Credit scoring and behavioral scoring have become very important credit risk management tasks during the past few years due to the impact of several financial crises. The objective of the proposed study is to explore the performance of behavioral scoring using three commonly discussed data mining techniqueslinear discriminant analysis (LDA), backpropagation neural networks (BPN), and support vector machine (SVM). To demonstrate the effectiveness of behavioral scoring using the above-mentioned techniques, behavioral scoring tasks are performed on one bank credit card dataset in Taiwan. As the results reveal, BPN outperforms other techniques in terms of overall scoring accuracy and hence is an efficient alternative in implementing behavioral scoring tasks.

In this paper, a time series forecasting approach by integrating particle swarm optimization (PSO) and support vector regression (SVR) is proposed. SVR has been widely applied in time series predictions. However, no general guidelines are available to choose the free parameters of an SVR model. The proposed approach uses PSO to search the optimal parameters for model selections in the hope of improving the performance of SVR. In order to evaluate the performance of the proposed approach, the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) closing cash index is used as the illustrative example. Experimental results show that the proposed model outperforms the traditional SVR model and provides an alternative in financial time series forecasting.