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- Test
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By: Heather Grover In my previous blog, I covered top of mind issues that our clients are challenged with related to their risk based authentication efforts and fraud account management. My goal in this blog is to share many of the specific fraud trends we have seen in recent months, as well as those that you – our clients and the industry as a whole – are experiencing. Management of risk and strategies to minimize fraud is on your mind. 1. Migration of fraud from Internet to call centers – and back again. Channel specific fraud is nothing new. Criminals prefer non-face-to-face channels because they can preserve anonymity, while increasing their number of attempts. The Internet has been long considered a risky channel, because many organizations have built defenses around transaction velocity checks, IP address matching and other tools. Once fraudsters were unable to pass through this channel, the call center became the new target, and path of least resistance. Not surprisingly, once the industry began to address the call center, fraud began to migrate, yet again. Increasingly we hear that the interception and compromise of online credentials due to keystroke loggers and other malware is on the rise. 2. Small business fraud on the rise. As the industry has built defenses in their consumer business, fraudsters have again migrated — this time to commercial products. Historically, small business has not been a target for fraud, which is changing. We see and hear that, while similar to consumer fraud in many ways, small business fraud is often more difficult to detect many times due to “shell businesses” that are established. 3. Synthetic ID becoming less of an issue. As lenders tighten their criteria, not only are they turning down those less likely to pay, but their higher standards are likely affecting Synthetic ID fraud, which many times creates identities with similar characteristics that mirror “thin file” consumers. 4. Family fraud continues. We have seen consumers using the identities of members of their family in an attempt to gain and draw down credit. These occurrences are nothing new, but sadly this continues in the current economic environment. Desperate parents use their children’s identities to apply for new credit, or other family may use an elderly person’s dormant accounts with a goal of finding a short term lifeline in a bad credit situation. 5. Fraud increasing from specific geographic regions. Some areas are notorious for perpetrating fraud – not too long ago it was Nigeria and Russia. We have seen and are hearing that the new hot spots are Vietnam and other Eastern Europe countries that neighbor Russia. 6. Falsely claiming fraud. There has been an increase of consumers who claim fraud to avoid an account going into delinquency. Given the poor state of many consumers credit status, this pattern is not unexpected. The challenge many clients face is the limited ability to detect this occurrence. As a result, many clients are seeing an increase in fraud rates. This misclassification is masking what should be bad debt.

— by Heather Grover I’m often asked in various industry forums to give talks about, or opinions on, the latest fraud trends and fraud best practices. Let’s face it – fraudsters are students of their craft and continue to study the latest defenses and adapt to controls that may be in place. You may be surprised, then, to learn that our clients’ top-of-mind issues are not only how to fight the latest fraud trends, but how they can do so while maximizing use of automation, managing operational costs, and preserving customer experience — all while meeting compliance requirements. Many times, clients view these goals as being unique goals that do not affect one another. Not only can these be accomplished simultaneously, but, in my opinion, they can be considered causal. Let me explain. By looking at fraud detection as its own goal, automation is not considered as a potential way to improve this metric. By applying analytics, or basic fraud risk scores, clients can easily incorporate many different potential risk factors into a single calculation without combing through various data elements and reports. This calculation or score can predict multiple fraud types and risks with less effort, than could a human manually, and subjectively reviewing specific results. Through an analytic score, good customers can be positively verified in an automated fashion; while only those with the most risky attributes can be routed for manual review. This allows expensive human resources and expertise to be used for only the most risky consumers. Compliance requirements can also mandate specific procedures, resulting in arduous manual review processes. Many requirements (Patriot Act, Red Flag, eSignature) mandate verification of identity through match results. Automated decisioning based on these results (or analytic score) can automate this process – in turn, reducing operational expense. While the above may seem to be an oversimplification or simple approach, I encourage you to consider how well you are addressing financial risk management. How are you managing automation, operational costs, and compliance – while addressing fraud?

By: Kari Michel Bankruptcies continue to rise and are expected to exceed 1.4 million by the end of this year, according to American Bankruptcy Institute Executive Director, Samuel J. Gerdano. Although, the overall bankruptcy rates for a lender’s portfolio is small (about 1 percent), bankruptcies result in high dollar losses for lenders. Bankruptcy losses as a percentage of total dollar losses are estimated to range from 45 percent for bankcard portfolios to 82 percent for credit unions. Additionally, collection activity is restricted because of legislation around bankruptcy. As a result, many lenders are using a bankruptcy score in conjunction with their new applicant risk score to make better acquisition decisions. This concept is a dual score strategy. It is key in management of risk, to minimize fraud, and in managing the cost of credit. Traditional risk scores are designed to predict risk (typically predicting 90 days past due or greater). Although bankruptcies are included within this category, the actual count is relatively small. For this reason the ability to distinguish characteristics typical of a “bankruptcy” are more difficult. In addition, often times a consumer who filed bankruptcy was in “good standings” and not necessarily reflective of a typical risky consumer. By separating out bankrupt consumers, you can more accurately identify characteristics specific to bankruptcy. As mentioned previously, this is important because they account for a significant portion of the losses. Bankruptcy scores provide added value when used with a risk score. A matrix approach is used to evaluate both scores to determine effective cutoff strategies. Evaluating applicants with both a risk score and a bankruptcy score can identify more potentially profitable applicants and more high- risk accounts.


