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Updated November 17th Related Posts Link to automotive form, business form

Discover how token-based authentication works, its types, and why businesses trust it to secure sensitive data.

Discover how data and analytics can enhance deposit growth strategies, improve customer engagement, and drive financial wellness for banks and credit unions.

Consumer credit card debt has dipped to levels not seen since 2006 and the memory of pre-recession spending habits continues to get hazier with each passing day. In May, revolving credit card balances totaled over $790 billion, down $180 billion from mid-2008 peak levels. Debit and Prepaid volume accounted for 44% or nearly half of all plastic spending, growing substantially from 35% in 2005 and 23% a decade ago. Although month-to-month tracking suggests some noise in the trends as illustrated by the slight uptick in credit card debt from April to May, the changes we are seeing are not at all temporary. What we are experiencing is a combination of many factors including the aftermath impacts of recession tightening, changes in the level of comfort for financing non-essential purchases, the “new boomer” population entering the workforce in greater numbers and the diligent efforts to improve the general household wallet composition by Gen Xers. How do card issuers shift existing strategies? Baby boomers are entering that comfortable stage of life where incomes are higher and expenses are beginning to trail off as the last child is put through college and mortgage payments are predominantly applied toward principle. This group worries more about retirement investments and depressed home values and as such, they demand high value for their spending. Rewards based credit continues to resonate well with this group. Thirty years ago, baby boomers watched as their parents used cash, money orders and teller checks to manage finances but today’s population has access to many more options and are highly educated. As such, this group demands value for their business and a constant review of competitive offerings and development of new, relevant rewards products are needed to sustain market share. The younger generation is focused on technology. Debit and prepaid products accessible through mobile apps are more widely accepted for this group unlike ten to fifteen years ago when multiple credit cards with four figure credit limits each were provided to college students in large scale. Today’s new boomer is educated on the risks of using credit, while at the same time, parents are apt to absorb more of their children’s monthly expenses. Servicing this segment’s needs, while helping them to establish a solid credit history, will result in long-term penetration in a growing segment. Recent CARD Act and subsequent amendments have taken a bite out of revenue previously used to offset increased risk and related costs that allowed card issuers to service the near-prime sector. However, we are seeing a trend of new lenders getting in to the credit card game while existing issuers start to slowly evaluate the next tier. After six quarters of consistent credit card delinquency declines, we are seeing slow signs of relief. The average VantageScore for new card originations increased by 8 points from the end of 2008 into early 2010 driven by credit tightening actions and has started to slowly come back down in recent months.   What next? What all of this means is that card issuers have to be more sophisticated with risk management and marketing practices. The ability to define segments through the use of alternate data sources and access channels is critical to ongoing capture of market share and profitable usage. First, the segmentation will need to identify the “who” and the “what.” Who wants what products, how much credit is a consumer eligible for and what rate, terms and rewards structure will be required to achieve desired profit and risk levels, particularly as the economy continues to teeter between further downturn and, at best, slow growth. By incorporating new modeling and data intelligence techniques, we are helping sophisticated lenders cherry pick the non-super prime prospects and offering guidance on aligning products that best balance risk and reward dynamics for each group. If done right, card issuers will continue to service a diverse universe of segments and generate profitable growth.

As I’m sure you are aware, the Federal Financial Institutions Examination Council (FFIEC) recently released its, “Supplement to Authentication in an Internet Banking Environment” guiding financial institutions to mitigate risk using a variety of processes and technologies as part of a multi-layered approach. In light of this updated mandate, businesses need to move beyond simple challenge and response questions to more complex out-of-wallet authentication.  Additionally, those incorporating device identification should look to more sophisticated technologies well beyond traditional IP address verification alone. Recently, I contribute to an article on how these new guidelines might affect your institution.  Check it out here, in full:  http://ffiec.bankinfosecurity.com/articles.php?art_id=3932 For more on what the FFIEC guidelines mean to you, check out these resources – which also gives you access to a recent Webinar.

What happens when once desirable models begin to show their age? Not the willowy, glamorous types that prowl high-fashion catwalks. But rather the aging scoring models you use to predict risk and rank-order various consumer segments. Keeping a fresh face on these models can return big dividends, in the form of lower risk, accurate scoring and higher quality customers. In this post, we provide an overview of custom attributes and present the benefits of overlaying current scoring models with them. We also suggest specific steps communications companies can take to improve the results of an aging or underperforming model. The beauty of custom attributes Attributes are highly predictive variables derived from raw data. Custom attributes, like those you’ve created in house or obtained from third parties, can provide deeper insights into specific behaviors, characteristics and trends. Overlaying your scoring model with custom attributes can further optimize its performance and improve lift. Often, the older the model, the greater the potential for improvement. Seal it with a KS Identifying and integrating the most predictive attributes can add power to your overlay, including the ability to accurately rank-order consumers. Overlaying also increases the separation of “goods and bads” (referred to as “KS”) for a model within a particular industry or sub-segment. Not surprisingly, the most predictive attributes vary greatly between industries and sub-segments, mainly due to behavioral differences among their populations. Getting started The first step in improving an underperforming model is choosing a data partner—one with proven expertise with multivariate statistical methods and models for the communications industry. Next, you’ll compile an unbiased sample of consumers, a reject inference sample and a list of attributes derived from sources you deem most appropriate. Attributes are usually narrowed to 10 or fewer from the larger list, based on predictiveness Predefined, custom or do-it-yourself Your list could include attributes your company has developed over time, or those obtained from other sources, such as Experian Premier AttributesSM (more than 800 predefined consumer-related choices) or Trend ViewSM attributes. Relationship, income/capacity, loan-to-value and other external data may also be overlaid. Attribute ToolboxTM Should you choose to design and create your own list of custom attributes, Experian’s Attribute ToolboxTM offers a platform for development and deployment of attributes from multiple sources (customer data or third-party data identified by you). Testing a rejuvenated model The revised model is tested on your both your unbiased and reject inference samples to confirm and evaluate any additional lift induced by newly overlaid attributes. After completing your analysis and due diligence, attributes are installed into production. Initial testing, in a live environment, can be performed for three to twelve months, depending on the segment (prescreen, collections, fraud, non-pay, etc), outcome or behavior your model seeks to predict. This measured, deliberate approach is considered more conservative, compared with turning new attributes on right away. Depending on the model’s purpose, improvements can be immediate or more tempered. However, the end result of overlaying attributes is usually better accuracy and performance. Make your model super again If your scoring model is starting to show its age, consider overlaying it with high-quality predefined or custom attributes. Because in communications, risk prevention is always in vogue. To learn more about improving your model, contact your Experian representative. To read other recent posts related to scoring, click here.