Credit decision algorithm for SMEs based on data mining

Nowadays, SMEs have difficulty in obtaining credit due to relatively small scale, lack of information on mortgage assets and low information transparency. In practice, banks have problems such as heavy workload and strong subjectivity in formulating credit strategies for SMEs. Our group mainly focuses on the credit decision-making problems of SMEs, mining invoice data in business operations, and establishing an entropy-TOPSIS evaluation model to evaluate credit risk. Based on this, a multi-objective decision-making model is made for the three goals of maximizing bank benefits, minimizing risks, and minimizing customer churn. Finally, we use statistical data verification and genetic algorithm to solve the problem, trying to establish a unified evaluation and decision-making method to solve the problem of small, medium and micro enterprise credit.