By definition, “Return on Investment” is simple:
(The gain from an investment – The cost of the investment)
The cost of the investment
With such a simple definition, why do companies that develop fraud analytics and their customers have difficulty agreeing to move forward with new fraud models and tools? I believe the answer lies in the definition of the factors that make up the ROI equation:
“The gain from an investment”- When it comes to fraud, most vendors and customers want to focus on minimizing fraud losses. But what happens when fraud losses are not large enough to drive change?
To adopt new technology it’s necessary for the industry to expand its view of the “gain.” One way to expand the “gain” is to identify other types of savings and opportunities that aren’t currently measured as fraud losses. These include:
- Cost of other tools – Data returned by fraud tools can be used to resolve Red Flag compliance discrepancies and help fraud analysts manage high-risk accounts. By making better use of this information, downstream costs can be avoided.
Other types of “bad” organizations are beginning to look at the similarities among fraud and credit losses. Rather than identifying a fraud trend and searching for a tool to address it, some industry leaders are taking a different approach — let the fraud tool identify the high-risk accounts, and then see what types of behavior exist in that population. This approach helps organizations create the business case for constant improvement and also helps them validate the way in which they currently categorize losses.
To increase cross sell opportunities – Focus on the “good” populations. False positives aren’t just filtered out of the fraud review work flow, they are routed into other work flows where relationships can be expanded.