Heuristic strategies for solving the combinatorial optimization problem in real-world credit risk assessment

Credit risk assessment plays an important role in financial institutions and banks. A common method of predicting credit risks is to build an interpretable rule-set model to distinguish high-risk and non-risk users. This further assists experts and analysts to make critical decisions. In a rule-set model, the quality of selected rules dramatically influences the prediction accuracy. It is challenging to automatically select rules for a rule-set learning task.