Adaptive credit scoring with kernel learning methods

Credit scoring is a method of modelling potential risk of credit applications. Traditionally, logistic regression, linear regression and discriminant analysis are the most popular approaches for building credit scoring models. Despite their popularity, quite a few limitations are known to be associated with these methods, such as instability with high-dimensional data (also known as combinatorial explosion) and small sample size, requirement for intensive data pre-processing through variable selection/reduction analysis and incapability of efficiently handling non-linear components. Most importantly, based on these algorithms, it is difficult to automate the modelling process and design a continuous workflow. When environment or population changes occur, the static models usually fail to adapt and may need to be rebuilt from scratch. In this paper, a kernel learning method is used to derive a novel and practical adaptive credit scoring system capable of adjusting the model on-line. Th...

• Type: Journal
• Year: 2007
• Where: EOR
• Authors: Yingxu Yang
• Keywords: Credit Scoring, Credit Scoring Models, EOR 2007, Popular Gaussian Kernel