This experiment will predict if a customer has a probability to be put into default into payment based on customer data and payment history.
This research aimed at the case of customers default payments in Taiwan and compares the predictive accuracy of probability of default among six data mining methods. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification - credible or not credible clients. Because the real probability of default is unknown, this study presented the novel Sorting Smoothing Method to estimate the real probability of default. With the real probability of default as the response variable (Y), and the predictive probability of default as the independent variable (X), the simple linear regression result (Y = A + BX) shows that the forecasting model produced by artificial neural network has the highest coefficient of determination; its regression intercept (A) is close to zero, and regression coefficient (B) to one. Therefore, among the six data mining techniques, artificial neural network is the only one that can accurately estimate the real probability of default. This experiment also tried to compare the performance of Two-Class Neural Network model versus the Two-Class Bayes Point model in terms of predicting the risk of customer going into payment default. All data used in this experiment came from the University of California Irvine research team. For reference and for future data needs, kindly visit https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients.