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Marketers are keenly aware of how important it is to “Know thy customer.” Yet customer knowledge isn’t restricted to the marketing-savvy. It’s also essential to credit risk managers and model developers. Identifying and separating customers into distinct groups based on various types of behavior is foundational to building effective custom models. This integral part of custom model development is known as segmentation analysis. Segmentation is the process of dividing customers or prospects into groupings based on similar behaviors such as length of time as a customer or payment patterns like credit card revolvers versus transactors. The more similar or homogeneous the customer grouping, the less variation across the customer segments are included in each segment’s custom model development. So how many scorecards are needed to aptly score and mitigate credit risk? There are several general principles we’ve learned over the course of developing hundreds of models that help determine whether multiple scorecards are warranted and, if so, how many. A robust segmentation analysis contains two components. The first is the generation of potential segments, and the second is the evaluation of such segments. Here I’ll discuss the generation of potential segments within a segmentation scheme. A second blog post will continue with a discussion on evaluation of such segments. When generating a customer segmentation scheme, several approaches are worth considering: heuristic, empirical and combined. A heuristic approach considers business learnings obtained through trial and error or experimental design. Portfolio managers will have insight on how segments of their portfolio behave differently that can and often should be included within a segmentation analysis. An empirical approach is data-driven and involves the use of quantitative techniques to evaluate potential customer segmentation splits. During this approach, statistical analysis is performed to identify forms of behavior across the customer population. Different interactive behavior for different segments of the overall population will correspond to different predictive patterns for these predictor variables, signifying that separate segment scorecards will be beneficial. Finally, a combination of heuristic and empirical approaches considers both the business needs and data-driven results. Once the set of potential customer segments has been identified, the next step in a segmentation analysis is the evaluation of those segments. Stay tuned as we look further into this topic. Learn more about how Experian Decision Analytics can help you with your segmentation or custom model development needs.

On May 11, 2018, financial institutions will be required to perform Customer Due Diligence routines for their legal entity customers, such as a corporation or limited liability company. Here are 3 facts that you should know about this upcoming rule: When validating ownership, financial institutions can accept what customers have provided unless they have a reason to believe otherwise. Some possible trigger events requiring review of beneficial ownership information for existing accounts include: change in ownership and law enforcement warrants or subpoenas. When collecting and updating beneficial ownership information, the financial institution must retain the original and updated information. While financial institutions are required to collect the same basic customer identification program information from business owners that is required from consumer customers, your current policies may not satisfy this new rule. Learn more

In the credit game, the space is deep and diverse. From super prime to prime to subprime consumers, there is much to be learned about how different segments are utilizing credit and navigating the financial services arena. With 78 percent of full-time workers saying they live paycheck-to-paycheck and 71 percent of U.S. workers responding that they live in debt, it is not surprising a sudden life event can plunge a solid credit consumer from prime to subprime within months. Think lost job, divorce or unexpected medical bill. This population is not going away, and they are seeking ways to make ends meet and obtain finances for needs big and small. In many instances, alternative credit data can shed a light on new opportunities for traditional lenders, fintech players and those in the alternative financial space when servicing this specific consumer segment. In a new study, Clarity analyzed the trends and financial behavior of subprime loan users by looking at application and loan data in Clarity’s database, as well as overlaying VantageScore® credit score insights from Experian from 2013 to 2017. Clarity conducted this subprime trends report last year, but this is the first time it factored in VantageScore® credit score data, providing a different lens as to where consumers fall within the credit score tiers. Among the study highlights: Storefront single pay loan customers are becoming more comfortable with applying for online loans, with a growing percentage seeking installment products. For the first time in five years, online single pay lending (payday) saw a reduction in total credit utilization per customer. Online installment, on the other hand, saw an increase. While the number of online installment loans increased by 12 percent and the number of borrowers by only 9 percent, the dollar value grew by 30 percent. Online installment lenders had the greatest percentage increase in average loan amount. California and Texas remain the most significant markets for online lenders, ranking first and second for five years in a row due to population size. There has also been growth in the Midwest. The in-depth report additionally delves into demographics, indicators of financial stability among the subprime market and comparisons between storefront and online product use and performance. “Every year, there are more financial lenders and products emerging to serve this population,” said Andy Sheehan, president of Clarity Services. “It’s important to understand the trends and data associated with these individuals and how they are maneuvering throughout the credit spectrum. As we know, it is often not a linear journey.” The inclusion of the VantageScore® credit score showcased additional findings around prime versus subprime financial behaviors and looks at generational trends. Access Full Report
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