Managing credit risk of credit cards from perspective of credit operations.

Abstract

Summary

Credit risk researchers have mainly focused on the area of wholesale and corporate loans. Modern commercial credit risk models are also built on the assumptions that are only applicable to corporate risk environment. There are little existing credit risk models that can be applied directly to consumer credit risk areas such as consumer loan portfolio credit losses forecast and customer default behavior prediction.

The objective of my thesis is to contribute to the literature by introducing models that are accordant with consumer credit risk characteristics. Unprecedentedly, we bring concepts and terminologies used by credit card industry, such as buckets and credit card account structures which are core components of risk control procedures in consumer credit area, into credit risk modeling. First, the modeling started from a simple scenario of binary response (pay/default) of a obligator over single time period. Next, we propose a portfolio credit risk model combining bucket strategy with logit models and then compared it with the traditional portfolio risk models introduced by Vasicek and implied by the Basel II Accord. Finally, a new model that can account for multi-layer of associations among credit card accounts is proposed. Built on datastructure assumptions that are of business intuition, the proposed models outperform the traditional models based on only academic sense.