Credit Risk Management

A commercial bank faced significant challenges in its SME lending business. The bank serves over 100,000 small and medium-sized enterprise (SME) clients and processes hundreds of credit applications on a daily basis. However, its traditional risk control approaches revealed clear limitations:

— Fragmented Data:
Enterprise credit data is scattered across more than 200 data sources, including business registration, taxation, judicial records, and financial systems. Traditional tools struggle to achieve unified integration, resulting in high information acquisition costs and significant delays.

— High Analytical Barriers:
Credit officers are required to manually cross-check data across multiple dimensions, with limited intelligent analytical support. Completing a comprehensive enterprise credit assessment report typically takes 3–5 business days.

— Delayed Risk Response:
Complex risk patterns—such as hidden related-party risks and financial fraud—are difficult to identify in real time. As a result, annual non-performing loan losses exceed RMB 10 million.

— Mounting Compliance Pressure:
With increasingly stringent regulatory requirements, traditional risk control systems are unable to meet the demands of penetrative supervision and real-time early warning.

Key questions included:

— How can related-party risks and financial anomalies be identified rapidly?
— How can the credit approval cycle be shortened while improving operational efficiency?
— How can risk early warning be made more accurate and real-time?

Enterprise-Wide Data Integration and Intelligent Modeling
The Qimingdeng platform first integrates both internal and external banking data, building an enterprise-wide database covering 200+ dimensions, including business registration, judicial records, taxation, and financial data. Leveraging the Aladdin Enterprise Credit Scoring System, the platform conducts credit scoring for over 100,000 enterprises, establishing a foundational risk control model.

Intelligent Risk Control System Deployment
Enterprises are quantitatively scored on a 0–1000 scale based on 142 indicators and categorized into five risk levels. Using relationship graph technologies, the system identifies hidden related parties and enables penetrative risk assessment. Comprehensive ratings are generated across four dimensions—holistic corporate profiling, financial analysis, financial behavior, and business ecosystem analysis. The platform also automatically parses corporate financial statements to detect financial anomalies and fraud risks.

End-to-End Intelligent Process Upgrade
With the support of an intelligent Agent-based decision assistant, credit officers can generate risk assessment reports through natural language instructions. The platform supports real-time risk monitoring and immediately triggers alerts upon the detection of anomalies.