Method and system for risk control support of community micro entrepreneurship

Abstract

The invention relates to the technical field of community finance science and technology and risk control, in particular to a community micro entrepreneurship risk control support method and system combining credit assessment, neighborhood guarantee, dynamic risk prediction and hierarchical hosting loaning. The system comprises a server (including a processor and a memory), a communication interface, a credit evaluation module, a guarantee management module, a hosting loaning module, a default processing module, a dynamic guarantee management unit, a real-time monitoring unit and a default prediction unit. The modules cooperatively realize applicant admission verification, multi-dimensional risk assessment, batch trusteeship loaning according to risk levels and default deduction, a real-time monitoring and condition triggering mechanism is further introduced in the loaning process, and default risks are predicted in combination with a machine learning model. When the risk is increased, guarantee supplement or advanced intervention measures are triggered, so that fund safety is guaranteed, and the construction level of a community credit system is improved. The method and the system are suitable for community entrepreneurship scenes which are low in threshold, small in amount and lack of collateral.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of community financial technology and risk control, more specifically to a community micro-entrepreneurship risk control support method and system combining credit points, neighborhood guarantee, dynamic risk prediction and hierarchical loan management. BACKGROUND

[0002] Community micro-entrepreneurship provides residents with low-threshold economic income opportunities, but due to the lack of collateral and credit records among most entrepreneurs, traditional loan models are difficult to effectively support such entrepreneurial activities, with high default risk and difficult fund recovery. Existing solutions often overlook the mechanism of combining dynamic risk prediction and hierarchical lending, failing to achieve real-time monitoring and early intervention during the lending process. Therefore, a comprehensive risk control system that integrates credit scoring, neighborhood guarantee, real-time monitoring and default prediction is needed to ensure fund safety and promote community economic development. SUMMARY

[0003] The present application aims to provide a community micro-entrepreneurship risk control support method and system that combines credit points, neighborhood guarantee, dynamic risk prediction and hierarchical loan management, achieving precise lending and dynamic risk intervention through multi-dimensional credit calculation and risk grading, thereby reducing community entrepreneurship barriers and ensuring fund safety and sustainable operation of the credit system.

[0004] ADVANTAGEOUS EFFECTS Fusion of multi-dimensional credit data and community contribution to achieve more accurate credit scoring; Introduction of machine learning model to predict default probability for early intervention; Combination of hierarchical loan management and condition triggering mechanism to improve fund use efficiency and safety; Dynamic guarantee supplement mechanism to improve risk response ability and reduce default loss. BRIEF DESCRIPTION OF DRAWINGS

[0005] Figure 1 System structure schematic diagram.

[0006] Reference signs: 1 - credit assessment module; 2 - guarantee management module; 3 - dynamic guarantee management module; 4 - loan management module; 5 - default prediction and processing module; 6 - real-time monitoring module; 7 - server; 8 - communication interface.

[0007] Figure 2 Credit calculation and risk assessment flowchart.

[0008] Reference signs: 11 - data collection; 12 - weighted calculation; 13 - generate comprehensive credit score; 21 - risk assessment; 22 - generate risk level.

[0009] Figure 3Flowchart for tiered lending and real-time monitoring.

[0010] Reference signs: 22 - risk level; 41 - batched escrow lending; 42 - real-time monitoring; 43 - condition verification; 44 - continue lending; 45 - freeze / adjust.

[0011] Figure 4 Flowchart for default prediction and handling closed loop.

[0012] Reference signs: 31 - dynamic guarantee supplement; 42 - real-time monitoring; 51 - default prediction model; 52 - early intervention; 53 - risk escalation. DETAILED DESCRIPTION

[0013] As shown in Figure 1 , the system includes a credit evaluation module, a guarantee management module, a dynamic guarantee management module, an escrow lending module, a real-time monitoring module, and a default prediction and handling module, which realize data interaction and collaborative work through servers and communication interfaces.

[0014] As shown in Figure 2 , the credit evaluation module collects historical credit data of the applicant and the guarantor and calculates the comprehensive credit score combined with community contribution, and the risk assessment module generates the risk level according to the comprehensive credit score and the feasibility of the business plan.

[0015] As shown in Figure 3 , the escrow lending module releases funds in batches according to the risk level, and the real-time monitoring module collects project progress and financial status during the lending period. If the conditions are triggered, the lending strategy will be suspended or adjusted.

[0016] As shown in Figure 4 , the default prediction and handling module uses machine learning models to predict the probability of default, and triggers dynamic guarantee supplement or early intervention measures when the risk escalates, forming a risk control closed loop.

Claims

1. A method for risk control support for community micro-entrepreneurship, characterized in that, include: (1) Credit calculation steps: Collect the applicant's and guarantor's historical credit data, guarantee records and community contributions, and perform weighted calculations to generate a comprehensive credit score; (2) Risk assessment steps: Generate risk level based on comprehensive credit score, feasibility of business plan and historical default rate; (3) Tiered loan disbursement process: Implement escrow loans in batches according to risk level and set condition triggering mechanisms; (4) Real-time monitoring steps: Collect data on entrepreneurial progress and financial status during the loan period and assess changes in risk; (5) Default prediction and handling steps: Use machine learning models to predict the probability of default and trigger guarantee supplementation or early intervention measures when the risk increases.

2. A community micro-entrepreneurship risk control support system for implementing the method of claim 1, comprising: - Servers, including processors and memory; - Communication interface for data interaction with the applicant's terminal, the guarantor's terminal and external account system; - Credit assessment module, used to calculate the credit scores of the applicant and guarantor; - The guarantee management module is used to record guarantor information and verify guarantee conditions; - The escrow loan module is used to execute batch escrow loans based on risk level; - Default handling module, used to deduct funds from the guarantor's account and inject the proceeds into the risk reserve in the event of default; - Dynamic guarantee management module, used to automatically match supplementary guarantors when risks increase; - Real-time monitoring module, used to collect startup progress and financial status during the loan period and trigger conditional verification; - Default prediction and handling module, used to predict default risk based on machine learning and perform early intervention.

3. The system according to claim 2, wherein, The credit assessment module calculates the overall credit score as follows: α × Applicant's credit score + β × Guarantor's average credit score + γ × Community contribution, where α + β + γ = 1.

4. The system according to claim 2, wherein, The risk level is divided into three levels: A, B, and C, which correspond to different loan batches and amount limits.

5. The system according to claim 2, wherein, The triggering mechanism includes conditions such as the startup progress falling below a threshold, the guarantor's credit declining, and abnormal project cash flow.

6. The system according to claim 2, wherein, The default prediction model is trained based on historical loan data, credit change trends, and financial behavior patterns.

7. The system according to claim 2, wherein, The tiered loan disbursement module features batch control, condition verification, and automatic freezing functions.

8. The system according to claim 2, wherein, The default prediction and handling module interacts with the dynamic guarantee management module to implement risk intervention measures in advance.

9. A basic embodiment corresponding to the system of claim 2, characterized in that: - Excludes the dynamic guarantee management module, real-time monitoring module, and default prediction and handling module; - Retain the credit assessment module, guarantee management module, escrow loan module, and default handling module; - The system structure and module functions are the same as the independent claims of the utility model patent application for "Community Micro-Entrepreneurship Risk Control Support System".

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