1. Use data mining and machine learning algorithms to establish a credit risk assessment model to predict the possible overdue probability of borrowers. Commonly used algorithms include decision tree, random forest, neural network and so on.

2. Retrospectively analyze overdue borrowers, find out the reasons for overdue, such as the deterioration of financial situation and the decline of repayment willingness, so as to make a more accurate prediction of future risks.

3. Analyze the overdue situation of different types of borrowers, understand which groups are more likely to be overdue, and then formulate corresponding risk control strategies.

4. Mining information from non-traditional data sources (such as social media, e-commerce platform, mobile payment, etc.) with the help of big data technology and artificial intelligence algorithms. ) to assist risk assessment and overdue prediction.

5. Introduce blockchain technology, strengthen borrower identity verification and credit traceability, and improve data security and credibility.