Credit risk assessment and dynamic limit adjustment method and system

Abstract

The invention relates to the technical field of financial credit investigation and credit risk management, and particularly provides a credit risk assessment and dynamic limit adjustment method and system, and the method comprises the following steps: S1, data collection and analysis; s2, risk assessment and quota allocation; s3, customer classification and adjustment; and S4, dynamic monitoring and emergency. Compared with the prior art, the real-time data can be combined with the dynamic model, so that the hysteresis of the static scoring model is reduced, and the timeliness of risk assessment is enhanced. Manual intervention is reduced, and the default rate and the collection cost are reduced through an automatic strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of financial credit investigation and credit risk management, and specifically provides a credit risk assessment and dynamic quota adjustment method and system. Background Art

[0002] In the field of financial credit investigation, traditional credit assessment methods usually rely on static scoring models and historical data, and it is difficult to cope with market fluctuations and dynamic changes in user behavior. This results in some customers being overly restricted in their quotas due to changes in the credit environment, while the credit potential of other low-risk customers is not fully explored, affecting the overall resource allocation efficiency and business returns. In addition, when the market environment changes drastically, traditional methods cannot adjust strategies in real time, easily leading to systemic risks.

[0003] In the prior art, rule-based static risk control models lack flexibility, while simply relying on machine learning models may lead to decision-making lags due to delayed data updates. Therefore, there is an urgent need for a credit management strategy that integrates real-time data monitoring and dynamic optimization algorithms to improve the timeliness of risk assessment and the rationality of quota allocation. Summary of the Invention

[0004] The present invention aims at the deficiencies of the above-mentioned prior art and provides a practical credit risk assessment and dynamic quota adjustment method.

[0005] A further technical task of the present invention is to provide a credit risk assessment and dynamic quota adjustment system with reasonable design, safety and applicability.

[0006] The technical solution adopted by the present invention to solve its technical problems is as follows:

[0007] A credit risk assessment and dynamic quota adjustment method has the following steps:

[0008] S1. Data collection and analysis;

[0009] S2. Risk assessment and quota allocation;

[0010] S3. Customer classification and adjustment;

[0011] S4. Dynamic monitoring and emergency response.

[0012] Further, in step S1, user credit data and market dynamic data are collected in real time, cleaned, integrated and feature extracted through a big data platform, and the difference between the theoretical credit score and the actual default rate is calculated.

[0013] Further, in step S2, when the difference between the theoretical credit score and the actual default rate exceeds the threshold, the dynamic adjustment strategy is initiated. Based on historical data and the real-time market change rate, the credit limit increment ΔC that the system needs to adjust is calculated, and customers are divided into a high-credit queue and a risk queue.

[0014] Further, in step S3, for the customers in RiskList, according to the repayment ability, data update frequency, and risk prediction results, the adjustment value Δ for each household is set to ensure that the total adjustment value does not exceed ΔC;

[0015] For the customers in StableList, the remaining limit increment is allocated according to the credit score ratio to fully exploit the credit potential.

[0016] Further, in step S4, the market fluctuations and customer behavior changes are monitored in real time. If the proportion of risky customers exceeds 70% due to severe market fluctuations, then switch to the conservative mode and allocate the limit according to the theoretical risk ratio;

[0017] If the market environment improves, then continue to apply the dynamic Δ adjustment strategy.

[0018] A credit risk assessment and dynamic limit adjustment system first conducts data collection and analysis, then conducts risk assessment and limit allocation, customer classification and adjustment, and finally conducts dynamic monitoring and emergency response.

[0019] Further, in data collection and analysis, the user credit data and market dynamic data are collected in real time, and through the big data platform, they are cleaned, integrated, and feature extracted, and the difference between the theoretical credit score and the actual default rate is calculated.

[0020] Further, when conducting risk assessment and limit allocation, when the difference between the theoretical credit score and the actual default rate exceeds the threshold, the dynamic adjustment strategy is initiated. Based on historical data and the real-time market change rate, the credit limit increment ΔC that the system needs to adjust is calculated, and customers are divided into a high-credit queue and a risk queue.

[0021] Further, when conducting customer classification and adjustment, for the customers in RiskList, according to the repayment ability, data update frequency, and risk prediction results, the adjustment value Δ for each household is set to ensure that the total adjustment value does not exceed ΔC;

[0022] For the customers in StableList, the remaining limit increment is allocated according to the credit score ratio to fully exploit the credit potential.

[0023] Further, when conducting dynamic monitoring and emergency response, the market fluctuations and customer behavior changes are monitored in real time. If the proportion of risky customers exceeds 70% due to severe market fluctuations, then switch to the conservative mode and allocate the limit according to the theoretical risk ratio;

[0024] If the market environment improves, the dynamic Δ adjustment strategy will continue to be applied.

[0025] Compared with the prior art, a credit risk assessment and dynamic quota adjustment method and system of the present invention has the following prominent beneficial effects:

[0026] By combining real-time data with a dynamic model, the present invention reduces the lag of the static scoring model and enhances the timeliness of risk assessment. Dynamically adjust the quotas of high-risk customers, and at the same time release the credit potential of low-risk customers to improve the overall business income. Quickly switch the risk control mode in the event of a sudden credit event to avoid the spread of systemic risks. Reduce manual intervention and lower the default rate and collection cost through automated strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0028] Attached Figure 1 is a schematic flowchart of a credit risk assessment and dynamic quota adjustment method. DETAILED DESCRIPTION OF THE INVENTION

[0029] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will further elaborate on the present invention in conjunction with specific embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0030] The following gives an optimal embodiment:

[0031] As Figure 1 shown, a credit risk assessment and dynamic quota adjustment method in this embodiment has the following steps:

[0032] S1. Data collection and analysis;

[0033] Real-time collect user credit data (such as income, liabilities, consumption behavior, social information, etc.) and market dynamic data (such as economic indices, industry risk indicators), clean, integrate and extract features through a big data platform, calculate the difference between the theoretical credit score and the actual default rate, and provide data support for subsequent strategies.

[0034] S2. Risk assessment and quota allocation;

[0035] On the premise of meeting regulatory compliance requirements, when the difference between the theoretical credit score and the actual default rate exceeds a threshold value (such as 5%), the dynamic adjustment strategy is initiated.

[0036] According to historical data and the real-time market change rate, calculate the credit limit increment ΔC that the system needs to adjust, and divide customers into a high-credit queue (StableList, credit score > 700) and a risk queue (RiskList, credit score < 600 and there is overdue behavior recently).

[0037] S3. Customer classification and adjustment;

[0038] For customers in the RiskList, according to their repayment ability, data update frequency, and risk prediction results, set the adjustment value Δ for each household (such as reducing the limit by 10%) to ensure that the total adjustment value does not exceed ΔC.

[0039] For customers in the StableList, allocate the remaining limit increment according to the proportion of the credit score to fully tap their credit potential.

[0040] S4. Dynamic monitoring and emergency response;

[0041] Monitor market fluctuations and customer behavior changes in real time. If the proportion of risky customers exceeds 70% due to severe market fluctuations (such as an industry crisis), switch to the conservative mode and allocate limits according to the theoretical risk ratio;

[0042] If the market environment improves, continue to apply the dynamic Δ adjustment strategy to ensure the flexibility and stability of the system.

[0043] Based on the above method, in a credit risk assessment and dynamic limit adjustment system in this embodiment, data collection and analysis are first performed, then risk assessment and limit allocation, customer classification and adjustment, and finally dynamic monitoring and emergency response.

[0044] Among them, in data collection and analysis, user credit data and market dynamic data are collected in real time, cleaned, integrated, and feature extracted through a big data platform, and the difference between the theoretical credit score and the actual default rate is calculated.

[0045] When performing risk assessment and limit allocation, when the difference between the theoretical credit score and the actual default rate exceeds the threshold, initiate the dynamic adjustment strategy, calculate the credit limit increment ΔC that the system needs to adjust according to historical data and the real-time market change rate, and divide customers into a high-credit queue and a risk queue.

[0046] When performing customer classification and adjustment, for customers in the RiskList, according to the repayment ability, data update frequency, and risk prediction results, set the adjustment value Δ for each household to ensure that the total adjustment value does not exceed ΔC;

[0047] For customers in the StableList, allocate the remaining quota increment according to the credit score ratio to fully tap the credit potential.

[0048] When conducting dynamic monitoring and emergency response, monitor market fluctuations and changes in customer behavior in real time. If the proportion of risky customers exceeds 70% due to severe market fluctuations, switch to the conservative mode and allocate the quota according to the theoretical risk ratio.

[0049] If the market environment improves, continue to apply the dynamic Δ adjustment strategy.

[0050] The above specific implementation manners are only specific cases of the present invention. The patent protection scope of the present invention includes but is not limited to the above specific implementation manners. Any technical solution that conforms to the above specific implementation manners of the present invention and any appropriate changes or substitutions made by those of ordinary skill in the relevant technical field shall fall within the patent protection scope of the present invention.

[0051] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A credit risk assessment and dynamic quota adjustment method, characterized in that It has the following steps: S1. Data collection and analysis; S2. Risk assessment and quota allocation; S3. Customer classification and adjustment; S4. Dynamic monitoring and emergency response.

2. The credit risk assessment and dynamic quota adjustment method according to claim 1, characterized in that In step S1, user credit data and market dynamic data are collected in real time, cleaned, integrated and feature-extracted through a big data platform, and the difference between the theoretical credit score and the actual default rate is calculated.

3. A credit risk assessment and dynamic quota adjustment method according to claim 2, characterized in that In step S2, when the difference between the theoretical credit score and the actual default rate exceeds the threshold, a dynamic adjustment strategy is initiated. According to historical data and the real-time market change rate, the credit quota increment ΔC that the system needs to adjust is calculated, and customers are divided into a high-credit queue and a risk queue.

4. A credit risk assessment and dynamic quota adjustment method according to claim 3, characterized in that In step S3, for the customers in RiskList, according to the repayment ability, data update frequency and risk prediction results, the quota adjustment value Δ for each household is set to ensure that the total adjustment value does not exceed ΔC; For the customers in StableList, the remaining quota increment is allocated according to the credit score ratio to fully tap the credit potential.

5. The credit risk assessment and dynamic quota adjustment method according to claim 4, characterized in that In step S4, the market fluctuations and customer behavior changes are monitored in real time. If the proportion of risk customers exceeds 70% due to severe market fluctuations, switch to the conservative mode and allocate the quota according to the theoretical risk ratio; If the market environment improves, continue to apply the dynamic Δ adjustment strategy.

6. A credit risk assessment and dynamic quota adjustment system, characterized in that, First, data collection and analysis are carried out, then risk assessment and quota allocation, customer classification and adjustment, and finally dynamic monitoring and emergency response.

7. The credit risk assessment and dynamic quota adjustment system according to claim 6, characterized in that In data collection and analysis, user credit data and market dynamic data are collected in real time, cleaned, integrated and feature-extracted through a big data platform, and the difference between the theoretical credit score and the actual default rate is calculated.

8. A credit risk assessment and dynamic quota adjustment system according to claim 7, characterized in that, When carrying out risk assessment and quota allocation, when the difference between the theoretical credit score and the actual default rate exceeds the threshold, a dynamic adjustment strategy is initiated. According to historical data and the real-time market change rate, the credit quota increment ΔC that the system needs to adjust is calculated, and customers are divided into a high-credit queue and a risk queue.

9. The credit risk assessment and dynamic quota adjustment system according to claim 8, wherein, When carrying out customer classification and adjustment, for the customers in RiskList, according to the repayment ability, data update frequency and risk prediction results, the quota adjustment value Δ for each household is set to ensure that the total adjustment value does not exceed ΔC; For the customers in StableList, the remaining quota increment is allocated according to the credit score ratio to fully tap the credit potential.

10. A credit risk assessment and dynamic quota adjustment system according to claim 9, characterized in that When carrying out dynamic monitoring and emergency response, the market fluctuations and customer behavior changes are monitored in real time. If the proportion of risk customers exceeds 70% due to severe market fluctuations, switch to the conservative mode and allocate the quota according to the theoretical risk ratio; If the market environment improves, continue to apply the dynamic Δ adjustment strategy.

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