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Solving the Red Flags Rule problem, Part 2

As I wrote in my previous posting, a key Red Flags Rule challenge facing many institutions is one that manages the number of referrals generated from the detection of Red Flags conditions.  The big ticket item in referral generation is the address mismatch condition.

Identity Theft Prevention Program
I’ve blogged previously on the subject of risk-based authentication and risk-based pricing, so I won’t rehash that information.  What I will suggest, however, is that those institutions who now have an operational Identity Theft Prevention Program (if you don’t, I’d hurry up) should continue to explore the use of alternate data sources, analytics and additional authentication tools (such as knowledge-based authentication) as a way to detect Red Flags conditions and reconcile them all within the same real-time transaction.

Referral rates
Referral rates stemming from address mismatches (a key component of the Red Flags Rule high risk conditions) can approach or even surpass 30 percent.  That is a lot.  The good news is that there are tools which employ additional data sources beyond a credit profile to “find” that positive address match.  The use of alternate data sources can often clear the majority of these initial mismatches, leaving the remaining transactions for treatment with analytics and knowledge-based authentication and Identity Theft Prevention Program.

Whatever “referral management” process you have in place today, I’d suggest exploring risk-based authentication tools that allow you to keep the vast majority of those referrals out of the hands of live agents, and distanced from the need to put your customers through the authentication wringer.  In the current marketplace, there are many services that allow you to avoid high referral costs and risks to customer experience.  Of course, we think ours are pretty good.