0.01≤a≤0.5}.

[0137] In this embodiment, the algorithm unit uses a cyclic influence mechanism to enable the constructed prediction model to adjust the risk assessment results and loan risk level in real time according to changes in the customer's credit status, thereby enhancing the flexibility of the model;

[0138] Specifically, the cyclical impact mechanism enables the model to comprehensively consider data and factors at multiple time points, thereby more accurately assessing the customer's credit risk level. Through the cyclical impact mechanism, financial institutions can dynamically adjust risk management strategies based on the latest risk assessment results to better respond to potential risks. When financial institutions want to control risks more strictly, they can choose a large adjustment coefficient a. In this way, a small change in the dynamic risk adjustment coefficient DT will lead to a significant change in the interest rate adjustment coefficient V. This setting helps financial institutions respond quickly when risk signals first appear and reduce potential losses by adjusting interest rates. On the contrary, when financial institutions want to adjust interest rates more flexibly to attract customers, they can choose a small adjustment coefficient a. In this way, even if the dynamic risk adjustment coefficient DT changes significantly, the change in the interest rate adjustment coefficient V is relatively limited. This setting enables financial institutions to respond more flexibly to changes in market competition and customer demand while maintaining a certain level of risk control.

[0139] By setting a reasonable adjustment coefficient a, financial institutions can more accurately price based on the customer's credit status and risk level. This helps financial institutions maintain profitability while improving customer satisfaction and loyalty. Setting the adjustment coefficient a can also help financial institutions find a balance between risk and return. A large adjustment coefficient a means higher risk control requirements, but it may also lead to the loss of some high-quality customers. A small adjustment coefficient a helps attract more customers but also increases potential risks. Therefore, financial institutions need to select an appropriate adjustment coefficient a based on their own circumstances and market environment, as well as the value range of the adjustment coefficient a.

[0140] By combining the dynamic risk adjustment coefficient DT with the adjustment coefficient a, financial institutions can achieve automated and intelligent risk management. This allows them to more efficiently monitor and assess their customers' credit risk profiles and take timely measures to reduce risk. Automated risk management can also help financial institutions reduce operating costs. By reducing manual intervention and error rates, financial institutions can more efficiently process large amounts of customer information and reduce losses caused by improper risk management.

[0141] A reasonable setting of the adjustment coefficient a helps financial institutions promote product innovation. By flexibly adjusting interest rate pricing strategies, financial institutions can develop financial products that better meet market demand and customer preferences, thereby enhancing their market competitiveness. Furthermore, a reasonable setting of the adjustment coefficient a can provide strong support for the business development of financial institutions. By optimizing interest rate pricing strategies, financial institutions can attract more high-quality customers, expand market share, and promote sustained business growth.

[0142] To sum up, the setting of the value range of the adjustment coefficient α and the adjustment calculation formula combined with the interest rate adjustment coefficient V have many beneficial effects on financial institutions. These effects not only help financial institutions enhance the flexibility of risk control, optimize interest rate pricing strategies, and improve risk management efficiency, but also promote business innovation and development. Therefore, in actual applications, financial institutions need to select the appropriate adjustment coefficient a based on their own circumstances and market environment, and continuously optimize and adjust interest rate pricing strategies to cope with market changes.

[0143] For example 2, please refer to Figures 1 to 4 , the calculation formula of credit score P is as follows:

[0144] ;

[0145] ;

[0146] HC is the total number of repayments, which indicates the total number of repayments a customer needs to make during this evaluation period.

[0147] YT is the number of days overdue, which represents the total number of days the customer is overdue on all loans;

[0148] Q is the loan term, which represents the total term of the customer's current loan amount DE;

[0149] T avg is the average repayment period in days;

[0150] n is the repayment period;

[0151] T1 is the repayment period for the first cycle, T2 is the repayment period for the second cycle, and T3 is the repayment period for the third cycle. n is the number of repayment days in the nth cycle;

[0152] Z is the total client assets;

[0153] A1, A2 and A3 are weight coefficients;

[0154] A1 focuses on the punctuality of repayment records;

[0155] A2 focuses on the length of overdue days;

[0156] A3 focuses on the matching degree between the loan amount DE and the customer's financial capacity;

[0157] A1, A2 and A3 are set according to the risk preferences and business strategies of the financial institution and satisfy the condition A1+A2+A3=1. If the risk preferences and business strategies of the financial institution are ranked as A2>A1>A3, then A1 is 0.3, A2 is 0.5 and A3 is 0.2.

[0158] In this embodiment, A1 focuses on the punctuality of repayment records. This percentage is directly related to the credit score P. This percentage is calculated using a logarithmic function and aims to more accurately reflect the negative impact of overdue payments YC on the credit score P. Therefore, the larger A1 is, the more the financial institution values ​​the customer's timely repayment. If the customer has a good repayment record and few overdue payments, the score for this part will be higher, thereby improving the overall credit score.

[0159] A2 focuses on the length of overdue days. A2 and “overdue days” account for This ratio reflects the ratio of the customer's overdue time to the loan term Q. The setting of A2 reflects the financial institution's attention to the length of the overdue days YT. If the customer is overdue for a short period of time, the score for this part may not be too low. On the contrary, if the overdue days are long, the score will be significantly reduced.

[0160] A3 focuses on the matching degree between the loan amount and the customer's financial capacity. A3 is related to the inverse of "(DE / Z)". This ratio reflects whether the customer's loan amount matches his or her financial capacity. The setting of A3 shows the degree to which financial institutions attach importance to the relationship between the loan amount and the customer's actual financial capacity. If the customer's loan amount is too high, exceeding his or her financial capacity, the score of this part will be low. Conversely, if the loan amount is moderate and matches the customer's financial capacity, the score will be high.

[0161] It should be noted that although A1, A2 and A3 each focus on different aspects, their roles in the comprehensive credit score are interrelated. When setting these weight coefficients, financial institutions need to comprehensively consider various factors to ensure that the credit score can comprehensively and accurately reflect the customer's credit status. At the same time, these weight coefficients also need to be adjusted in a timely manner according to changes in the market environment and business strategies.

[0162] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for constructing a financial risk control prediction model, characterized in that: The specific implementation steps are as follows: Step 1: Use the data collection module to collect and store the characteristics related to the current customer in this evaluation cycle; Step 2: Use the prediction model built by the customer feature assessment module to calculate the output risk score F, risk warning index FY, and dynamic risk adjustment coefficient DT in sequence; Step 3: Based on the dynamic risk adjustment coefficient DT and using the customer feature assessment module, evaluate and update the interest rate adjustment coefficient V for the next assessment period for the current customer; Step 4: using the monitoring module and the customer feature evaluation module to regularly monitor and update the evaluation prediction output by the customer feature evaluation module; The customer characteristics assessment module includes a unit for measuring the customer's credit risk level, a unit for predicting the degree of risk faced by the customer in the future, and a unit for adjusting the risk based on the customer's latest credit; The calculation formula for the unit for measuring customer credit risk level is as follows: ； in: F is the risk score; DE is the loan amount, which represents the total loan amount applied for by the customer; YC is the number of overdue payments. YC indicates the number of times a customer has overdue payments during this evaluation period. WD is the outstanding loan amount, which indicates the number of outstanding loans currently held by the customer; P is the credit score; V is the interest rate adjustment coefficient; The calculation formula of the credit score P is as follows: ； ； HC is the total number of repayments, which indicates the total number of repayments a customer needs to make during this evaluation period. YT is the number of days overdue, which represents the total number of days the customer is overdue on all loans; Q is the loan term, which represents the total term of the customer's current loan amount DE; T avg is the average repayment period in days; n is the repayment period; T1 is the repayment period for the first cycle, T2 is the repayment period for the second cycle, and T3 is the repayment period for the third cycle. n is the number of repayment days in the nth cycle; Z is the total client assets; A1, A2 and A3 are weight coefficients; A1 focuses on the punctuality of repayment records; A2 focuses on the length of overdue days; A3 focuses on the matching degree between the loan amount DE and the customer's financial capacity; The calculation formula for the unit for predicting the degree of risk faced by customers in the future is as follows: ； ； in: FY is the risk warning index; HN is repayment capacity; IN is the customer’s monthly income; IO is the customer's monthly expenditure; It represents the ratio of the number of overdue payments YC to the loan amount DE, and is used to measure the severity of the overdue payment; It represents the impact of the interest rate adjustment coefficient V and the number of outstanding loans WD on the risk warning index FY. At the same time, the credit score P and repayment capacity HN are introduced to conduct a comprehensive assessment of the impact. In the calculation formula for the credit score P, A1, A2, and A3 are set based on the risk appetite and business strategy of the financial institution, and satisfy the condition of A1+A2+A3=1. If the risk appetite and business strategy of the financial institution prioritize A2>A1>A3, then A1 is 0.3, A2 is 0.5, and A3 is 0.

2. The calculation formula for adjusting the risk unit based on the customer's latest credit is as follows: ； in: DT is the dynamic risk adjustment factor; P lasttime Credit score for the previous assessment cycle; YC max is the maximum number of overdue payments, YC max Indicates the maximum number of overdue payments allowed by the financial institution for the customer during this assessment period; Indicates the degree of impact of changes in credit score P on the dynamic risk adjustment coefficient DT; Represents the risk score F, the number of overdue payments YC relative to the maximum number of overdue payments YC max The proportion of and the impact of the loan amount DE on the dynamic risk adjustment coefficient DT.

2. A method for constructing a financial risk control prediction model according to claim 1, characterized in that: The devices used by the data collection module include storage devices; The equipment used by the customer feature evaluation module includes a server; The equipment used by the monitoring module includes monitoring tools.

3. A method for constructing a financial risk control prediction model according to claim 2, characterized in that: Based on the dynamic risk adjustment coefficient DT, the calculation formula for adjusting the interest rate adjustment coefficient V for the next assessment period is as follows: ； V new is the adjusted interest rate adjustment factor; a is the adjustment coefficient, which represents the degree of influence of the dynamic risk adjustment coefficient DT on the interest rate adjustment coefficient V. This coefficient needs to be set based on the risk preferences and business strategies of the financial institution.

4. A method for constructing a financial risk control prediction model according to claim 3, characterized in that: The adjustment coefficient a is set as follows: If a financial institution needs to control risks more strictly, the adjustment coefficient a can be in the range of {0.5≤a≤1.0}; If a financial institution needs to flexibly adjust interest rates to attract customers, the value range of the adjustment coefficient a is {0.01≤a<0.5}.

5. A method for constructing a financial risk control prediction model according to claim 4, characterized in that: The monitoring module regularly collects the same loan amount DE of m customers and the risk score F in the same assessment cycle, and uses the customer feature assessment module to calculate and evaluate the accuracy, recall rate and F1 score indicators of the constructed prediction model in sequence; The risk score F is divided into the risk score F calculated and output by the constructed prediction model, and the risk score F that has occurred and is calculated and output based on the actual situation.

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