Application of Smoothing Spline and Support Vector Machine Regression for Prediction and Clustering of Consumer Credit Risk
Date Issued
2007
Date
2007
Author(s)
Wu, Min-Hua
Abstract
This research mainly focuses on forecasting and clustering retail banking customers by their credit risk metrics, such as probability of default and exposure. Forecasting the probability of default and exposure is critical in credit risk management. The credit loss should be estimated correctly for banks to allocate fund more efficiently, avoid running out of cash and maintain a bank''s credit rating at its target level.he importance of customer segmentation is also emphasized. Segmentation affects the credit risk measurement of portfolio. Banks can also make profit by applying marketing strategies to different customer segments.wo nonlinear curve fitting models, Smoothing Spline and Support Vector Machine Regression (SVR), are used to identify the patterns of customer behavior. We then cluster customers by their different behavior patterns.e show that both smoothing spline and SVR are better in capturing the patterns of PD and EAD curves than polynomial regression. However, when predicting new vintage, SVR outperforms smoothing spline with its characteristic of allowing a small deviation. We also modify the k-means clustering method to cluster customers by fitted PD and EAD curves together, and find that smoothing spline is better than SVR in clustering.
Subjects
Smoothing Spline
Support Vector Machine Regression
Clustering
Consumer Credit Risk
Type
thesis
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