With the recent release of first-time unemployment applications by the Labor Department showing weaker than expected results, it comes as no surprise that July foreclosure rates  also reflect the on-going stress being experienced by consumers across the nation. When considering credit score trends and delinquency measures across credit products, it’s interesting to see how these trends appear to be playing out in terms of their impact on consumer score migration patterns. Over the past year or so, it appears that the impact of a struggling economy is the creation of a two-tier consumer credit system. On one hand, for consumers with stronger credit risk scores who are able to successfully manage their financial obligations, we see stability in the composition of the prime and super-prime population. On the other hand, as other consumers face challenging times, especially through joblessness and reductions in real-estate equity, there are consumers who experience significant credit management issues and subsequently, their risk scores decline. The interesting phenomenon is that there seems to be fewer and fewer consumers who remain in between these two segments. Credit score migration patterns suggest the evolution of two distinct consumer populations: a relatively stable, lower-risk segment, and a somewhat bottom-heavy higher-risk population, comprised of consumers with long-term repayment challenges, recent foreclosures, repossessions and higher delinquency rates. Clearly, this type of change in score distribution directly impacts lenders and their acquisition and account management strategies. With few signs of a pending economic recovery, it will be interesting to watch this pattern develop in the long-term to see if the chasm between these groups becomes wider and more measurable, or whether other economic influences will further transform the consumer credit landscape.

Recently, a number of media articles have discussed the task facing financial institutions today – find opportunities growth in a challenging and flat economy. The majority of perspectives discuss the fact that lenders will soon have no choice but to look to the ‘fringe’, by lowering score cut-offs, adjusting acquisition strategies and introducing greater risk into their portfolios in order to grow. Risk and marketing departments are sure to be creating and analyzing credit risk models and assessing credit risk in new, untapped markets in order to achieve these objectives. While it may appear to be oversimplifying the task, many lenders have the opportunity to grow simply by understanding more about two groups of consumers that are already sitting in their offices (or application queues) today: applicants who are approved, but book elsewhere, and applicants that are declined. There are a number of analytic techniques that can be utilized to understand these populations further. Lenders can study the characteristics of other loans originated by these lost consumers, and can also perform analyses of how these consumers performed after booking competitive offers. By understanding the credit characteristics and account delinquency trends of its current applicants, lenders can uncover a wealth of information and insight about the growth opportunities sitting right before them.

There are a number of people within the industry heralding the death of knowledge based authentication. To those people I would say, “In my humble opinion you are as wrong as those recent tweets proclaiming the death of Bill Cosby.” Before anyone’s head spins around, let me explain. When I talk about knowledge based authentication and out of wallet questions, I mean it in the truest sense, a la dynamic questions presented as a pop quiz and not the secret questions you answered when you set-up an account. Dynamic knowledge based authentication presents questions are generated from information known about the consumer, concerning things the true consumer would know and a fraudster wouldn’t. The key to success, and the key to good questions, is the data, which I have said many, many times before. The truth is every tool will let some fraud through; otherwise, you’re keeping too many good customers away. But if knowledge based authentication truly fails, there are two places to look: Data: There are knowledge based authentication providers who rely solely on public record data for their KBA solutions. In my opinion, that data is a higher data risk segment for compromise. Experian’s knowledge based authentication practice is disciplined and includes a mix of data. Our research has shown us that a question set should, ideally, include questions that are proprietary, non-credit, credit and innovative. Yes, it may make sense to include some public record data in a question set, but should it be the basis for the entire question set? Providers who can rely on their own data, or a strategic combination of data sources, rather than purchasing it from one of the large data aggregators are, in my opinion, at an advantage because fraudsters would need to compromise multiple sources in order to “game the system.” Actual KBA use: Knowledge based authentication works best as part of a risk management strategy where risk based authentication is a component within the framework and not the single, determining factor for passing a consumer. Our research has shown that clients who combine fraud analytics and a score with knowledge based authentication can increase authentication performance from 20% – 30% or more, depending on the portfolio and type of fraud (ID Fraud vs. First Party, etc.)… and adding a score has the obvious benefit of increasing fraud detection, but it also allows organizations to prioritize review rates efficiently while protecting the consumer experience. So before we write the obituary of KBA, let’s challenge those who tinker with out of wallet products, building lists of meaningless questions that a 5th grader could answer. Embrace optimized decisions with risk based authentication and employ fraud best practices in your use of KBA.