Selecting useful features for personal credit risk analysis

Abstract

The recent credit crisis has renewed regulatory concerns of industrial interest in credit risk analysis. To reduce exposure to credit default, it thus becomes a crucial motive to select vital features to analyse the customer's credit profiles. This desired set of features can be generated through data mining techniques such as feature selection methods. However, each feature selection method has its advantages and limitations. In practice, using a single method inevitably introduces undesirable estimation bias. Instead, this paper proposes a bagging feature selection model, which is an ensemble learning approach, to identify the most significant features that determine the credit worthiness of customers. The experimental results demonstrate promising results using bagging feature selection model as compared to fundamental models for personal credit risk analysis.