Optimal cost-sensitive credit scoring using a new hybrid performance metric

This paper aims to provide an optimal credit rating by using a new hybrid performance metric, including several performance metrics, referred to as the probability of credit scoring correctness. There have been several credit -scoring models proposed in the banking system. These models, either single classifiers or hybrid and ensemble models determine the class of customers (good or bad accounts). The banks need the probability of default to calculate the expected loss and the unexpected loss and then determine whether their capital is adequate. Generally speaking, the proposed model can utilize n classifiers and m performance metrics, the larger n, and m, the more diverse and more reliable the model is as an estimate of customer classes. We evaluated the proposed model using five common classifiers, namely k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), AdaBoost, and probabilistic neural network (PNN), and eight performance metrics. To extract the feature, an optimization approach using genetic algorithms and nine different scenarios were used. We have then calculated the optimal parameters of each algorithm simultaneously by defining the cost-sensitive objective function based on the probability of correctly classifying it into two classes 0 (good) and 1 (bad). We evaluated the performance of the proposed hybrid model using the Iranian dataset and the UCI German and Australian datasets. The results indicate the proposed method provides very good scoring performance. Using this approach, the bank can filter its bad customers based on the tolerance it considers for the probability value, so it can decide whether it should provide loans to that customer.