By: Wendy Greenawalt Optimization has become a "buzz word" in the financial services marketplace, but some organizations still fail to realize all the possible business applications for optimization. As credit card lenders scramble to comply with the pending credit card legislation, optimization can be a quick and easily implemented solution that fits into current processes to ensure compliance with the new regulations. Optimizing decisions Specifically, lenders will now be under strict guidelines of when an APR can be changed on an existing account, and the specific circumstances under which the account must return to the original terms. Optimization can easily handle these constraints and identify which accounts should be modified based on historical account information and existing organizational policies. APR account changes can require a great deal of internal resources to implement and monitor for on-going performance. Implementing an optimized strategy tree within an existing account management strategy will allow an organization to easily identify consumer level decisions.  This can be accomplished while monitoring accounts through on-going batch processing. New delivery options are now available for lenders to receive optimized strategies for decisions related to: Account acquisition Customer management Collections Organizations who are not currently utilizing this technology within their  processes should investigate the new delivery options. Recent research suggests optimizing decisions can provide an improvement of 7-to-16 percent over current processes.  

In my last blog, I discussed the basic concept of a maturation curve, as illustrated below: Exhibit 1 In Exhibit 1, we examine different vintages beginning with those loans originated by year during Q2 2002 through Q2 2008. The purpose of the vintage analysis is to identify those vintages that have a steeper slope towards delinquency, which is also known as delinquency maturation curve. The X-axis represents a timeline in months, from month of origination.  Furthermore, the Y-axis represents the 90+ delinquency rate expressed as a percentage of balances in the portfolio.  Those vintage analyses that have a steeper slope have reached a normalized level of delinquency sooner, and could in fact, have a trend line suggesting that they overshoot the expected delinquency rate for the portfolio based upon credit quality standards. So how can you use a maturation curve as a useful portfolio management tool? As a consultant, I spend a lot of time with clients trying to understand issues, such as why their charge-offs are higher than plan (budget).  I also investigate whether the reason for the excess credit costs are related to collections effectiveness, collections strategy, collections efficiency, credit quality or a poorly conceived budget. I recall one such engagement, where different functional teams within the client’s organization were pointing fingers at each other because their budget evaporated. One look at their maturation curves and I had the answers I needed. I noticed that two vintages per year had maturation curves that were pointed due north, with a much steeper curve than all other months of the year. Why would only two months or vintages of originations each year be so different than all other vintage analyses in terms of performance? I went back to my career experiences in banking, where I worked for a large regional bank that ran marketing solicitations several times yearly. Each of these programs was targeted to prospects that, in most instances, were out-of-market, or in other words, outside of the bank’s branch footprint. Bingo! I got it! The client was soliciting new customers out of his market, and was likely getting adverse selection. While he targeted the “right” customers – those with credit scores and credit attributes within an acceptable range, the best of that targeted group was not interested in accepting their offer, because they did not do business with my client, and would prefer to do business with an in-market player. Meanwhile, the lower grade prospects were accepting the offers, because it was a better deal than they could get in-market. The result was adverse selection…and what I was staring at was the "smoking gun" I’d been looking for with these two-a-year vintages (vintage analysis) that reached the moon in terms of delinquency. That’s the value of building a maturation curve analysis – to identify specific vintages that have characteristics that are more adverse than others.  I also use the information to target those adverse populations and track the performance of specific treatment strategies aimed at containing losses on those segments. You might use this to identify which originations vintages of your home equity portfolio are most likely to migrate to higher levels of delinquency; then use credit bureau attributes to identify specific borrowers for an early lifecycle treatment strategy. As that beer commercial says – “brilliant!”  

–by Jeff Bernstein In the current economic environment, many lenders and issuers across the globe are struggling to manage the volume of caseloads coming into collections. The challenge is that as these new collection cases come into collections in early phases of delinquency, the borrower is already in distress, and the opportunity to have a good outcome is diminished. One of the real “hot” items on the list of emerging best practices and innovating changes in collections is the concept of early lifecycle treatment strategy. Essentially, what we are referring to is the treatment of current and non-delinquent borrowers who are exhibiting higher risk characteristics.  There are also those who are at-risk of future default at higher levels than average. The challenge is how to identify these customers for early intervention and triage in the collections strategy process. One often-overlooked tool is the use of maturation curves to identify vintages within a portfolio that is performing worse than average. A maturation curve identifies how long from origination until a vintage or segment of the portfolio reaches a normalized rate of delinquency. Let’s assume that you are launching a new credit product into the marketplace. You begin to book new loans under the program in the current month. Beyond that month, you monitor all new loans that were originated/booked during that initial time frame which we can identify as a “vintage” of the portfolio. Each month’s originations are a separate vintage or vintage analysis, and we can track the performance of each vintage over time. How many months will it take before the “portfolio” of loans booked in that initial month reach a normal level of delinquency based on these criteria: the credit quality of the portfolio and its borrowers, typical collections servicing, delinquency reporting standards, and factor of time?  The answer would certainly depend upon the aforementioned factors, and could be graphed as follows:   Exhibit 1        In Exhibit 1, we examine different vintages beginning with those loans originated during Q2 2002, and by year Q2 2008. The purpose of the analysis is to identify those vintages that have a steeper slope towards delinquency, which is also known as a delinquency maturation curve.  The X-axis represents a timeline in months, from month of origination.  Furthermore,, the Y-axis represents the 90+ delinquency rate expressed as a percentage of balances in the portfolio. Those vintages that have a steeper slope have reached a normalized level of delinquency sooner, and could in fact, have a trend line suggesting that they overshoot the expected delinquency rate for the portfolio based upon credit quality standards. So how do we use the maturation curve as a tool? In my next blog, I will discuss how to use maturation curves to identify trends across various portfolios.  I will also examine differentiate collections issues from originations or lifecycle risk management opportunities.