Risk target-based rule adaptive dynamic adjustment method and system

Abstract

The invention relates to the technical field of credit management, and discloses a rule adaptive dynamic adjustment method and system based on a risk target, and the method comprises the steps: S1, an early warning module comprehensively judges a credit risk value based on different key indexes, and continues a next step when the credit risk value is greater than a preset credit risk value threshold; s2, a division module collects different empirical data; s3, dividing the different empirical data into a first group, a second group, a third group and a fourth group by a division module, training and generating a first model by using the different empirical data in the first group and the second group, and training and generating a second model by using the different empirical data in the third group and the fourth group; and S4, the adjustment module determines a final risk result of the target customer by combining output results of the first model and the second model, and sets a credit rule corresponding to the final risk result for the target customer. According to the invention, the credit rule of the target customer can be dynamically adjusted, and a better credit management effect can be achieved.

Description

Technical Field

[0001] The present application relates to the technical field of credit management, and in particular to a rule-adaptive dynamic adjustment method and system based on risk objectives. Background Art

[0002] With the development of computer application technology, intelligent credit management systems have become more and more common. For example, the Chinese invention application with publication number CN107924535A provides a credit management system, which has: a unit for storing customer information by customer; a storage unit for storing case information by credit transaction, wherein the case information is associated with one or more customer information; a unit for receiving an identifier for determining a customer or a credit transaction; and a unit for notifying case information associated with a customer having the received identifier, or customer information associated with a credit transaction having the received identifier. In addition, the Chinese invention application with publication number CN115358851A proposes a credit management method and system, which includes: obtaining a credit instruction sent by a user and a user category to which the user belongs; selecting a system participating in credit management according to the user category to which the user belongs; and managing the credit instructions sent by the user based on the system participating in credit management. The technical solution improves the level of credit management and the ability to prevent credit risks, and also makes credit management more efficient and convenient.

[0003] However, both of the above two invention applications are unable to dynamically adjust the credit rules for target customers, which affects the effectiveness of credit management. Summary of the invention

[0004] In the case where the credit risk value is greater than a preset credit risk value threshold, the application collects different experience data, divides the different experience data into a first group and a second group, and a third group and a fourth group, uses the different experience data in the first group and the second group to train and generate a first model, uses the different experience data in the third group and the fourth group to train and generate a second model, combines the output results of the first model and the second model to determine the final risk result of the target customer, and sets a credit rule corresponding to the final risk result for the target customer. The application aims to dynamically adjust the credit rules for the target customer.

[0005] This application provides a rule adaptive dynamic adjustment method based on risk objectives, which mainly includes the following steps: S1. The early warning module collects and processes different key indicators of the credit business, including the overall pass rate, rule hit rate, overdue rate, and default rate. The credit risk value is comprehensively determined based on different key indicators. If the credit risk value is greater than the preset credit risk value threshold, the next step is continued; S2. The segmentation module collects different experience data, the experience data includes feature data generated by the customer's credit score, the customer's repayment history, the customer's income level, and the customer's loan purpose, and risk results corresponding to the feature data, the risk results include first risk, second risk, and third risk; S3, the division module divides different empirical data corresponding to the risk results of the first risk and the second risk into the first group, divides different empirical data corresponding to the risk results of the third risk into the second group, modifies the risk results of the different empirical data in the first group into the first combined risk, and the division module divides different empirical data corresponding to the risk result of the first risk into the third group, divides different empirical data corresponding to the risk results of the second risk and the third risk into the fourth group, modifies the risk results of the different empirical data in the fourth group into the second combined risk, and the division module further uses the different empirical data in the first group and the second group to train and generate the first model, and uses the different empirical data in the third group and the fourth group to train and generate the second model; S4. The adjustment module obtains the target customer's credit score, the target customer's repayment history, the target customer's income level, and the target customer's loan purpose to generate feature data, and the adjustment module inputs the feature data into the first model and the second model respectively, determines the target customer's final risk result based on the output results of the first model and the second model, and sets credit rules corresponding to the final risk result for the target customer.

[0006] As a preferred technical solution of the present application, the division module uses different empirical data in the first group and the second group to train and generate a first model, including the following steps: S31, the classification module uses different experience data in the first group and the second group to train and generate a first sub-model, when the first sub-model outputs positive one, it represents that the input feature data is classified into the first combined risk, and when the first sub-model outputs negative one, it represents that the input feature data is classified into the third risk; S32, for each empirical data in the first group and the second group, the division module calculates the interval value between the empirical data and the division surface of the first sub-model, and the division module determines whether the interval values ​​of different empirical data in the first group and the second group meet the preset conditions, and if so, ends all steps, and if not, proceeds to the next step; S33, the division module divides different empirical data with corresponding interval values ​​greater than plus one into a first data group, divides different empirical data with corresponding interval values ​​greater than or equal to minus one and less than or equal to plus one into a second data group, and divides different empirical data with corresponding interval values ​​less than minus one into a third data group, and the division module sets the first data group, the second data group, and the third data group as target data groups; S34. Regarding each target data group, the division module performs a first processing on different experience data in the target data group, and determines whether the end flag is positive one. If yes, all steps are ended, and if not, this step is repeated.

[0007] As a preferred technical solution of the present application, the first process includes the following steps: S341, the partitioning module uses different experience data in the target data group to train and generate a new first sub-model, and for each experience data in the target data group, the partitioning module calculates the interval value between the experience data and the partitioning surface of the new first sub-model, and the partitioning module also determines whether the interval value of different experience data in the target data group meets the preset condition, and if so, sets the end flag to positive one, and ends all steps, and if not, proceeds to the next step; S342, the division module divides the different empirical data in the target data group whose corresponding interval value is greater than plus one into a new first data group, divides the different empirical data in the target data group whose corresponding interval value is greater than or equal to negative one and less than or equal to positive one into a new second data group, divides the different empirical data in the target data group whose corresponding interval value is less than negative one into a new third data group, and the division module sets the new first data group, the new second data group, and the new third data group as target data groups.

[0008] As a preferred technical solution of the present application, in the process in which the division module determines whether the interval values ​​of different empirical data in the first group and the second group meet the preset conditions, the preset conditions refer to that the number of interval values ​​greater than or equal to negative one and less than or equal to positive one is zero.

[0009] As a preferred technical solution of the present application, the process in which the adjustment module inputs the characteristic data of the target customer into the first model to obtain the output result includes the following steps: S41, the adjustment module uses the first first sub-model to divide the characteristic data of the target customer, calculates the interval value between the characteristic data of the target customer and the division surface of the first first sub-model, and the adjustment module determines whether the current first sub-model is the last first sub-model, and if so, proceeds to S43, and if not, determines the corresponding first sub-model according to the interval value corresponding to the characteristic data of the target customer; S42, the adjustment module performs a second process on the characteristic data of the target customer, and the adjustment module determines whether the first sub-model determined by the second process is the last first sub-model, and if yes, proceeds to the next step, and if no, repeats this step; S43, the adjustment module uses the last first sub-model to divide the characteristic data of the target customer, calculates the interval value between the characteristic data of the target customer and the division surface of the last first sub-model, and when the interval value is greater than or equal to zero, divides the characteristic data of the target customer into the first combination risk; when the interval value is less than zero, divides the characteristic data of the target customer into the third risk.

[0010] As a preferred technical solution of the present application, the second process includes the following steps: S421, the adjustment module determines whether the current first sub-model is the last first sub-model, and if so, ends all steps, and if not, proceeds to the next step; S422, the adjustment module uses the current first sub-model to divide the characteristic data of the target customer, calculates the interval value between the characteristic data of the target customer and the division surface of the current first sub-model, and the adjustment module determines the corresponding first sub-model according to the interval value corresponding to the characteristic data of the target customer.

[0011] As a preferred technical solution of the present application, the process in which the adjustment module determines the final risk result of the target customer in combination with the output results of the first model and the second model includes: when the output result of the first model is the first combined risk and the output result of the second model is the first risk, the final risk result of the target customer is determined to be the first risk; when the output result of the first model is the first combined risk and the output result of the second model is the second combined risk, the final risk result of the target customer is determined to be the second risk; when the output result of the first model is the third risk and the output result of the second model is the second combined risk, the final risk result of the target customer is determined to be the third risk.

[0012] The present application also provides a rule adaptive dynamic adjustment system based on risk targets, which mainly includes the following modules: The early warning module is used to collect and process different key indicators of credit business, including overall pass rate, rule hit rate, overdue rate, and default rate, and comprehensively determine the credit risk value based on different key indicators; a partitioning module, for collecting different experience data, the experience data including feature data generated by a customer's credit score, a customer's repayment history, a customer's income level, and a customer's loan purpose, and risk results corresponding to the feature data, the risk results including a first risk, a second risk, and a third risk, and for partitioning different experience data corresponding to the risk results of the first risk and the second risk into a first group, partitioning different experience data corresponding to the risk results of the third risk into a second group, modifying the risk results of different experience data in the first group into a first combined risk, partitioning different experience data corresponding to the risk results of the first risk into a third group, partitioning different experience data corresponding to the risk results of the second risk and the third risk into a fourth group, modifying the risk results of different experience data in the fourth group into a second combined risk, and using different experience data in the first group and the second group to train and generate a first model, and using different experience data in the third group and the fourth group to train and generate a second model; The adjustment module is used to obtain the credit score of the target customer, the repayment history of the target customer, the income level of the target customer, and the loan purpose of the target customer to generate feature data, input the feature data into the first model and the second model respectively, and determine the final risk result of the target customer based on the output results of the first model and the second model, and set credit rules corresponding to the final risk result for the target customer.

[0013] This application has at least the following beneficial effects: In the present application, first, the early warning module collects and processes different key indicators of the credit business, comprehensively determines the credit risk value based on different key indicators, and proceeds to the next step when the credit risk value is greater than the preset credit risk value threshold. Secondly, the division module collects different experience data. Again, the division module divides the different experience data corresponding to the risk results of the first risk and the second risk into the first group, divides the different experience data corresponding to the risk results of the third risk into the second group, modifies the risk results of the different experience data in the first group to the first combined risk, and the division module divides the different experience data corresponding to the risk results of the first risk into the third group, divides the different experience data corresponding to the risk results of the second risk and the third risk into the fourth group, modifies the risk results of the different experience data in the fourth group to the second combined risk, and the division module also uses the different experience data in the first group and the second group to train and generate the first model, and uses the different experience data in the third group and the fourth group to train and generate the second model. Finally, the adjustment module generates the characteristic data of the target customer, inputs the characteristic data into the first model and the second model respectively, determines the final risk result of the target customer in combination with the output results of the first model and the second model, and sets the credit rules corresponding to the final risk result for the target customer. Through this application, not only can timely warnings be issued when the credit risk value is large, but the final risk results of target customers can also be automatically generated. By adopting corresponding credit rules for target customers, better credit management effects can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 This is a flow chart of a rule adaptive dynamic adjustment method based on risk targets in an embodiment of the present application; Figure 2 A schematic diagram of a first model of a rule-adaptive dynamic adjustment method based on risk targets in an embodiment of the present application; Figure 3 Schematic diagram of a rule-adaptive dynamic adjustment system based on risk objectives in an embodiment of the present application. DETAILED DESCRIPTION

[0016] The embodiment of the present application provides a rule-adaptive dynamic adjustment method and system based on risk objectives. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0017] It should be noted that the acquisition and processing of all information or data in this application are carried out in compliance with the relevant national data protection laws and policies and with the authorization given by the owner of the corresponding device.

[0018] For easier understanding, see Figure 1 In an embodiment of the present application, a rule-based adaptive dynamic adjustment method based on risk targets mainly includes the following steps: S1. The early warning module collects and processes different key indicators of the credit business, including the overall pass rate, rule hit rate, overdue rate, and default rate. The credit risk value is comprehensively determined based on different key indicators. If the credit risk value is greater than the preset credit risk value threshold, the next step is continued; S2. The segmentation module collects different experience data, the experience data includes feature data generated by the customer's credit score, the customer's repayment history, the customer's income level, and the customer's loan purpose, and risk results corresponding to the feature data, the risk results include first risk, second risk, and third risk; S3, the division module divides the different experience data whose corresponding risk results are the first risk and the second risk into the first group, divides the different experience data whose corresponding risk results are the third risk into the second group, modifies the risk results of the different experience data in the first group into the first combined risk, and the division module divides the different experience data whose corresponding risk results are the first risk into the third group, divides the different experience data whose corresponding risk results are the second risk and the third risk into the fourth group, modifies the risk results of the different experience data in the fourth group into the second combined risk, and the division module further uses the different experience data in the first group and the second group to train and generate the first model, and uses the different experience data in the third group and the fourth group to train and generate the second model; S4. The adjustment module obtains the target customer's credit score, the target customer's repayment history, the target customer's income level, and the target customer's loan purpose to generate feature data, and the adjustment module inputs the feature data into the first model and the second model respectively, determines the final risk result of the target customer based on the output results of the first model and the second model, and sets credit rules corresponding to the final risk result for the target customer.

[0019] Specifically, in S1, the early warning module collects different key indicators of the credit business, including the overall pass rate, the rule hit rate, the overdue rate, and the default rate, wherein the overall pass rate refers to the proportion of customers who are approved among customers who apply for credit within a certain period of time, the rule hit rate refers to the proportion of applications that meet specific approval rules during the credit review process, the overdue rate refers to the proportion of loans that are not repaid on time within a certain period of time to the total loans, and the default rate refers to the proportion of customers who fail to fulfill their repayment obligations according to the terms of the contract within a certain period of time. The early warning module comprehensively determines the credit risk value based on different key indicators. For example, the hierarchical analysis method can be used to determine the credit risk value. When the credit risk value is greater than the preset credit risk value threshold, continue to S2. The credit risk value threshold is set according to the actual application scenario. In S2, the division module collects different experience data, the experience data includes feature data, and the risk results corresponding to the feature data. The feature data is generated according to the customer's credit score, the customer's repayment history, the customer's income level, and the customer's loan purpose. It can be in the form of a vector. The risk results include the first risk, the second risk, and the third risk. It should be noted that the first risk corresponds to high risk, the second risk corresponds to medium risk, and the third risk corresponds to low risk. In S3, the division module divides different empirical data corresponding to the risk results of the first risk and the second risk into the first group, divides different empirical data corresponding to the risk result of the third risk into the second group, modifies the risk results of the different empirical data in the first group into the first combination risk, and keeps the risk results of the different empirical data in the second group unchanged. In addition, the division module divides different empirical data corresponding to the risk result of the first risk into the third group, divides different empirical data corresponding to the risk results of the second risk and the third risk into the fourth group, modifies the risk results of the different empirical data in the fourth group into the second combination risk, and keeps the risk results of the different empirical data in the third group unchanged. The division module also uses the different empirical data in the first group and the second group to train and generate a first model, and uses the different empirical data in the third group and the fourth group to train and generate a second model. How to obtain the first model will be introduced below. According to the process of obtaining the first model, it is easy to think of the process of obtaining the second model. Therefore, the process of obtaining the second model will not be repeated below.In S4, the adjustment module obtains the target customer's credit score, the target customer's repayment history, the target customer's income level, and the target customer's loan purpose, in order to generate feature data. The target customer can be any customer that needs to be divided. The adjustment module inputs the feature data into the first model and the second model respectively, and determines the final risk result of the target customer based on the output results of the first model and the second model. The specific process will be described below. Credit rules corresponding to the final risk result are set for the target customer. For ease of understanding, for example, if the final risk result is the first risk, credit rules such as rejecting or restricting loan applications and raising interest rates can be adopted for the target customer.

[0020] Through the above method, not only can timely warnings be issued when the credit risk value is large, but also the final risk results of target customers can be automatically generated. By adopting corresponding credit rules for target customers, better credit management effects can be achieved.

[0021] Furthermore, the division module uses different empirical data in the first group and the second group to train and generate a first model, including the following steps: S31, the classification module uses different experience data in the first group and the second group to train and generate a first sub-model, when the first sub-model outputs positive one, it represents that the input feature data is classified into the first combination risk, and when the first sub-model outputs negative one, it represents that the input feature data is classified into the third risk; S32, for each empirical data in the first group and the second group, the partitioning module calculates the interval value between the empirical data and the partitioning surface of the first sub-model, and the partitioning module determines whether the interval values ​​of different empirical data in the first group and the second group meet the preset conditions, and if so, ends all steps, and if not, proceeds to the next step; S33, the division module divides different empirical data with corresponding interval values ​​greater than plus one into a first data group, divides different empirical data with corresponding interval values ​​greater than or equal to minus one and less than or equal to plus one into a second data group, and divides different empirical data with corresponding interval values ​​less than minus one into a third data group, and the division module sets the first data group, the second data group, and the third data group as target data groups; S34. For each target data group, the division module performs the first processing on the different experience data in the target data group, and determines whether the end flag is positive one. If yes, all steps are ended; if not, this step is repeated.

[0022] Specifically, in S31, the division module uses different empirical data in the first group and the second group to train and generate a first sub-model. The first sub-model can be a support vector machine using a soft margin. When the first sub-model outputs positive one, it represents that the input feature data is divided into the first combination risk. When the first sub-model outputs negative one, it represents that the input feature data is divided into the third risk. In S32, for each empirical data in the first group and the second group, the division module calculates the interval value between the empirical data and the division surface of the first sub-model. It should be noted that the interval value is also normalized so that the mean of the interval value corresponding to all feature data divided into the first combination risk is positive one, and the mean of the interval value corresponding to all feature data divided into the third risk is negative one. The division module determines whether the interval value of different empirical data in the first group and the second group meets the preset conditions. The preset conditions will be introduced below. If it meets, all steps are terminated. If it does not meet, it is necessary to continue to generate a new first sub-model and continue to S33. In S33, the division module divides the different experience data whose corresponding interval values ​​are greater than positive one into a first data group, divides the different experience data whose corresponding interval values ​​are greater than or equal to negative one and less than or equal to positive one into a second data group, and divides the different experience data whose corresponding interval values ​​are less than negative one into a third data group, and the division module sets the first data group, the second data group, and the third data group as target data groups. In S34, with respect to each target data group, the division module performs a first processing on the different experience data in the target data group, and the process of the first processing will be described below, and then determines whether the end flag is positive one, and if so, ends all steps, and if not, repeats this step.

[0023] Furthermore, the first process comprises the following steps: S341, the partitioning module uses different experience data in the target data group to train and generate a new first sub-model, and for each experience data in the target data group, the partitioning module calculates the interval value between the experience data and the partitioning surface of the new first sub-model, and the partitioning module also determines whether the interval value of the different experience data in the target data group meets the preset condition, and if so, sets the end flag to positive one, and ends all steps, and if not, proceeds to the next step; S342. The division module divides different empirical data in the target data group whose corresponding interval value is greater than plus one into a new first data group, divides different empirical data in the target data group whose corresponding interval value is greater than or equal to negative one and less than or equal to positive one into a new second data group, and divides different empirical data in the target data group whose corresponding interval value is less than negative one into a new third data group, and the division module sets the new first data group, the new second data group, and the new third data group as target data groups.

[0024] Specifically, in S341, the partitioning module uses different empirical data in the target data group to train and generate a new first sub-model. The new first sub-model can also be a support vector machine using a soft margin. For each empirical data in the target data group, the partitioning module calculates the interval value between the empirical data and the partitioning surface of the new first sub-model. The partitioning module also determines whether the interval value of different empirical data in the target data group meets the preset conditions. The preset conditions will be introduced below. If it meets the preset conditions, the end flag is set to positive one and all steps are ended. It should be noted that the initial value of the end flag is zero. If it does not meet the preset conditions, continue to S342. In S342, the partitioning module divides the different empirical data in the target data group whose corresponding interval value is greater than positive one into a new first data group, divides the different empirical data in the target data group whose corresponding interval value is greater than or equal to negative one and less than or equal to positive one into a new second data group, divides the different empirical data in the target data group whose corresponding interval value is less than negative one into a new third data group, and sets the new first data group, the new second data group, and the new third data group as the target data group.

[0025] By executing the above method, the division module can obtain the first model. In fact, the first model is composed of several first sub-models. For ease of understanding, for example, the first model is as follows Figure 2 As shown, A is the first sub-model, B, D, E, F, and G are the last first sub-models, and C is the first sub-model.

[0026] Further, in the process where the division module determines whether the interval values ​​of different empirical data in the first group and the second group meet a preset condition, the preset condition refers to that the number of interval values ​​greater than or equal to negative one and less than or equal to positive one is zero.

[0027] Specifically, in the process of the division module judging whether the interval values ​​of different empirical data in the first group and the second group meet the preset conditions, the preset conditions refer to that the number of interval values ​​greater than or equal to negative one and less than or equal to positive one is zero, and the sign of the interval value of each empirical data can also be added to be the same as the sign of the corresponding output result. In addition, the preset conditions in the process of the division module judging whether the interval values ​​of different empirical data in the first group and the second group meet the preset conditions are the same as this.

[0028] Furthermore, the process in which the adjustment module inputs the characteristic data of the target customer into the first model to obtain the output result includes the following steps: S41, the adjustment module uses the first first sub-model to divide the characteristic data of the target customer, and the adjustment module calculates the interval value between the characteristic data of the target customer and the division surface of the first first sub-model, and the adjustment module further determines whether the current first sub-model is the last first sub-model, and if so, proceeds to S43, and if not, determines the corresponding first sub-model according to the interval value corresponding to the characteristic data of the target customer; S42, the adjustment module performs a second process on the characteristic data of the target customer, and the adjustment module determines whether the first sub-model determined by the second process is the last first sub-model, and if yes, proceeds to the next step, and if no, repeats this step; S43. The adjustment module uses the last first sub-model to divide the characteristic data of the target customer, and calculates the interval value between the characteristic data of the target customer and the division surface of the last first sub-model. When the interval value is greater than or equal to zero, the characteristic data of the target customer is divided into the first combination risk. When the interval value is less than zero, the characteristic data of the target customer is divided into the third risk.

[0029] Specifically, for ease of understanding, the above example is introduced. In S41, the adjustment module uses the first first sub-model, i.e., A, to divide the target customer's feature data. The adjustment module calculates the interval value between the target customer's feature data and the division surface of the first first sub-model, i.e., A. The adjustment module also determines whether the current first sub-model, i.e., A, is the last first sub-model. If so, continue to S43. If not, determine the corresponding first sub-model according to the interval value corresponding to the target customer's feature data. Since A is not the last first sub-model, it is assumed that the corresponding first sub-model determined is C. In S42, the adjustment module performs a second processing on the target customer's feature data. It is assumed that the first sub-model determined by performing the second processing is E. The adjustment module determines whether the first sub-model, i.e., E, determined by performing the second processing is the last first sub-model. If so, continue to the next step. If not, repeat this step. Since E is the last first sub-model, continue to the next step. In S43, the adjustment module uses the last first sub-model, i.e., E, to divide the characteristic data of the target customer, and calculates the interval value between the characteristic data of the target customer and the dividing surface of the last first sub-model. If the interval value is greater than or equal to zero, the characteristic data of the target customer is divided into the first combination risk; if the interval value is less than zero, the characteristic data of the target customer is divided into the third risk.

[0030] Further, the second process comprises the following steps: S421, the adjustment module determines whether the current first sub-model is the last first sub-model, and if so, ends all steps, and if not, proceeds to the next step; S422. The adjustment module uses the current first sub-model to divide the characteristic data of the target customer, calculates the interval value between the characteristic data of the target customer and the division surface of the current first sub-model, and determines the corresponding first sub-model according to the interval value corresponding to the characteristic data of the target customer.

[0031] Specifically, for ease of understanding, the above example is also used for introduction. In S421, the adjustment module determines whether the current first sub-model, i.e., C, is the last first sub-model. If so, all steps are terminated. If not, the next step is continued. Since C is not the last first sub-model, the next step is continued. In S422, the adjustment module uses the current first sub-model, i.e., C, to divide the characteristic data of the target customer, calculates the interval value between the characteristic data of the target customer and the division surface of the current first sub-model, and the adjustment module determines the corresponding first sub-model according to the interval value corresponding to the characteristic data of the target customer. It is assumed that the determined corresponding first sub-model is E.

[0032] Through the above method, the adjustment module can accurately divide the characteristic data of the target customer based on the first model, so as to facilitate the adoption of credit rules for the final risk results of the target customer.

[0033] Furthermore, the process of the adjustment module determining the final risk result of the target customer in combination with the output results of the first model and the second model includes: when the output result of the first model is the first combined risk and the output result of the second model is the first risk, determining the final risk result of the target customer to be the first risk; when the output result of the first model is the first combined risk and the output result of the second model is the second combined risk, determining the final risk result of the target customer to be the second risk; when the output result of the first model is the third risk and the output result of the second model is the second combined risk, determining the final risk result of the target customer to be the third risk.

[0034] Specifically, the present invention introduces a process in which the adjustment module determines the final risk result of the target customer by combining the output results of the first model and the second model. If the output result of the first model is the first combined risk and the output result of the second model is the first risk, the final risk result of the target customer is determined to be the first risk. If the output result of the first model is the first combined risk and the output result of the second model is the second combined risk, the final risk result of the target customer is determined to be the second risk. If the output result of the first model is the third risk and the output result of the second model is the second combined risk, the final risk result of the target customer is determined to be the third risk. At other times, it is considered that the final risk result of the target customer cannot be generated, and the first model and the second model can be regenerated.

[0035] The above describes a method for adaptively adjusting rules based on risk targets in an embodiment of the present application. The following describes a system for adaptively adjusting rules based on risk targets in an embodiment of the present application. Figure 3 A rule-based adaptive dynamic adjustment system based on risk targets in an embodiment of the present application includes the following modules: The early warning module is used to collect and process different key indicators of credit business, including overall pass rate, rule hit rate, overdue rate, and default rate, and comprehensively determine the credit risk value based on different key indicators; a partitioning module, for collecting different experience data, the experience data including feature data generated by a customer's credit score, a customer's repayment history, a customer's income level, and a customer's loan purpose, and risk results corresponding to the feature data, the risk results including a first risk, a second risk, and a third risk, and for partitioning different experience data corresponding to the risk results of the first risk and the second risk into a first group, partitioning different experience data corresponding to the risk results of the third risk into a second group, modifying the risk results of different experience data in the first group into a first combined risk, partitioning different experience data corresponding to the risk results of the first risk into a third group, partitioning different experience data corresponding to the risk results of the second risk and the third risk into a fourth group, modifying the risk results of different experience data in the fourth group into a second combined risk, and using different experience data in the first group and the second group to train and generate a first model, and using different experience data in the third group and the fourth group to train and generate a second model; The adjustment module is used to obtain the credit score of the target customer, the repayment history of the target customer, the income level of the target customer, and the loan purpose of the target customer to generate feature data, input the feature data into the first model and the second model respectively, and determine the final risk result of the target customer based on the output results of the first model and the second model, and set credit rules corresponding to the final risk result for the target customer.

[0036] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the above-described system and specific working processes of the modules can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0037] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A rule adaptive dynamic adjustment method based on risk objectives, characterized in that: The steps include: S1. The early warning module collects and processes different key indicators of the credit business, including the overall pass rate, rule hit rate, overdue rate, and default rate. The credit risk value is comprehensively determined based on different key indicators. If the credit risk value is greater than the preset credit risk value threshold, the next step is continued; S2. The segmentation module collects different experience data, the experience data includes feature data generated by the customer's credit score, the customer's repayment history, the customer's income level, and the customer's loan purpose, and risk results corresponding to the feature data, the risk results include first risk, second risk, and third risk; S3, the division module divides different empirical data corresponding to the risk results of the first risk and the second risk into the first group, divides different empirical data corresponding to the risk results of the third risk into the second group, modifies the risk results of the different empirical data in the first group into the first combined risk, and the division module divides different empirical data corresponding to the risk result of the first risk into the third group, divides different empirical data corresponding to the risk results of the second risk and the third risk into the fourth group, modifies the risk results of the different empirical data in the fourth group into the second combined risk, and the division module further uses the different empirical data in the first group and the second group to train and generate the first model, and uses the different empirical data in the third group and the fourth group to train and generate the second model; S4. The adjustment module obtains the target customer's credit score, the target customer's repayment history, the target customer's income level, and the target customer's loan purpose to generate feature data, and the adjustment module inputs the feature data into the first model and the second model respectively, determines the target customer's final risk result based on the output results of the first model and the second model, and sets credit rules corresponding to the final risk result for the target customer.

2. The method for adaptive dynamic adjustment of rules based on risk targets according to claim 1, characterized in that: The division module uses different empirical data in the first group and the second group to train and generate a first model, including the following steps: S31, the classification module uses different experience data in the first group and the second group to train and generate a first sub-model, when the first sub-model outputs positive one, it represents that the input feature data is classified into the first combined risk, and when the first sub-model outputs negative one, it represents that the input feature data is classified into the third risk; S32, for each empirical data in the first group and the second group, the division module calculates the interval value between the empirical data and the division surface of the first sub-model, and the division module determines whether the interval values ​​of different empirical data in the first group and the second group meet the preset conditions, and if so, ends all steps, and if not, proceeds to the next step; S33, the division module divides different empirical data with corresponding interval values ​​greater than plus one into a first data group, divides different empirical data with corresponding interval values ​​greater than or equal to minus one and less than or equal to plus one into a second data group, and divides different empirical data with corresponding interval values ​​less than minus one into a third data group, and the division module sets the first data group, the second data group, and the third data group as target data groups; S34. Regarding each target data group, the division module performs a first processing on different experience data in the target data group, and determines whether the end flag is positive one. If yes, all steps are ended, and if not, this step is repeated.

3. The method for adaptive dynamic adjustment of rules based on risk targets according to claim 2, characterized in that: The first process includes the following steps: S341, the partitioning module uses different experience data in the target data group to train and generate a new first sub-model, and for each experience data in the target data group, the partitioning module calculates the interval value between the experience data and the partitioning surface of the new first sub-model, and the partitioning module also determines whether the interval value of different experience data in the target data group meets the preset condition, and if so, sets the end flag to positive one, and ends all steps, and if not, proceeds to the next step; S342, the division module divides the different empirical data in the target data group whose corresponding interval value is greater than plus one into a new first data group, divides the different empirical data in the target data group whose corresponding interval value is greater than or equal to negative one and less than or equal to positive one into a new second data group, divides the different empirical data in the target data group whose corresponding interval value is less than negative one into a new third data group, and the division module sets the new first data group, the new second data group, and the new third data group as target data groups.

4. The method for adaptive dynamic adjustment of rules based on risk targets according to claim 3, characterized in that: In the process where the division module determines whether the interval values ​​of different empirical data in the first group and the second group meet the preset condition, the preset condition refers to that the number of interval values ​​greater than or equal to negative one and less than or equal to positive one is zero.

5. The method for adaptive dynamic adjustment of rules based on risk targets according to claim 4, characterized in that: The process in which the adjustment module inputs the characteristic data of the target customer into the first model to obtain the output result includes the following steps: S41, the adjustment module uses the first first sub-model to divide the characteristic data of the target customer, calculates the interval value between the characteristic data of the target customer and the division surface of the first first sub-model, and the adjustment module determines whether the current first sub-model is the last first sub-model, and if so, proceeds to S43, and if not, determines the corresponding first sub-model according to the interval value corresponding to the characteristic data of the target customer; S42, the adjustment module performs a second process on the characteristic data of the target customer, and the adjustment module determines whether the first sub-model determined by the second process is the last first sub-model, and if yes, proceeds to the next step, and if no, repeats this step; S43, the adjustment module uses the last first sub-model to divide the characteristic data of the target customer, calculates the interval value between the characteristic data of the target customer and the division surface of the last first sub-model, and when the interval value is greater than or equal to zero, divides the characteristic data of the target customer into the first combination risk; when the interval value is less than zero, divides the characteristic data of the target customer into the third risk.

6. A rule adaptive dynamic adjustment method based on risk target according to claim 5, characterized in that: The second process includes the following steps: S421, the adjustment module determines whether the current first sub-model is the last first sub-model, and if so, ends all steps, and if not, proceeds to the next step; S422, the adjustment module uses the current first sub-model to divide the characteristic data of the target customer, calculates the interval value between the characteristic data of the target customer and the division surface of the current first sub-model, and the adjustment module determines the corresponding first sub-model according to the interval value corresponding to the characteristic data of the target customer.

7. The method for adaptive dynamic adjustment of rules based on risk targets according to claim 6, characterized in that: The process of the adjustment module determining the final risk result of the target customer in combination with the output results of the first model and the second model includes: when the output result of the first model is the first combined risk and the output result of the second model is the first risk, determining the final risk result of the target customer to be the first risk; when the output result of the first model is the first combined risk and the output result of the second model is the second combined risk, determining the final risk result of the target customer to be the second risk; when the output result of the first model is the third risk and the output result of the second model is the second combined risk, determining the final risk result of the target customer to be the third risk.

8. A rule-adaptive dynamic adjustment system based on risk targets, used to implement the method according to any one of claims 1 to 7, characterized in that: Includes the following modules: The early warning module is used to collect and process different key indicators of credit business, including overall pass rate, rule hit rate, overdue rate, and default rate, and comprehensively determine the credit risk value based on different key indicators; a partitioning module, for collecting different experience data, the experience data including feature data generated by a customer's credit score, a customer's repayment history, a customer's income level, and a customer's loan purpose, and risk results corresponding to the feature data, the risk results including a first risk, a second risk, and a third risk, and for partitioning different experience data corresponding to the risk results of the first risk and the second risk into a first group, partitioning different experience data corresponding to the risk results of the third risk into a second group, modifying the risk results of different experience data in the first group into a first combined risk, partitioning different experience data corresponding to the risk results of the first risk into a third group, partitioning different experience data corresponding to the risk results of the second risk and the third risk into a fourth group, modifying the risk results of different experience data in the fourth group into a second combined risk, and using different experience data in the first group and the second group to train and generate a first model, and using different experience data in the third group and the fourth group to train and generate a second model; The adjustment module is used to obtain the credit score of the target customer, the repayment history of the target customer, the income level of the target customer, and the loan purpose of the target customer to generate feature data, input the feature data into the first model and the second model respectively, and determine the final risk result of the target customer based on the output results of the first model and the second model, and set credit rules corresponding to the final risk result for the target customer.

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