Data Mining in Building Behavioral Scoring Models

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 techniques-linear 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.