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By: Kari Michel Credit risk models are used by almost every lender, and there are many choices to choose from including custom or generic models. With so many choices how do you know what is best for your portfolio? Custom models provide the strongest risk prediction and are developed using an organization’s own data. For many organizations, custom models may not be an option due to the size of the portfolio (may be too small), lack of data including not enough bads, time constraints, and/or lack of resources. If a custom model is not an option for your organization, generic bureau scoring models are a very powerful alternative for predicting risk. But how can you understand if your current scoring model is the best option for you? You may be using a generic model today and you hear about a new generic model, for example the VantageScore® credit score. How do you determine if the new model is more predictive than your current model for your portfolio? The best way to understand if the new model is more predictive is to do a head-to-head comparison – a validation. A validation requires a sample of accounts from your portfolio including performance flags. An archive is pulled from the credit reporting agency and both scores are calculated from the same time period and a performance chart is created to show the comparison. There are two key performance metrics that are used to determine the strength of the model. The KS (Komogorov-Smirnov) is a statistical term that measures the maximum difference between the bad and good cumulative score distribution. The KS range is from 0% to 100%, with the higher the KS the stronger the model. The second measurement uses the bad capture rate in the bottom 5%, 10% or 15% of the score range. A stronger model will provide better risk prediction and allow an organization to make better risk decisions. Overall, when stronger scoring models are used, organizations will be best prepared to decrease their bad rates and have a more profitable portfolio.

With the upcoming changes to overdraft fee policies coming to the banking industry July 1st, courtesy of the Federal Reserve, banks and credit unions are re-examining the revenue growth opportunities through their new account opening process. We frequently hear from our fraud risk and operations client partners that when there is a push for revenue growth, fraud detection gets de-prioritized as a trade off to bringing in more new customers. A DDA-friendly risk based authentication approach may offer some compromise to this seemingly “one for one” exchange. Here are some quick revenue-friendly, risk-averse practices being seen in the branches, call centers, and online channels of Experian clients: • Drive referrals to knowledge based authentication (KBA), negative record checks (account abuse, fraud records) or both off of an upfront fraud score, such as the Precise ID(SM) for Account Opening score. Segmenting based on risk is cost efficient and promotes an improved customer experience. • Bolster the fraud defenses of your online channel by raising the “pass” or “accept” threshold. The lower acquisition costs for this online account opening are tempting but this is also the venue most exploited by fraudsters. Some incremental manual reviews should work out as a small price to pay to catch the higher prevalence of fraud. • Cross sell and up sell with confidence based on more comprehensive authentication. By applying appropriate risk based authentication strategies, more products can be offered and exposure is reduced because you know you are dealing with the true consumer.

I often provide fraud analyses to clients, whereby they identify fraudsters that have somehow gotten through the system. We then go in and see what kinds of conditions exist in the fraudulent population that exist to a much lesser degree in the overall population. We typically do this with indicators, flags, match codes, and other conditions that we have available on the Experian end of things. But that is not to say there aren't things on your side of the fence that could be effective indicators of fraud risk as well! One simple example could be geography. If 50% of your known frauds are coming from a state that only sees 5% of your overall population, then that state sounds like a great indicator of fraud risk! What action you take based on this knowledge is up to you (and, I suppose, government regulation). One option would be to route the risky customers through a more onerous authentication procedure. For example, they might have to come into a branch in person to validate their identity. Geography is certainly not the only potential indicator of fraud risk. Be creative! There might be previously untapped indicators of fraud risk lurking in your customer databases. Do not limit yourself to intuition either. Oftentimes the best indicators of fraud risk that I find are counterintuitive. Just compare the percentage of time a condition occurs in your fraud population to the percentage of time it occurs in the overall population. It might be that you have a fraud ring that is leaving some telltale fingerprint on their behavior–one that is actionable in ways that will jumpstart your fraud prevention practices and minimize fraud losses!
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