Risk Control Model Construction for Online Small Loans: Pre‑loan, In‑loan, Post‑loan and Monitoring

The presentation introduces the main content of an online small‑loan risk control sharing, focusing on model construction across pre‑loan, in‑loan, post‑loan, and monitoring stages.

Pre‑loan risk control includes identity verification, anti‑fraud assessment, credit risk assessment, and quota‑rate matching. A typical workflow consists of admission strategy, pre‑model rules, credit scoring models (often multiple), post‑model rules, and final user rating, forming a cost‑funnel where cheap rules filter users before expensive data sources are consulted. The system architecture comprises a data ingestion module (internal and external data), a feature engine for real‑time feature calculation, a model engine (supporting both asynchronous “dry‑run” and synchronous decision models), and a decision engine that configures decision flows and rule sets.

In‑loan management differs from pre‑loan by operating after disbursement, requiring continuous observation, dynamic risk assessment, and actions such as credit limit adjustments or freezing. It introduces batch scheduling for data retrieval, offline feature storage, and both real‑time and offline model scoring. Common use cases include repeat‑loan admission, existing‑customer operation, and various model tools (behavior models, early‑settlement models, loan‑intent evaluation).

Post‑loan management aims to improve repayment rates and reduce defaults, primarily using operational tools with models as auxiliary aids. Typical models include repayment‑rate prediction and complaint‑risk prediction, which help staff take differentiated actions based on risk scores.

Monitoring construction covers business, model, feature, and data monitoring, with daily checks and alert mechanisms. Visual dashboards illustrate monitoring points for each dimension, ensuring model accuracy, stability and data quality.

The article concludes by summarizing the end‑to‑end risk control workflow, the differences in deployment across stages, and the typical contents of monitoring and alert systems.