To calculate the expected business benefits of making an improvement to your decisioning strategies, you must first identify and prioritize the key metrics you are trying to positively impact.  For example, if one of your key business objectives is improved enterprise risk management, then some of the key metrics you seek to impact, in order to effectively address changes in credit score trends, could include reducing net credit losses through improved credit risk modeling and scorecard monitoring. Assessing credit risk is a key element of enterprise risk management and can addressed as part of your application risk management processes as well as other decisioning strategies that are applied at different points in the customer lifecycle. In working with our clients, Experian has identified 15 key metrics that can be positively impacted through optimizing decisions.  As you review the list of metrics below, you should identify those metrics that are most important to your organization. • Approval rates • Booking or activation rates • Revenue • Customer net present value • 30/60/90-day delinquencies • Average charge-off amount • Average recovery amount • Manual review rates • Annual application volume • Charge-offs (bad debt & fraud) • Avg. cost per dollar collected • Average amount collected • Annual recoveries • Regulatory compliance • Churn or attrition Based on Experian’s extensive experience working with clients around the world to achieve positive business results through optimizing decisions, you can expect between a 10 percent and 15 percent improvement in any of these metrics through the improved use of data, analytics and decision management software. The initial high-level business benefit calculation, therefore, is quite important and straightforward.  As an example, assume your current approval rate for vehicle loans is 65 percent, the average value of an approved application is $200 and your volume is 75,000 applications per year.  Keeping all else equal, a 10 percent improvement in your approval rates (from 65 percent to 72 percent) would generate $10.7 million in incremental business value each year ($200 x 75,000 x .65 x 1.1).  To prioritize your business improvement efforts, you’ll want to calculate expected business benefits across a number of key metrics and then focus on those that will deliver the greatest value to your organization.  

I’ve recently been hearing a lot about how bankcard lenders are reacting to changes in legislation, and recent statistics clearly show that lenders have reduced bankcard acquisitions as they retune acquisition and account management strategies for their bankcard portfolios. At this point, there appears to be a wide-scale reset of how lenders approach the market, and one of the main questions that needs to be answered pertains to market-entry timing: Should a lender be the first to re-enter the market in a significant manner, or is it better to wait, and see how things develop before executing new credit strategies? I will dedicate my next two blogs to defining these approaches and discussing them with regard to the current bankcard market. Based on common academic frameworks, today’s lenders have the option of choosing one of the following two routes: becoming a first-mover, or choosing to take the role of a secondary or late mover. Each of these roles possess certain advantages and also corresponding risks that will dictate their strategic choices: The first-mover advantage is defined as “A sometimes insurmountable advantage gained by the first significant company to move into a new market.” (1)  Although often confused with being the first-to-market, first-mover advantage is more commonly considered for firms that first substantially enter the market. The belief is that the first mover stands to gain competitive advantages through technology, economies of scale and other avenues that result from this entry strategy. In the case of the bankcard market, current trends suggest that segments of subprime and deep-subprime consumers are currently underserved, and thus I would consider the first lender to target these customers with significant resources to have ‘first-mover’ characteristics. The second-mover to a market can also have certain advantages: the second-mover can review and assess the decisions of the first-mover and develops a strategy to take advantage of opportunities not seized by the first-mover. As well, it can learn from the mistakes of the first-mover and respond, without having to incur the cost of experiential learning and possessing superior market intelligence. So, being a first-mover and second-mover can each have its advantages and pitfalls. In my next contribution, I’ll address these issues as they pertain to lenders considering their loan origination strategies for the bankcard market. (1) http://www.marketingterms.com/dictionary/first_mover_advtanage  

Conducting a validation on historical data is a good way to evaluate fraud models; however, fraud best practices dictate that a proper validation uses properly defined fraud tags. Before you can determine if a fraud model or fraud analytics tool would have helped minimize fraud losses, you need to know what you are looking for in this category.  Many organizations have difficulty differentiating credit losses from fraud losses.  Usually, fraud losses end up lumped-in with credit losses. When this happens, the analysis either has too few “known frauds” to create a business case for change, or the analysis includes a large target population of credit losses that result in poor results. By planning carefully, you can avoid this pitfall and ensure that your validation gives you the best chance to improve your business and minimize fraud losses. As a fraud best practice for validations, consider using a target population that errs on the side of including credit losses; however, be sure to include additional variables in your sample that will allow you and your fraud analytics provider to apply various segmentations to the results.  Suggested elements to include in your sample are; delinquency status, first delinquency date, date of last valid payment, date of last bad  payment and indicator of whether the account was reviewed for fraud prior to booking. Starting with a larger population, and giving yourself the flexibility to narrow the target later will help you see the full value of the solutions you evaluate and reduce the likelihood of having to do an analysis over again.