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In a recent article, www.CNNMoney.com reported that Federal Reserve Chairman, Ben Bernanke, said that the pace of recovery in 2010 would be moderate and added that the unemployment rate would come down quite slowly, due to headwinds on ongoing credit problems and the effort by families to reduce household debt.’ While some media outlets promote an optimistic economic viewpoint, clearly there are signs that significant challenges lie ahead for lenders. As Bernanke forecasts, many issues that have plagued credit markets will sustain themselves in the coming years. Therefore lenders need to be equipped to monitor these continued credit problems if they wish to survive this protracted time of distress. While banks and financial institutions are implementing increasingly sophisticated and thorough processes to monitor fluctuations in credit trends, they have little intelligence to compare their credit performance to that of their peers. Lenders frequently cite that they are concerned about their lack of awareness or intelligence regarding the credit performance and status of their peers. Marketing intelligence solutions are important for management of risk, loan portfolio monitoring and related decisioning strategies. Currently, many vendors offer data on industry-wide trends, but few vendors provide the information needed to allow a lender to understand its position relative to a well-defined group of firms that it considers its peers. As a result, too many lenders are performing benchmarking using data sources that are biased, incomplete, inaccurate, or that lack the detail necessary to derive meaningful conclusions. If you were going to measure yourself personally against a group to understand your comparative performance, why would you perform that comparison against people who had little or nothing in common with you? Does an elite runner measure himself against a weekend warrior to gauge his performance? No; he segments the runners by gender, age, and performance class to understand exactly how he stacks up. Today’s lending environment is not forgiving enough for lenders to make broad industry comparisons if they want to ensure long-term success. Lenders cannot presume they are leading the pack, when, in fact, the race is closer than ever.

The term “risk-based authentication” means many things to many institutions. Some use the term to review to their processes; others, to their various service providers. I’d like to establish the working definition of risk-based authentication for this discussion calling it: “Holistic assessment of a consumer and transaction with the end goal of applying the right authentication and decisioning treatment at the right time.” Now, that “holistic assessment” thing is certainly where the rubber meets the road, right? One can arguably approach risk-based authentication from two directions. First, a risk assessment can be based upon the type of products or services potentially being accessed and/or utilized (example: line of credit) by a customer. Second, a risk assessment can be based upon the authentication profile of the customer (example: ability to verify identifying information). I would argue that both approaches have merit, and that a best practice is to merge both into a process that looks at each customer and transaction as unique and therefore worthy of distinctively defined treatment. In this posting, and in speaking as a provider of consumer and commercial authentication products and services, I want to first define four key elements of a well-balanced risk based authentication tool: data, detailed and granular results, analytics, and decisioning. 1. Data: Broad-reaching and accurately reported data assets that span multiple sources providing far reaching and comprehensive opportunities to positively verify consumer identities and identity elements. 2. Detailed and granular results: Authentication summary and detailed-level outcomes that portray the amount of verification achieved across identity elements (such as name, address, Social Security number, date of birth, and phone) deliver a breadth of information and allow positive reconciliation of high-risk fraud and/or compliance conditions. Specific results can be used in manual or automated decisioning policies as well as scoring models, 3. Analytics: Scoring models designed to consistently reflect overall confidence in consumer authentication as well as fraud-risk associated with identity theft, synthetic identities, and first party fraud. This allows institutions to establish consistent and objective score-driven policies to authenticate consumers and reconcile high-risk conditions. Use of scores also reduces false positive ratios associated with single or grouped binary rules. Additionally, scores provide internal and external examiners with a measurable tool for incorporation into both written and operational fraud and compliance programs, 4. Decisioning: Flexibly defined data and operationally-driven decisioning strategies that can be applied to the gathering, authentication, and level of acceptance or denial of consumer identity information. This affords institutions an opportunity to employ consistent policies for detecting high-risk conditions, reconcile those terms that can be changed, and ultimately determine the response to consumer authentication results – whether it be acceptance, denial of business or somewhere in between (e.g., further authentication treatments). In my next posting, I’ll talk more specifically about the value propositions of risk-based authentication, and identify some best practices to keep in mind.

By: Kari Michel In August, consumer bankruptcy filings were up by 24 percent over the past year and are expected to increase to 1.4 million this year. “Consumers continue to turn to bankruptcy as a shield from the sustained financial pressures of today’s economy,” said American Bankruptcy Institute’s Executive Director Samuel J. Gerdano. What are lenders doing to protect themselves from bankruptcy losses? In my last blog, I talked about the differences and advantage of using both risk and bankruptcy scores. Many lenders are mitigating and managing bankruptcy losses by including bankruptcy scores into their standard account management programs. Here are some ways lenders are using bankruptcy scores: • Incorporating them into existing internal segmentation schemes for enhanced separation and treatment assessment of high risk accounts; • Developing improved strategies to act on high-bankruptcy-risk accounts • In order to manage at-risk consumers proactively and • Assessing low-risk customers for up-sell opportunities. Implementation of a bankruptcy score is recommended given the economic conditions and expected rise in consumer bankruptcy. When conducting model validations/assessments, we recommend that you use the model that best rank orders bankruptcy or pushes more bankruptcies into the lowest scoring ranges. In validating our Experian/Visa BankruptcyPredict score, results showed BankruptcyPredict was able to identify 18 to 30 percent more bankruptcy compared to other bankruptcy models. It also identified 12 to 33 percent more bankruptcy compared to risk scores in the lowest five percent of the score range. This supports the need to have distinct bankruptcy scores in addition to risk scores.


