Statistical challenges in credit card issuing

The credit card industry is awakening to the fact that the vast amounts of data it has on its customers should be treated as a valuable asset and that extracting knowledge from this data is crucial if one wants to optimize ones credit book. The company I work for—Barclaycard has over 7 million customers and over 9 million credit cards in circulation and deals with over 300 million credit card transactions per annum—a tremen dous amount of data. This paper is intended to provide a flavour of some of the statistical work that is required to take advantage of this asset. The suggestions are only the tip of the iceberg. Credit cards are a complex business. A factor that makes up this complexity is that credit card customers use their cards for a number of non‐credit reasons, namely: payment convenience, smoothing of finances, paying regular bills, emergencies and spontaneous spending. Moreover, managing customers implies balancing revenue, churn and bad debt issues, and the explosion of competetion in recent years is making it more difficult to attract and retain profitable, low‐risk customers. In the paper I will examine the traditional statistical methodology behind the issue of credit, along with some of the problems that have been solved and some that are still outstanding. The paper then provides an overview of the new Customer Value Management paradigm, also known as Customer Relationship Management (CRM), highlighting the new challenges that have emanated from this and the implications for statisticians. Copyright © 2001 John Wiley & Sons, Ltd.