Anti-Fraud Solutions Must Fit a Bank's Parameters

Article excerpt

No one doubts that bank card fraud is a serious problem. Visa reports that annual worldwide fraud losses are well over $700 million.

Although many card issuers are implementing numerous fraud prevention measures, the money lost is only part of the problem. Law enforcement officials have reported that card fraud is also being used by organized criminal groups to finance violent crimes.

Certainly, the ultimate goal of fraud detection and prevention is to reduce losses. But you also must make sure that whatever solution you choose will work within your operating parameters. Is the solution cost effective, or are you spending $2 to prevent $1 of fraud? Is the response time- acceptable? Will this solution affect your staffing? Is the solution acceptable to your customers?

These questions and others should be considered when selecting a fraud detection solution. Using the following basic evaluation steps will give card issuers a head start.

First, assess what data and analysis are incorporated in the fraud solution. The solution ought to allow further data collection and analysis by the risk manager. You want an experienced analyst with expertise in fraud prediction who will parse the data before the model is developed, during the building of predictors, and as part of the final delivery.

A good fraud detection model should combine many factors, built from hundreds of characteristics generated from master file and transaction data. These models may be developed using the issuer's own data for optimum predictability or with pooled data. Predictive technology has shown impressive results in fraud detection.

The key consideration is not which technology is used to build the model- that is only one component of a total solution-but how to maximize the information value of the data with which you are working.

For example, SunTrust Bankcard uses a solution that allows it to maximize its data's information value at a lower cost than some other fraud systems based on different technologies. Joe Vautrin, SunTrust vice president of fraud detection, detailed his experience in placing emphasis on data analysis over technology.

The vendor "demonstrated the ability to comprehensively research the history of our accounts," Mr. Vautrin said, and "added velocity tools to the solution's fraud detection, which allowed analysis and targeting of data by merchant category codes."

Mr. Vautrin added: "We're a regional issuer, and we found that fraud occurrences by merchant type differ from fraud at a national level. Earlier detection reduces the cost of fraud, improves customer service and satisfaction, and lifts the staff's confidence and morale."

Since fraud is dynamic, card issuers should look for a solution that combines robust scoring models with flexible, easily maintained strategies to identify short-term pattern shifts and long-term fraud patterns.

Many issuers ask whether continuous strategy development is really necessary, since models can periodically be redeveloped. …