Identifying a Default of Credit Card Clients by using a LSTM Method: A Case Study

Detecting fraudulent transactions is critical and challenging for financial banks and institutes. This study used a deep learning technique, which is a long short-term memory (LSTM) method, for identifying a default of credit card clients (an imbalanced dataset). To evaluate the performance of optimizers for the LSTM approach, this study employed three optimizers based on gradient methods, such as adaptive moment estimation (Adam), stochastic gradient descent with momentum (Sgdm) and root mean square propagation (Rmsprop). This study used 10-fold cross-validation. Moreover, this study compared the best numerical results of the LSTM method with those of supervised machine learning classifiers, which are back-propagation neural network (BPNN) with a gradient descent algorithm (GDA) and a scaled conjugate gradient algorithm (SCGA). Numerical results indicate that the LSTM-Adam and the BPNN-SCGA classifiers have identical performance, and that selecting an appropriate classification threshold value is important for an imbalanced dataset. Based on the numerical results, the LSTM-Adam classifier can be considered for dealing with credit scoring problems, which are binary classification problems.