AN EFFECTIVE CREDIT SCORING MODEL BASED ON FEATURE SELECTION APPROACHES

Abstract

Recent finance and debt crises have made credit risk management one of the most important issues in financial research. Credit scoring is one of the most important issues in financial decision-making. Reliable credit scoring models are crucial for financial agencies to evaluate credit applications and have been widely studied in the field of machine learning and statistics. In this paper, we propose an effective credit scoring model based on feature selection approaches. Feature selection is a process of selecting a subset of relevant features, which can decrease the dimensionality, shorten the running time, and/or improve the classification accuracy. Using the standard k-nearest-neighbors (kNN) rule as the classification algorithm, the feature selection methods are evaluated in classification tasks. Two well-known and readily available such as: Australia and German dataset has been used to test the algorithm. The results obtained by feature selection approaches shown have been superior to state-of-the-art classification algorithms in credit scoring.