Micro and Small Enterprises Credit Risk and Response Strategies to Emerging Factors Based on Big Data-Driven Multi-Objective Nonlinear Model

This study constructs a framework based on a multi-objective non-linear programming model, aiming to systematically explore the credit risk and contingency factors coping strategies for SMEs. By taking into account a variety of factors such as national policy inclination, the degree of industry influence, enterprise size and customer turnover rate, the model provides banks with optimal credit decisions in a complex economic environment. The results show that in the context of emergencies such as epidemics, banks can support affected SMEs by adjusting loan amounts and interest rate preferences, while effectively controlling credit risks. Although the model shows high effectiveness in theoretical and empirical analyses, it still faces challenges such as data acquisition and model solving in practical applications. Future research can combine machine learning and big data technologies to further improve the prediction accuracy and applicability of the model. This study not only enriches the theoretical framework of financial risk management, but also provides scientific and systematic guidance for banks' lending decisions in special economic environments, which has important academic and practical value.