Application of a rule extraction algorithm family based on the Re-RX algorithm to financial credit risk assessment from a Pareto optimal perspective

Historically, the assessment of credit risk has proved to be both highly important and extremely difficult. Currently, financial institutions rely on the use of computer-generated credit scores for risk assessment. However, automated risk evaluations are currently imperfect, and the loss of vast amounts of capital could be prevented by improving the performance of computerized credit assessments. A number of approaches have been developed for the computation of credit scores over the last several decades, but these methods have been considered too complex without good interpretability and have therefore not been widely adopted. Therefore, in this study, we provide the first comprehensive comparison of results regarding the assessment of credit risk obtained using 10 runs of 10-fold cross validation of the Re-RX algorithm family, including the Re-RX algorithm, the Re-RX algorithm with both discrete and continuous attributes (Continuous Re-RX), the Re-RX algorithm with J48graft, the Re-RX algorithm with a trained neural network (Sampling Re-RX), NeuroLinear, NeuroLinear+GRG, and three unique rule extraction techniques involving support vector machines and Minerva from four real-life, two-class mixed credit-risk datasets. We also discuss the roles of various newly-extended types of the Re-RX algorithm and high performance classifiers from a Pareto optimal perspective. Our findings suggest that Continuous Re-RX, Re-RX with J48graft, and Sampling Re-RX comprise a powerful management tool that allows the creation of advanced, accurate, concise and interpretable decision support systems for credit risk evaluation. In addition, from a Pareto optimal perspective, the Re-RX algorithm family has superior features in relation to the comprehensibility of extracted rules and the potential for credit scoring with Big Data.