A method and system for automatic processing of credit business based on large language model

Abstract

The present invention provides a method and system for automatically processing credit business based on a large language model, which belongs to the field of financial information technology, and specifically includes: determining that the change in user credit risk within the target period meets the requirements through the change in distribution data of credit application users within different credit risk intervals within the target period, determining the location distribution data of the credit application users based on the parsing results of the credit application information of the credit application users, and determining suspected fraudulent users among the credit application users in combination with the parsing results of the credit application information of different credit application users, vectorizing all credit application data of the suspected fraudulent users in the credit application process, and combining the vectorization processing results of the credit application information excluding the suspected fraudulent users, using the large language model to automatically generate a credit report, thereby improving the efficiency of the generation and processing of the credit report.

Description

Technical Field

[0001] The present invention belongs to the field of financial information technology, and in particular relates to a credit business automation processing method and system based on a large language model. Background Art

[0002] When processing credit business using existing technical solutions, it is often necessary for credit employees to process credit applications based on the personal information of credit applicants. Not only is the processing efficiency slow, but the accuracy of the processing results is also difficult to meet requirements.

[0003] In response to the above technical problems, more and more consumer finance companies have set up their own credit application models to realize the automatic processing of credit applications. Specifically, the invention patent CN202410880254.0 "A method and system for generating and processing an optimal risk control strategy" and the invention patent application CN202311738093.3 "A credit risk control management method and system based on a multi-task boosting tree" both realize the automated approval and processing of credit applications. However, the above technical solutions have the following technical problems:

[0004] When processing credit business, credit reports are often generated regularly. The existing credit reports are often generated manually, which inevitably makes the generation and processing of credit reports very difficult. Therefore, how to reduce the difficulty of generating and processing credit reports has become a technical problem that needs to be solved urgently.

[0005] In view of the above technical problems, the present invention proposes a method and system for automatic processing of credit business based on a large language model. Summary of the invention

[0006] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0007] According to one aspect of the present invention, a method for automatic processing of credit business based on a large language model is provided.

[0008] A method for automatic processing of credit business based on a large language model, specifically comprising:

[0009] S1 obtains overdue user data of a target credit business within a target period, and when the target credit business is determined to have approval risk by using the overdue user data, proceeds to the next step;

[0010] S2 determines the identification result of the credit application information of the credit application user in the target period in the credit approval model, and through the change of the distribution data of the credit application users in different credit risk intervals in the target period, determines that the change of the user credit risk in the target period meets the requirements, and enters the next step;

[0011] S3: determining the location distribution data of the credit applicant based on the analysis results of the credit application information of the credit applicant, and determining the suspected fraudulent users among the credit applicants in combination with the analysis results of the credit application information of different credit applicants;

[0012] S4 vectorizes all the credit application data of the suspected fraudulent user during the credit application process, and automatically generates a credit report using a large language model based on the vectorized processing result of the credit application information excluding the suspected fraudulent user.

[0013] The beneficial effects of the present invention are:

[0014] 1. In the present invention, overdue user data is used to determine whether a target credit business has an approval risk, thereby achieving an accurate assessment of the approval risk of the target credit business from multiple angles such as the number of overdue users and the overdue amount, and dynamically achieving an assessment of the approval risk. It also lays the foundation for further generating differentiated credit reports based on the size and changes of the approval risk, which not only reduces the difficulty of generating and processing the credit report, but also allows for faster attribution and dynamic adjustment of credit businesses with higher approval risks.

[0015] 2. In the present invention, all credit application data of suspected fraudulent users in the credit application process, excluding the vectorized processing results of the credit application information of suspected fraudulent users, are used to automatically generate credit reports using a large language model. This not only ensures the comprehensiveness of the data of suspected fraudulent users and realizes accurate identification of fraud risks of target credit business, but also reduces the information dimension of the input data. On the basis of reducing the difficulty of large language model processing, the efficiency and accuracy of credit report generation and processing are guaranteed.

[0016] A further technical solution is that the target period ranges from 1 month to 3 months, and is specifically determined according to the number of credit application users of the target credit business.

[0017] A further technical solution is that the overdue user data includes the number of overdue users and the overdue amount of the overdue users.

[0018] A further technical solution is that all the credit application data of the suspected fraudulent user includes credit application information, communication information during the credit application process, and identity identification information.

[0019] A further technical solution is to automatically generate a credit report, specifically including:

[0020] Using the vectorized processing results of all the credit application data of the suspected fraudulent user and the vectorized processing results of the credit application information excluding the suspected fraudulent user as the input of the large language model, and based on the input, using the large language model to determine the analysis results of indicators in multiple dimensions;

[0021] Based on the analysis results of indicators in multiple dimensions, credit reports are automatically generated according to preset templates.

[0022] In a second aspect, the present invention provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, characterized in that: when the processor runs the computer program, it executes the above-mentioned method for automated processing of credit business based on a large language model.

[0023] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0024] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0026] Figure 1 It is a flow chart of a method for automating credit business processing based on a large language model;

[0027] Figure 2 It is a flow chart of the method for determining whether the target credit business has approval risk;

[0028] Figure 3 It is a flow chart to determine whether the changes in user credit risk within the target period meet the requirements;

[0029] Figure 4 It is a flow chart of a method for determining suspected fraud users among credit application users;

[0030] Figure 5 It is a framework diagram of a computer system. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0032] When processing credit business, credit reports are often generated on a regular basis. Existing credit reports are often generated manually, resulting in problems such as high report production costs, high review costs, high new document supplement costs, and lack of automatic pre-examination and verification capabilities.

[0033] In view of the above technical problems, the present invention proposes a method and system for automatic processing of credit business based on a large language model.

[0034] Determine whether the target credit business has approval risks: Use the proportion of overdue users of the target credit business within the target period to determine whether the target credit business has approval risks, and regard the target credit business with an overdue user proportion greater than 0.05 as the target credit business with approval risks.

[0035] Determine whether the changes in user credit risk within the target period meet the requirements: determine the average deviation coefficient of the comparison target period based on the deviation between the distribution ratio of credit application users in different credit risk intervals and the distribution ratio of the comparison target period, and determine the deviation target period in the comparison target period based on the average deviation coefficients of different comparison target periods; and when the number of deviation target periods accounts for more than 0.2, it is determined that the changes in user credit risk within the target period do not meet the requirements.

[0036] Suspected fraud users: other credit applicants who are within a preset range of distance from the credit applicant are classified into the same applicant group, the number of credit applicants in the applicant group is determined, and similar information items with other credit applicants are used to determine other credit applicants with similar information. The fraud risk coefficient of the credit applicant is determined based on the number of credit applicants and the number of similar information users, and credit applicants with a fraud risk coefficient greater than 0.6 are regarded as suspected fraud users.

[0037] The vectorized processing results of all credit application data of suspected fraudulent users and the vectorized processing results of the credit application information excluding the suspected fraudulent users are used as the input of the large language model, and based on the input, the large language model is used to determine the analysis results of indicators in multiple dimensions, and based on the analysis results of indicators in multiple dimensions, a credit report is automatically generated according to a preset template.

[0038] Embodiment 1 To solve the above problem, according to one aspect of the present invention, Figure 1 As shown, a method for automatic processing of credit business based on a large language model is provided, which specifically includes:

[0039] S1 obtains overdue user data of a target credit business within a target period, and when the target credit business is determined to have approval risk by using the overdue user data, proceeds to the next step;

[0040] Furthermore, the target period ranges from 1 month to 3 months, and is specifically determined according to the number of credit application users of the target credit business.

[0041] Optionally, the overdue user data includes the number of overdue users and the overdue amount of the overdue users.

[0042] It should be noted that if Figure 2 As shown, it is determined that the target credit business has approval risks, including:

[0043] Determining the overdue amounts of different overdue users based on the overdue user data of the target credit business within the target period;

[0044] Based on the overdue amount, the overdue users are divided into different overdue amount intervals, and the overdue proportion factors of the different overdue amount intervals are determined according to the proportion of overdue users in the different overdue amount intervals among the credit application users;

[0045] Determine the preset weight coefficients for different overdue amount intervals, and determine the approval risk factor of the target credit business in combination with the overdue proportion factors of different overdue amount intervals, and determine whether the target credit business has approval risk through the approval risk factor.

[0046] Further, determining whether the target credit business has an approval risk through the approval risk factor specifically includes:

[0047] When the approval risk factor is not within a preset range, it is determined that the target credit business has an approval risk.

[0048] It is understandable that when the target credit business does not have approval risk, the credit report is automatically generated using a large language model based on the vectorized processing result of the credit application information of the credit applicant.

[0049] Optionally, determining whether the target credit business has approval risk specifically includes:

[0050] Based on the overdue user data of the target credit business in the target period, the overdue amounts of different overdue users are determined, and the overdue risk factor of the target credit business in the target period is determined based on the average of the overdue amounts of different overdue users;

[0051] Determine the proportion of overdue users of the target credit business within a target period based on the analysis result of the overdue user data;

[0052] An approval risk factor is determined based on the product of the overdue risk factor and the proportion of overdue users, and whether the target credit business has an approval risk is determined by the approval risk factor.

[0053] It can be understood that the overdue risk factor of the target credit business within the target period is determined based on the preset risk factor corresponding to the average value of the overdue amount.

[0054] Optionally, determining whether the target credit business has approval risk specifically includes:

[0055] S11 determines the overdue amounts of different overdue users based on the overdue user data of the target credit business within the target period;

[0056] S12 classifies the overdue users into different overdue amount intervals based on the overdue amount, and determines interval risk factors of different overdue amount intervals according to the number of overdue users in different overdue amount intervals and the overdue amounts of different overdue users;

[0057] S13 determines preset weight coefficients for different overdue amount intervals, and determines the approval risk factor of the target credit business in combination with the interval risk factors of different overdue amount intervals, and determines whether the target credit business has an approval risk through the approval risk factor.

[0058] Optionally, the above step S11 includes steps S111-S113, which are specifically:

[0059] S111 determines the number of overdue users of the target credit business within the target period based on the overdue user data of the target credit business within the target period. When the number of overdue users of the target credit business within the target period does not meet the requirement, it is determined that the target credit business has an approval risk. When the number of overdue users of the target credit business within the target period meets the requirement, the process proceeds to step S112.

[0060] S112 determines the overdue amounts of different overdue users. When the number of overdue users of the target credit business within the target period is greater than the preset number of overdue users, the process proceeds to step S113. When the number of overdue users of the target credit business within the target period is not greater than the preset number of overdue users, the process directly proceeds to step S12.

[0061] S113 When the average value of the overdue amounts of different overdue users is not within the preset overdue amount range, it is determined that the target credit business has an approval risk. When the average value of the overdue amounts of different overdue users is within the preset overdue amount range, proceed to step S12.

[0062] Optionally, the above step S12 includes steps S121-S123, specifically:

[0063] S121 divides the overdue users into different overdue amount intervals based on the overdue amount. When the number of overdue users in the preset overdue amount interval does not meet the requirement, it is determined that the target credit business has an approval risk. When the number of overdue users in the preset overdue amount interval meets the requirement, the process proceeds to step S122.

[0064] S122 determines the interval risk factors of different overdue amount intervals according to the number of overdue users in different overdue amount intervals and the overdue amounts of different overdue users. When the interval risk factor in the preset overdue risk interval does not meet the requirements, it is determined that the target credit business has an approval risk. When the interval risk factor in the preset overdue risk interval meets the requirements, it proceeds to step S123.

[0065] S123 uses the preset risk factor threshold to determine the overdue risk intervals whose interval risk factors do not meet the requirements. When the number of overdue risk intervals whose interval risk factors do not meet the requirements does not meet the requirements, it is determined that the target credit business has approval risks. When the number of overdue risk intervals whose interval risk factors do not meet the requirements meets the requirements, proceed to step S13.

[0066] Furthermore, the overdue risk factor of the target credit business within the target period is determined based on a preset risk factor corresponding to the average value of the overdue amount.

[0067] S2 determines the identification result of the credit application information of the credit application user in the target period in the credit approval model, and through the change of the distribution data of the credit application users in different credit risk intervals in the target period, determines that the change of the user credit risk in the target period meets the requirements, and enters the next step;

[0068] Specifically, the change in the distribution data of the credit application user is determined based on the deviation of the distribution data of the target credit business within a preset number of target cycles.

[0069] Specifically, Figure 3 As shown, it is determined that the changes in the user's credit risk within the target period meet the requirements, specifically including:

[0070] Taking the target credit business in the adjacent preset number of target cycles as the comparison target cycle, and determining the distribution ratio of the credit application users in different credit risk intervals based on the distribution data of the credit application users in different credit risk intervals in the target cycle;

[0071] Determine the deviation from the distribution ratio of different comparison target periods based on the distribution ratio of credit application users in different credit risk intervals, and determine the deviation interval in the credit risk interval based on the deviation of the distribution ratio, and determine the distribution deviation factor of the comparison target period based on the number of the deviation intervals;

[0072] The coefficient of variation of the user credit risk within the target period is determined by taking the average value of the distribution deviation factors of different comparison target periods, and whether the variation of the user credit risk within the target period meets the requirements is determined by the coefficient of variation.

[0073] Furthermore, when the change in the user's credit risk within the target period does not meet the requirements, the credit report is automatically generated using a large language model based on the vectorized processing result of the credit application information of the credit applicant.

[0074] It can be understood that the deviation interval is a credit risk interval in which the deviation of the distribution ratio does not meet the requirements.

[0075] It should also be noted that the determination of whether the changes in user credit risk within the target period meet the requirements specifically includes:

[0076] Taking the target credit business in the adjacent preset number of target cycles as the comparison target cycle, and determining the distribution ratio of the credit application users in different credit risk intervals based on the distribution data of the credit application users in different credit risk intervals in the target cycle;

[0077] Determine the deviation of the distribution ratio of different comparison target periods based on the distribution ratio of credit application users in different credit risk intervals, and determine the average deviation coefficient of the comparison target period based on the deviation of the distribution ratio;

[0078] The deviation target period in the comparison target period is determined by using the average deviation coefficient of different comparison target periods, and the number of the deviation target periods is used to determine whether the change in the user credit risk within the target period meets the requirements.

[0079] Furthermore, the average deviation coefficient of the comparison target period is determined according to an average value of deviations of distribution ratios of the comparison target period in different credit risk intervals.

[0080] Optionally, determining whether the change in the user's credit risk within the target period meets the requirement specifically includes steps S21-S23:

[0081] S21: taking the target credit business in the adjacent preset number of target cycles as comparison target cycles, and determining the distribution ratio of credit application users in different credit risk intervals based on the distribution data of credit application users in different credit risk intervals in the target cycles;

[0082] S22: determining the deviations from the distribution ratios of different comparison target periods based on the distribution ratios of the credit application users in different credit risk intervals, and determining the deviation intervals in the credit risk intervals based on the deviations of the distribution ratios in different credit risk intervals, and determining the distribution deviation factor of the comparison target period based on the number of the deviation intervals and the deviations of the different deviation intervals;

[0083] S23 determines the weight coefficients of different comparison target periods based on the time intervals between different comparison target periods and the target period, and determines the coefficient of variation of the user credit risk within the target period in combination with the distribution deviation factors of different comparison target periods, and determines whether the change of the user credit risk within the target period meets the requirements through the coefficient of variation.

[0084] S3: determining the location distribution data of the credit applicant based on the analysis results of the credit application information of the credit applicant, and determining the suspected fraudulent users among the credit applicants in combination with the analysis results of the credit application information of different credit applicants;

[0085] Furthermore, the location distribution data of the credit application user is determined according to the analysis result of the positioning data corresponding to the credit application information of the credit application user.

[0086] It should be noted that if Figure 4 As shown, the method for determining the suspected fraudulent users among the credit application users is:

[0087] Based on the location distribution data of the credit application user, other credit application users within a preset range from the credit application user are divided into the same application user group, and the number of credit application users in the application user group is determined;

[0088] Obtaining similarities between the credit application information of the credit application user and other credit application users, and using the similarities to determine the ratio of similar information items to other credit application users, and determining information-similar users among other credit application users through the ratio of similar information items;

[0089] The fraud risk coefficient of the credit application user is determined based on the number of the credit application users and the number of users with similar information, and the suspected fraud users among the credit application users are determined based on the fraud risk coefficient.

[0090] Furthermore, the fraud risk coefficient of the credit application user is determined based on the product of the number of the credit application users, the number of users with similar information and a preset proportional factor.

[0091] S4 vectorizes all the credit application data of the suspected fraudulent user during the credit application process, and automatically generates a credit report using a large language model based on the vectorized processing result of the credit application information excluding the suspected fraudulent user.

[0092] It is understandable that all the credit application data of the suspected fraudulent user includes credit application information, communication information during the credit application process, and identity identification information.

[0093] It should be noted that the automatic generation of credit reports specifically includes:

[0094] Using the vectorized processing results of all the credit application data of the suspected fraudulent user and the vectorized processing results of the credit application information excluding the suspected fraudulent user as the input of the large language model, and based on the input, using the large language model to determine the analysis results of indicators in multiple dimensions;

[0095] Based on the analysis results of indicators in multiple dimensions, credit reports are automatically generated according to preset templates.

[0096] Optionally, before entering step S21, the following steps S201-S202 are further included, specifically:

[0097] S201 obtains the user credit risk of the credit application users within the target period. When the number of credit application users whose user credit risk is greater than the preset risk coefficient does not meet the requirement, it is determined that the change of the user credit risk within the target period does not meet the requirement. When the number of credit application users whose user credit risk is greater than the preset risk coefficient meets the requirement, the process proceeds to step S202.

[0098] S202 determines the average value of user credit risk of credit applicants within the target period. When the average value of the user credit risk is not within the preset credit risk range, it is determined that the change in user credit risk within the target period does not meet the requirements. When the average value of the user credit risk is within the preset credit risk range, proceed to step S21.

[0099] Optionally, the above step S21 includes steps S211-S213, which are specifically:

[0100] S211 takes the target credit business in the adjacent preset number of target cycles as the comparison target cycle. When the average value of the user credit risk of the credit application user in the target cycle and the deviation of other comparison target cycles are not within the preset risk deviation range, it is determined that the change of the user credit risk in the target cycle does not meet the requirements. When the average value of the user credit risk of the credit application user in the target cycle and the deviation of other comparison target cycles are within the preset risk deviation range, it goes to step S212;

[0101] S212: When the number of credit application users whose user credit risk in the target period is greater than the preset risk coefficient and the deviations of other comparison target periods are not within the preset deviation quantity interval, it is determined that the change of user credit risk in the target period does not meet the requirement; when the number of credit application users whose user credit risk in the target period is greater than the preset risk coefficient and the deviations of other comparison target periods are within the preset deviation quantity interval, the process proceeds to step S213;

[0102] S123 determines the distribution ratio of credit application users in different credit risk intervals based on the distribution data of credit application users in different credit risk intervals in the target period, and enters step S13.

[0103] Optionally, the above step S22 includes steps S221-S223, specifically:

[0104] S221 determines the deviation from the distribution ratio of different comparison target periods based on the distribution ratio of credit application users in different credit risk intervals, and determines the deviation interval in the credit risk interval based on the deviation of the distribution ratio in different credit risk intervals. When the number of comparison target periods with deviation intervals does not meet the requirement, it is determined that the change of user credit risk in the target period does not meet the requirement. When the number of comparison target periods with deviation intervals meets the requirement, it proceeds to step S222;

[0105] S222: The comparison target period in which the number of deviation intervals is greater than the number of preset intervals is taken as the deviation target period. If there is a deviation from the target period, it is determined that the change of the user's credit risk within the target period does not meet the requirements. If there is no deviation from the target period, the process proceeds to step S223.

[0106] S223 determines the distribution deviation factor of the comparison target period based on the number of deviation intervals and the deviations of different deviation intervals, and when there is no comparison target period whose distribution deviation factor does not meet the requirements, enters step S23; when there is a comparison target period whose distribution deviation factor does not meet the requirements, it is determined that the change in user credit risk within the target period does not meet the requirements.

[0107] Embodiment 2 In the second aspect, as Figure 5 As shown, the present invention provides a computer system, comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that: when the processor runs the computer program, it executes the above-mentioned method for automated processing of credit business based on a large language model.

[0108] Optionally, the determination of suspected fraudulent users among the credit application users includes steps S31-S33, specifically:

[0109] S31, based on the location distribution data of the credit application user, classifies other credit application users within a preset range from the credit application user into the same application user group, and determines the application user clustering coefficient of the credit application user according to the number of credit application users in the application user group and the distances between different credit application users;

[0110] S32 obtains the similarity between the credit application information of the credit application user and other credit application users, and uses the similarity to determine the ratio of the number of similar information items with other credit application users, determines the information similar users among other credit application users by the ratio of the number of similar information items, and determines the information similarity coefficient of the credit application user by the number of similar information users, the number of similar information items of different similar information users, and the distance between the similar information users and the credit application user;

[0111] S33 determines the fraud risk coefficient of the credit application user based on the information similarity coefficient and the applicant user clustering coefficient, and determines the suspected fraud users among the credit application users based on the fraud risk coefficient.

[0112] Optionally, before entering step S31, it is also necessary to determine the similarity of the credit application information between the credit application user and other credit application users. When it is determined based on the similarity that there is no user with similar information to the credit application user, it is directly determined that the credit application user is not a suspected fraudulent user. When it is determined based on the similarity that there is a user with similar information to the credit application user, step S31 is entered again.

[0113] Optionally, the above step S31 includes steps S311 to S313, which are specifically:

[0114] S311 determines, based on the location distribution data of the credit application user, that there are no other credit application users whose distance from the credit application user is within a preset range. If so, it is determined that the credit application user is not a suspected fraud user. When there are other credit application users whose distance from the credit application user is within the preset range, proceed to step S312;

[0115] S312: other credit application users within a preset range from the credit application user are divided into the same application user group. When the number of credit application users in the application user group is greater than the preset user number threshold, the credit application user is determined to be a suspected fraud user. When the number of credit application users in the application user group is not greater than the preset user number threshold, the process proceeds to step S313.

[0116] S313 determines the applicant user clustering coefficient of the credit applicant user by the number of credit applicant users in the applicant user group and the distance between different credit applicant users. When the applicant user clustering coefficient of the credit applicant user does not meet the requirements, it is determined that the credit applicant user is a suspected fraud user. When the applicant user clustering coefficient of the credit applicant user does not meet the requirements, step S33 is entered.

[0117] Optionally, the above step S32 includes steps S321-S323, specifically:

[0118] S321 obtains the similarity between the credit application information of the credit application user and other credit application users, and uses the similarity to determine the ratio of similar information items with other credit application users, and determines the information-similar users among other credit application users by the ratio of similar information items. When the number of similar users is greater than a preset similar user number threshold, it is determined that the credit application user is a suspected fraud user. When the number of similar users is not greater than the preset similar user number threshold, enter step S322;

[0119] S322 calculates the number of information-similar users in the applicant user group based on the distances between different information-similar users and the credit application user. When the number of information-similar users in the applicant user group is greater than the number setting value, it is determined that the credit application user is a suspected fraud user. When the number of information-similar users in the applicant user group is not greater than the number setting value, the process proceeds to step S323.

[0120] S323 determines the information similarity coefficient of the credit application user based on the number of information-similar users, the number of similar information items of different information-similar users, and the distance between the user and the credit application user. When the information similarity coefficient does not meet the requirements, it is determined that the credit application user is a suspected fraud user. When the information similarity coefficient meets the requirements, it enters step S33.

[0121] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0122] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0123] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A method for automatic processing of credit business based on a large language model, characterized in that: Specifically include: Acquire overdue user data of the target credit business within the target period, and when determining that the target credit business has approval risk using the overdue user data, proceed to the next step; Determine the identification result of the user credit risk of the credit application information of the credit application user within the target period in the credit approval model, and through the change of the distribution data of the credit application users in different credit risk intervals within the target period, determine that the change of the user credit risk within the target period meets the requirements, and then proceed to the next step; Based on the analysis results of the credit application information of the credit application user, determine the location distribution data of the credit application user, and determine the suspected fraudulent users among the credit application users in combination with the analysis results of the credit application information of different credit application users; Vectorize all credit application data of the suspected fraudulent user during the credit application process, and automatically generate a credit report using a large language model based on the vectorized processing results of the credit application information excluding the suspected fraudulent user; Determine whether the target credit business has approval risks, including: Determining the overdue amounts of different overdue users based on the overdue user data of the target credit business within the target period; Based on the overdue amount, the overdue users are divided into different overdue amount intervals, and the overdue proportion factors of the different overdue amount intervals are determined according to the proportion of overdue users in the different overdue amount intervals among the credit application users; Determine preset weight coefficients for different overdue amount intervals, and determine the approval risk factor of the target credit business in combination with the overdue proportion factors of the different overdue amount intervals, and determine whether the target credit business has an approval risk through the approval risk factor; Determine whether the changes in user credit risk during the target period meet the requirements, including: Taking the target credit business in the adjacent preset number of target cycles as the comparison target cycle, and determining the distribution ratio of the credit application users in different credit risk intervals based on the distribution data of the credit application users in different credit risk intervals in the target cycle; Determine the deviation from the distribution ratio of different comparison target periods based on the distribution ratio of credit application users in different credit risk intervals, and determine the deviation interval in the credit risk interval based on the deviation of the distribution ratio, and determine the distribution deviation factor of the comparison target period based on the number of the deviation intervals; Determine the coefficient of variation of user credit risk within the target period by using the average values ​​of distribution deviation factors of different comparison target periods, and determine whether the variation of user credit risk within the target period meets the requirements through the coefficient of variation; The location distribution data of the credit application user is determined according to the analysis result of the positioning data corresponding to the credit application information of the credit application user; The method for determining the suspected fraudulent users among the credit application users is as follows: Based on the location distribution data of the credit application user, other credit application users within a preset range from the credit application user are divided into the same application user group, and the number of credit application users in the application user group is determined; Obtaining similarities between the credit application information of the credit application user and other credit application users, and using the similarities to determine the ratio of similar information items to other credit application users, and determining information-similar users among other credit application users through the ratio of similar information items; The fraud risk coefficient of the credit application user is determined based on the number of the credit application users and the number of users with similar information, and the suspected fraud users among the credit application users are determined based on the fraud risk coefficient.

2. The method for automatically processing credit business based on a large language model according to claim 1, characterized in that: The target period ranges from 1 month to 3 months, and is specifically determined according to the number of credit application users of the target credit business.

3. The credit business automation processing method based on a large language model according to claim 1, characterized in that: The overdue user data includes the number of overdue users and the overdue amount of the overdue users.

4. The method for automatically processing credit business based on a large language model according to claim 1, characterized in that: Determining whether the target credit business has an approval risk through the approval risk factor specifically includes: When the approval risk factor is not within a preset range, it is determined that the target credit business has an approval risk.

5. The credit business automation processing method based on a large language model according to claim 1, characterized in that: The change in the distribution data of the credit application user is determined based on the deviation from the distribution data of the target credit business within a preset number of target cycles.

6. The credit business automation processing method based on a large language model according to claim 1, characterized in that: When the change of the user's credit risk within the target period does not meet the requirements, the credit report is automatically generated using a large language model based on the vectorized processing result of the credit application information of the credit applicant.

7. The method for automatically processing credit business based on a large language model according to claim 6, characterized in that: The deviation range is the credit risk range where the deviation of the distribution ratio does not meet the requirements.

8. A computer system comprising: A memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that: when the processor runs the computer program, it executes a method for automated processing of credit business based on a large language model as described in any one of claims 1-7.

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