Credit & Risk

By: Kristan Frend As my colleague Margarita Lim discussed in her December 3rd article, the SSA announced that it will change how social security numbers (SSNs) will be issued, with a move toward a random method of assigning SSNs. For organizations that currently incorporate the validation of an applicant’s SSN issue date and state as a part of their risk-based decisioning, they will lose this piece of applicant authentication post-randomization. But there is some good news - first, this validation piece won’t be entirely terminated on day one of the SSN randomization for organizations. All the change means is that the newly issued SSNs will be randomized. In other words, the only SSNs that the issue data and state won’t be validated on day one are the SSNs that have just been issued to the recently born or immigrants. Given that its likely newborns won’t be applying for credit for another 18 years, the bulk of the newly issued SSNs that organizations will see for a while are those belonging to adults who were recently issued a SSN…A growing number of applicants, but not the majority of applicants. The other bit of good news is this may actually be a good thing for all of us in the long run.   While we’ll end up losing the ability to validate an applicant’s SSN issue data and state, the criminals will be at an even greater disadvantage. Consider this- Last year researchers* were able to “identify all nine digits for 8.5 percent of people born after 1988 in fewer than 1,000 attempts. For people born recently in smaller states, researchers sometimes needed just 10 or fewer attempts to predict all nine digits.” I don’t expect this change to drastically reduce third party fraud rates but over time it should eliminate one component of identity theft and ultimately benefit an organization’s Customer Information Program. *The National Science Foundation, the U.S. Army Research Office, Carnegie Melon Cylab, and the Berkman Faculty Development Fund provided support for the research.  To view the entire study, please visit http://www.pnas.org/content/106/27/10975.full.pdf+html.

A recent article in the USA Today titled, “Jobs rebound will be slow”*, outlines state-by-state forecasts for the United States, as released by Moody's Economy.com. Although the national forecasted increase, 0.9%, reflects the expectation that unemployment will remain an issue throughout 2011, the state-level detail possesses interesting variances that should be further considered by lenders in determining their marketing and acquisition strategies. What I find intriguing, is that Moody’s forecasts job growth for several states that since the beginning of the housing decline have been the hot-spots for mortgage default and high delinquency rates. Moody’s projects job growth for Florida (+2.5%), Nevada (+1.5%), and California (+0.5%) – the so called “sand states” – with comparable growth rates to states like Texas (+2.5%) and North Carolina (+1.3%), which have not experienced the same notoriety for increased risk levels and delinquency. Should this growth transpire, then these states that have been the center of credit risk in recent years will soon become centers of opportunity for lenders, as increased employment should result in decreasing delinquency rates, improved repayment habits, and a generally more creditworthy consumer population. This shift is important, since any economic recovery will start with jobs growth, leading to increased lending, which will drive housing and a broader economic growth. As I noted above, the Moody’s forecast implies that lenders who are looking to drive growth may find that profitable portfolio segments exist in some of what appear to be the unlikeliest places. __________________ *http://www.usatoday.com/money/economy/2009-02-06-new-jobs-growth-graphic_N.htm

The U.S. Senate passed legislation recently that would exempt certain businesses from complying with the Red Flags Rule.  Sponsored by Senator John Thune (R-SD), the bill (S. 3987) creates an exception to the Red Flags Rule for businesses that do not advance funds to a customer. The bill would, for example, redefine the term “creditor” as currently described under the Red Flags Rule guidelines, to apply only to those businesses who advance funds to, or on behalf of, a customer, and based upon an obligation to repay those advanced funds.  The legislation also still provides the Federal Trade Commission with authority to require certain organizations to comply with the Red Flags Rule. The legislation now moves to the U.S. House of Representatives, where the chamber must approve the bill before the end of the year in order for the bill to become law.  This may alleviate many businesses in industries such as law practices, healthcare providers (particularly solo practitioners), and perhaps some service providers in telecommunications and utilities.  However, it is likely that many businesses in the utilities space will still fall under Red Flags Rule enforcement given their accessing of consumer credit profiles in many of their application processing procedures.  Again, one has to wonder what the original intent of the Red Flags Rule was.  If it was to protect consumers from identity theft and other fraud schemes via a robust identity theft prevention program, then vastly narrowing the businesses under which potential enforcement applies seems counter-productive.  The advancement of funds or not doesn’t necessarily add to or reduce risk of fraud, as much as the actual obtainment of accounts and services with identity information…regardless of industry.  More to follow…

As the December 31st deadline approaches for FTC enforcement of the Red Flags Rule, we still seem quite a ways off from getting out from under the cloud of confusion and debate related to the definition of ‘creditor’ under the statutory provisions. For example, the Thune-Begich amendment to “amend the Fair Credit Reporting Act with respect to the applicability of identity theft guidelines to creditors” looks to greatly narrow the definition of creditor under the Rule, and therefore narrow the universe of businesses and institutions covered by the Red Flags Rule. The question remains, and will remain far past the December 31 enforcement deadline, as to how narrow the ‘creditor’ universe gets. Will this amendment be effective in excluding those types of entities generally not in the business of extending credit (such as physicians, lawyers, and other service providers) even if they do provide service in advance of payment collection or billing? Will this amendment exclude more broadly, for example ‘buy-here, pay-here’ auto dealers who don’t extend credit or furnish data to a credit reporting agency? Finally, is this the tip of an iceberg in which more entities opt out of the requirement for robust and effective identity theft prevention programs? So one has to ask if the original Red Flags Rule intent to “require many businesses and organizations to implement a written Identity Theft Prevention Program designed to detect the warning signs – or “red flags” – of identity theft in their day-to-day operations, take steps to prevent the crime, and mitigate the damage it inflicts” still holds true? Or is the idea of protecting consumer identities only a good one when it is convenient? It doesn’t appear to be linked with fraud risk as healthcare fraud, for example, is of major concern to most practitioners and service providers in that particular industry. Lastly, from an efficiency perspective, this debate would likely have been better timed at the drafting of the Red Flags Rule, and prior to the implementation of Red Flags programs across industries that may be ultimately excluded.

By: Staci Baker As we approach the end of the year, and the beginning of holiday spending, consumers are looking at their budgets to determine what level of spending they can do this holiday season, or if they will need additional credit for those much wanted gifts. With that in mind, it is a great time for lenders to evaluate their portfolios to determine which consumers are the best credit risks. According to the National Retail Federation, consumer spending will be up 2.1% for the 2010 holiday season. Although still at pre-recession levels, consumer confidence is starting to re-bound.  But, with an increase in consumer confidence, how will lenders meet the demand for credit, and determine the credit worthiness of potential applicants? Since the beginning of the recession there has been a demand for tools that will assist lenders in managing credit risk. One such tool is the tri-bureau VantageScore, a scoring model that is highly accurate, offers greater predictiveness, and is able to score more people. Scoring models allow lenders to predict the likelihood a consumer will default on a loan. Determining who is a qualified candidate through scoring models is only part of the equation. Each lender needs to determine what level of risk to take, and what is the cost of the credit per applicant. By assessing credit risk, having a good plan in place and knowing who the target customer is, lenders will be more prepared for the holiday season. ___________________ National Retail Federation, http://www.nrf.com/modules.php?name=News&op=viewlive&sp_id=1016

By: Wendy Greenawalt Large financial institutions have acknowledged for some time that taking a more consumer-centric versus product-centric approach can be a successful strategy for an organization. However, implementing such a strategy can be difficult, because inherently organizations want to promote a specific product for one reason or another. With the current economic unrest, organizations are looking for ways to improve customer loyalty with their most profitable and lowest risk customers. They are also looking for ways to improve offers to consumers to provide segment of one decisioning, while satisfying organizational goals. Customer management, and specifically cross-sell or up-sell strategies, are a great example of where organizations can implement what I call “segment of one decisioning”.  In essence, this refers to identifying the best possible decision or outcome for a specific consumer when given multiple offers, scenarios and objectives. Marketers strive to identify the best strategies to maximize decision-making, while minimizing costs. For many, this takes the form of models and complex strategy trees or spreadsheets to identify the ideal offering for a segment of consumers. While this approach is effective, algorithm-based decisioning processes exist that can help organizations identify the optimal decisioning strategies, while considering all possible options at a consumers level. By leveraging an optimization tool, organizations can expand the decision process by considering all variables and all alternatives to find the most cost effective, most-likely-to-be-successful strategies. By optimizing decisions, marketers can determine the ideal offer, while quantifying the ROI and adhering to budgetary or other campaign constraints. Many organizations are once again focusing on account growth and building strategies to implement in the near future. With the limited pool of qualified candidates and increased competition, it is more important than ever that each consumer offer be the best to increase response rates, achieve portfolio growth goals and build a profitable portfolio.

By: Kari Michel How are your generic or custom models performing? As a result of the volatile economy, consumer behavior has changed significantly over the last several years and may have impacted the predictiveness of your models. Credit models need to monitored regularly and updated periodically in order to remain predictive. Let’s take a look at VantageScore, it was recently redeveloped using consumer behavioral data reflecting the volatile economic environment of the last few years. The development sample was compiled using two performance timeframes: 2006 – 2008, and 2007 – 2009, with each contributing 50% of the development sample. This is a unique approach and is unlike traditional score development methodology, which typically uses a single, two year time window. Developing models with data over an extended window reduces algorithm sensitivity to highly volatile behavior in a single timeframe. Additionally, the model is more stable as the development is built on a broader range of consumer behaviors. The validation results show VantageScore 2.0 outperforms VantageScore 1.0 by 3% for new accounts and 2% for existing accounts overall. To illustrate the differences that were seen in consumer behavior, the following chart and table show the consumer characteristics that contribute to a consumer’s score and compare the characteristic contributions of VantageScore 2.0 vs VantageScore 1.0. Payment History Utilization Balances Length of Credit Recent Credit Available Credit Vantage Score 2.0 28% 23% 9% 8% 30% 1% Vantage Score 1.0 32% 23% 15% 13% 10% 7% As we expect ‘payment history’ is a large portion driving the score, 28% for VantageScore 2.0 and 32% for VantageScore 1.0. What is interesting to see is the ‘recent credit’ contribution has increased significantly to 30% from 10%. There also is a shift with lower emphases on balances, 9% versus 15% as well as ‘length of credit’, 8% versus 13%. As you can see, consumer behavior changes over time and it is imperative to monitor and validate your scorecards in order to assess if they are producing the results you expect. If they are not, you may need to redevelop or switch to a newer version of a generic model.

By: Staci Baker As the economy has been hit by the hardest recession since the Great Depression, many people wonder how and when it will recover.  And, once we start to see recovery, will consumer credit return to what it once was? In a recent Experian-Oliver Wyman Market Intelligence Report quarterly webinar, 70% of the respondents in a survey said they believe consumer debt will return to pre-2008 levels.  Clearly, many believe that consumer spending and borrowing will return, despite the fact that consumer credit card borrowing recently declined for the 24th straight month*. Assuming that this optimism is valid, what can credit card lenders do to evaluate the risk levels of potential customers as they attempt to grow their portfolios? For lenders, determining who needs credit, as well as whom to lend to in this economic environment, can be quite challenging.  However, there are many tools available to assist lenders in assessing credit risk and growing their portfolio. Many lenders look at a consumer’s credit score, such as the tri-bureau VantageScore, to evaluate their credit worthiness. By utilizing an individual’s VantageScore, a lender is able to determine potential customer risk levels. Another way to evaluate a consumer’s credit worthiness is to evaluate a population using credit attributes.  Based on the attributes a lender is looking for in their portfolio, they can see improvement in evaluating risk prediction in their portfolio using pre-determined attributes, especially those specifically designed for the credit card industry. There are also models that can help lenders predict when a consumer is likely to be in the market for a new loan or account. Experian’s In the Market Models provide lenders with product-specific segmentation tools that can be combined with risk scores to enhance the efficiency and effectiveness of their offers. To identify the optimal cross-sell and line management decisions based on an individual customer’s risk score and potential value, a lender can also utilize optimization tools.  Optimization, combined with a viable risk management strategy, can assist a lender to achieve a healthy portfolio growth in a highly constrained environment. Although lenders will need to determine the best method to meet their objectives, these are just a few of the many tools available that will assist them in correctly growing their lending portfolios. ____________________ * http://www.usatoday.com/money/economy/2010-10-07-consumer-credit_N.htm

With the issue of delayed bank foreclosures at the top of the evening news, I wanted to provide a different perspective on the issue and highlight what I think are some very important, yet often underestimated risks hidden within this issue. For many homeowners, the process of becoming delinquent and eventually going into default is actually a cash-flow positive experience. The process offers these borrowers temporary “free rent,” whereby a major previous monthly commitment is no longer a monthly obligation, freeing up cash for other purposes, including paying other bills. For those consumers who are managing cash flow issues each month, the lack of a mortgage commitment immediately allows them to meet other commitments more easily - making payments on credit cards and car loans that may have previously also become delinquent. From the perspective of a credit card or auto lender, the extended foreclosure process is a short-term positive – it allows a borrower who had previously struggled to remain current to now pay on time and in the short-run, contributes to portfolio health. Although these lenders will experience an improvement in delinquency rates, the reality is that the credit risk is simply dormant. At some point, the consumer’s mortgage will go into foreclosure, and which point the consumer will again be under pressure to continue meeting their obligations. The hidden and significant risk management issue is the misinterpretation of improved delinquency rates. Halting foreclosures means that an accumulating number of consumers are going to enter into this delayed stage of ‘free rent’, without any immediate prospect of having to make a rent or mortgage payment in the near future. In fact, according to Bank of America, “the average foreclosed borrower has not made a payment in 18 months”. This extended period of foreclosure delay will naturally result in a larger number of consumers being able to meet their non-mortgage obligations – but only while their free-rent status exists. A lender who has an interest in the “free rent” consumer is actually sitting on a time-bomb. When foreclosures stop or slow to a rate that is less than consumers entering it, that group will continue to grow in size - until foreclosures start again – at which point thousands of consumers will be processed and will have to start managing rent/housing payments again. Almost immediately, thousands of consumers who have had no problems meeting their obligations will have to start making decisions about which to pay and which not to pay. So, this buildup of rent-free mortgage holders presents a serious risk management issue to non-mortgage lenders that must be addressed. Lenders who have a relationship with a consumer who is delinquent on their mortgage may be easily fooled into thinking that they are not exposed to the same credit risk as mortgage lenders, but I think that these lenders will quickly find that consumers who have lived rent-free for over a year will have a very difficult time managing this transition, and if not diligent, credit card issuers and automotive lenders may find themselves in trouble. _____________________ http://cnews.canoe.ca/CNEWS/World/2010/10/08/15629836.html

By: Kari Michel Credit bureau data has been used for many years to develop credit risk models, bankruptcy scores,  profitability models, and response models to name a few. For the utility industry (water and power companies), a new score is available to help them administer more efficiently their internal low-income assistance programs. One challenge that utility companies face is to identify those consumers who clearly qualify for low-income assistance in a more automated process in order to reduce the number of applications that require manual intervention. Utility companies are starting to use scoring models to help them determine the likelihood that a customer will qualify for low-income assistance from their local utility. In a recent Experian case study, a medium-sized municipal utility company in California conducted a test using Experian’s Financial Assistance Checker to understand the benefit of using this score in their recertification process. The test showed a reduction of manual review of about 40% of the test file and they expect a 40-50% reduction in manual review in the future. The inclusion of the score in the recertification process will reduce costs and make their low income assistance program more efficient and provide an excellent example of the utility’s efforts to make a positive impact on the community.

In my last entry I mentioned how we’re working with more and more clients that are ramping up their fraud and compliance processes to ensure Red Flag compliance. But it’s not just the FACT Act Identity Theft Program requirements that are garnering all the attention.  As every financial institution is painfully aware, numerous compliance requirements exist around the USA PATRIOT Act and Know Your Customer, Anti-Money Laundering, e-Signature and more. Legislation for banks, lenders, and other financial services organizations are only likely to increase with President Obama’s appointment of Elizabeth Warren to the new Bureau of Consumer Financial Protection. Typically FI’s must perform due diligence across more than one of these requirements, all the while balancing the competing pressures of revenue growth, customer experience, fraud referral rates, and risk management. Here’s a case where we were able to offer a solution to one client’s complex needs.  Recently, we were approached by a bank’s sales channel that needed to automate their Customer Information Program (CIP). The bank’s risk and compliance department had provided guidelines based on their interpretation of due diligence appropriate for CIP and now the Sales group had to find a tool that could facilitate these guidelines and decision appropriately. The challenge was doing so without a costly custom solution, not sacrificing their current customer service SLA’s, and being able to define the criteria in the CIP decisioning rather than a stock interpretation. The solution was to invest in a customer authentication product that offered flexible, adaptable “off the shelf” decisioning along with knowledge based authentication, aka out of wallet questions. The fact that the logic was hosted reduced costly and time consuming software and hardware implementations while at the same time allowing easy modification should their CIP criteria change or pass and review rates need to be tweaked. The net result? Consistent customer treatment and objective application of the CIP guidelines, more cross selling confidence, and the ability to refer only those applicants with fraud alerts or who did not meet the name, address, SSN, and DOB check for further authentication.

By: Wendy Greenawalt US interest rates are at historically low levels, and while many Americans are taking advantage of the low interest rates and refinancing their mortgages, a great deal more are struggling to find jobs, and unable to take advantage of the rate- friendly lending environment. This market however, continues to be complex as lenders try to competitively price products while balancing dynamic consumer risk levels, multiple product options and minimize the cost of acquisition. Due to this, lenders need to implement advanced risk-based pricing strategies that will balance the uncertain risk profiles of consumers while closely monitoring long-term profitability as re-pricing may not be an option given recent regulatory guidelines. Risk-based pricing has been a hot topic recently with the Credit Card Act and Risk-Based Pricing Rule regulation and pending deadline. For lenders who have not performed a new applicant scorecard validation or detailed portfolio analysis in the last few years now is the time to review pricing strategies and portfolio mix. This analysis will aid in maintaining an acceptable risk level as the portfolio evolves with new consumers and risk tiers while ensuring short and long-term profitability and on-going regulatory compliance. At its core, risk-based pricing is a methodology that is used to determine the what interest rate should be charged to a consumer based on the inherent risk and profitability present within a defined pricing tier. By utilizing risk-based pricing, organizations can ensure the overall portfolio is profitable while providing competitive rates to each unique portfolio segment. Consistent review and strategy modification is crucial to success in today’s lending environment. Competition for the lowest risk consumers will continue to increase as qualified candidate pools shrink given the slow economic recovery.  By reviewing your portfolio on a regular basis and monitoring portfolio pricing strategies closely an organization can achieve portfolio growth and revenue objectives while monitoring population stability, portfolio performance and future losses.

By: Staci Baker On September 12, 2010, the new Basel III rules were passed in Basel, Switzerland. These new rules aim to increase the liquidity of banks over the next decade, thereby mitigating the risk of bank failures and mergers that transpired during the recent financial crisis. Currently, banks must maintain capital reserves of 4% on their balance sheet to account for enterprise risk. Starting January 1, 2013, banks will be required to progressively increase their capital reserves, known as tier 1 capital, to 4.5%. By the end of 2019, this reserve will need to be 6%.  Banks will also be required to keep an emergency reserve, or “conservation buffer,” of 2.5%. What does this mean for banks? And, what are some tools that banks can use in assessing credit risk? By increasing capital reserves, banks will be more stable in times of economic hardship. The conservation buffer is meant to help absorb losses during times of economic stress, which means banks will be in a better position to maintain economic progress in the most challenging economic circumstances. The capital reserve designated by the Group of Governors and Heads of Supervision is the minimum requirement each bank will be held to. Each bank will need to assess their current risk levels, and run stress tests to ensure they are in a good financial position, and are able to sustain strong financial health during a failing economy. Stress tests should be run for different time intervals, which will allow lenders to assess future losses and to plan capital satisfactoriness accordingly. This type of credit risk analysis is possible through applications such as Moody’s CreditCycle Plus, powered by Experian, that allow for stress testing, and profit and loss forecasting.  These applications will measure future performance of consumer credit portfolios under various economic scenarios, measured against industry benchmarks. ______________ Bank for International Settlements, 9/12/10, http://bis.org/press/p100912.htm

With the recent release of first-time unemployment applications by the Labor Department showing weaker than expected results, it comes as no surprise that July foreclosure rates  also reflect the on-going stress being experienced by consumers across the nation. When considering credit score trends and delinquency measures across credit products, it’s interesting to see how these trends appear to be playing out in terms of their impact on consumer score migration patterns. Over the past year or so, it appears that the impact of a struggling economy is the creation of a two-tier consumer credit system. On one hand, for consumers with stronger credit risk scores who are able to successfully manage their financial obligations, we see stability in the composition of the prime and super-prime population. On the other hand, as other consumers face challenging times, especially through joblessness and reductions in real-estate equity, there are consumers who experience significant credit management issues and subsequently, their risk scores decline. The interesting phenomenon is that there seems to be fewer and fewer consumers who remain in between these two segments. Credit score migration patterns suggest the evolution of two distinct consumer populations: a relatively stable, lower-risk segment, and a somewhat bottom-heavy higher-risk population, comprised of consumers with long-term repayment challenges, recent foreclosures, repossessions and higher delinquency rates. Clearly, this type of change in score distribution directly impacts lenders and their acquisition and account management strategies. With few signs of a pending economic recovery, it will be interesting to watch this pattern develop in the long-term to see if the chasm between these groups becomes wider and more measurable, or whether other economic influences will further transform the consumer credit landscape.

Recently, a number of media articles have discussed the task facing financial institutions today – find opportunities growth in a challenging and flat economy. The majority of perspectives discuss the fact that lenders will soon have no choice but to look to the ‘fringe’, by lowering score cut-offs, adjusting acquisition strategies and introducing greater risk into their portfolios in order to grow. Risk and marketing departments are sure to be creating and analyzing credit risk models and assessing credit risk in new, untapped markets in order to achieve these objectives. While it may appear to be oversimplifying the task, many lenders have the opportunity to grow simply by understanding more about two groups of consumers that are already sitting in their offices (or application queues) today: applicants who are approved, but book elsewhere, and applicants that are declined. There are a number of analytic techniques that can be utilized to understand these populations further. Lenders can study the characteristics of other loans originated by these lost consumers, and can also perform analyses of how these consumers performed after booking competitive offers. By understanding the credit characteristics and account delinquency trends of its current applicants, lenders can uncover a wealth of information and insight about the growth opportunities sitting right before them.