In case you’ve never heard of it, a Babel fish is a small translator; that allows a carrier to understand anything said in any form of language.  Alta Vista popularized the name but I believe Douglas Adams, author of The Hitchhiker’s Guide to the Galaxy, should be given credit for coining the term.  So, what does a Babel fish have to do with Knowledge Based Authentication? Knowledge Based Authentication is always about the data – I have said this before.  There is one universal truth: data doesn’t lie.  However, that doesn’t mean it is easy to understand what the data is saying.  It is a bit like a foreign language.  You may have taken classes, and you can read the language or carry on a passable conversation, but that doesn’t mean it’s a good idea to enter into a contract – at least, not without an attorney who speaks the language, or your very own Babel fish. Setting up the best Knowledge Based Authentication configuration for risk management of your line of business can sometimes seem like that contract in a foreign language. There are many decisions to be made and the number of questions to present and which questions to ask is often the easy part.  To truly get the most out of fraud models, it is necessary to consider where the score cuts that will be used with your Knowledge Based Authentication session will be set and what methodology will be used to invoke the Knowledge Based Authentication session: objective score performance, manual review and decision, etc.  It is also important to consider the “kind of fraud” you might be seeing. This is where it is helpful to have your very own Babel fish – one designed specifically for fraud trends, fraud data, fraud models and Knowledge Based Authentication.  If your vendor doesn’t offer you a Babel fish, ask for one.  Yours could have one of many titles, but you will know this person when you speak with them, for their level of understanding of not only your business but, more importantly, your data and what it means.  Sometimes the Babel fish will work in Consulting, sometimes in Product Management, sometimes in Analytics – the important thing is that there are fraud-specific experts available to you. Think about that for a minute.  Business today is a delicate balance between customer experience/relationship management and risk management.  If your vendor can’t offer you a Babel fish, tell them you have fish to fry – elsewhere.  

By: Staci Baker With the increase in consumer behaviors such as ‘strategic default’, it has become increasingly difficult during the past few years for lenders to determine who the most creditworthy consumers are – defining consumers with the lowest credit risk. If you define risk as ‘the likelihood of [a consumer] becoming 90 days or more past due’, the findings are alarming. From June 2007 to June 2009, Super Prime consumers (those scoring 900 or higher) in the U.S. have gone from an average  VantageScore® credit score* of 945 to 918, which increased their risk level  from approx. 0.12% to 0.62% - an increase of 417% for this highly sought after population!  Prime and near prime risk levels increased by 400% and 96% respectively.  Whereas subprime consumers with few choices (stay subprime or improve their score), saw a slight decrease in risk, 8% - increasing their average VantageScore® credit score from 578 to 599. So how do lenders determine who to lend to, when the risk level for all credit tiers increases, or remain risky?  In today’s dynamic economy, lenders need tools that will give them an edge, and allow them to identify consumer trends quickly.  Incorporating analytic tools, like Premier Attributes, into lender’s origination models, will allow them to pinpoint specific consumer behavior, and provide segmentation through predefined attribute sets that are industry specific and target profitable accounts to improve acquisition strategies. As risk levels change, maintaining profitability becomes more difficult due to shrinking eligible consumer pools.  By adding credit attributes, assessing credit risk both within an organization and for new accounts will be simplified and allow for more targeted prospects, thus maximizing prospecting strategies across the customer lifecycle and helping to increase profitability. * VantageScore®, LLC, May, 2010, “Finding Creditworthy Consumers in a Changing Economic Climate”  

We've blogged about fraud alerts, fraud analytics, fraud models and fraud best practices. Sometimes, though, we delude ourselves into thinking that fraud prevention strategies we put into place today will be equally effective over time.  Unfortunately, when a rat finds a dead-end in a previously-learned maze, it just keeps hunting for an exit.  Fraudsters are no different.  Ideally we want to seal off all the exits, and teach the rats to go and do something productive with their lives, but sadly that is not always the case.  We also don't want to let too many good consumers get stuck either, so we cannot get too trigger-happy with our fraud best practices. Fraud behavior is dynamic, not static.  Fraudsters learn and adapt to the feedback they receive through trial and error.  That means when you plug a hole in your system today, there will be an increased push to seek out other holes tomorrow.  This underscores the importance of keeping a close eye on your fraudsters' behavior trends. But there must be some theoretical breaking point where the fraudsters simply give up trying--at least with your company.  This behavioral extinction may be idealistic in the general sense, but is nonetheless a worthy goal as related to your business.  One of the best things you can do to prevent fraud is to gain a reputation amongst the fraudsters of, "Don't even try, it's not even worth it."  And even if you don't succeed in getting them to stop trying altogether, it's still satisfying to know you are lowering their ROI while improving yours  

I recently attended a conference where Credit Union managers spoke of the many changes facing their industry in the wake of the real estate crisis and economic decline that has impacted the US economy over the past couple of years.  As these managers weighed in on the issues facing their businesses today, several themes began to emerge – tighter lending standards & risk management practices, increased regulatory scrutiny, and increased competition resulting in tighter margins for their portfolios. Across these issues, another major development was discussed – increased Credit Union mergers and acquisitions. As I considered the challenges facing these lenders, and the increase in M&A activity, it occurred to me that these lenders might have a common bond with an unexpected group –American family farms.  Overall, Credit Unions are facing the challenge of adding significant fixed costs (more sophisticated lending platforms & risk management processes) all the while dealing with increased competition from lenders like large banks and captive automotive lenders.  This challenge is not unlike the challenges faced by the family farm over the past few decades – small volume operators having to absorb significant fixed costs from innovation & increased corporate competition, without the benefit of scale to spread these costs over to maintain healthy lending margins. Without the benefit of scale, the family farm basically disappeared as large commercial operators acquired less-efficient (and less profitable) operators. Are Credit Unions entering into a similar period of competitive disadvantage? It appears that the Credit Union model will have to adjust in the very near future to remain viable. With high infrastructure expectations, many credit unions will have to develop improved decisioning strategies, become more proficient in assessing credit risk –implementing risk-based pricing models, and executing more efficient operational processes in order to sustain themselves when the challenges of regulation and infrastructure favor economies of scale. Otherwise, they are facing an uphill challenge, just as the family farm did (and does); to compete and survive in a market that favors the high-volume lender.

Well, in my last blog, I was half right and half wrong.  I said that individual trade associations and advocacy groups would continue to seek relief from Red Flag Rules ‘coverage’ and resultant FTC enforcement.  That was right.  I also said that I thought the June 1 enforcement date would ‘stick’.  That was wrong. Said FTC Chairman Jon Leibowitz, “Congress needs to fix the unintended consequences of the legislation establishing the Red Flag Rule – and to fix this problem quickly. We appreciate the efforts of Congressmen Barney Frank and John Adler for getting a clarifying measure passed in the House, and hope action in the Senate will be swift.  As an agency we’re charged with enforcing the law, and endless extensions delay enforcement.” I think the key words here are ‘unintended consequences’.  It seems to me that the unintended consequences of the Red Flag Rules reach far beyond just which industries are covered or not covered (healthcare, legal firms, retailers, etc).  Certainly, the fight was always going to be brought on by non-financial institutions that generally may not have had a robust identity authentication practice in place as a general baseline practice.  What continues to be lost on the FTC is the fact that here we are a few years down the road, and I still hear so much confusion from our clients as to what they have to do when a Red Flag compliance condition is detected.  It’s easy to be critical in hindsight, yes, but I must argue that if a bit more collaboration with large institutions and authentication service providers in all markets had occurred, creating a more detailed and unambiguous Rule, we may have seen the original enforcement date (or at least one of the first or second postponement dates) ‘stick’. At the end of the day, the idea of mandating effective and market defined identity theft protection programs makes a lot of sense.  A bit more intelligence gathering on the front end of drafting the Rule may, however, have saved time and energy in the long run.  Here’s hoping that December 31st ‘sticks’…I’m done predicting.  

By: Kristan Frend I recently gave a presentation on small business fraud at the annual National Association of Credit Managers (NACM) Credit Congress.  Following the session, several B2B credit professionals shared recent fraud issues   The attendees confirmed what we’ve been hearing from our customers: fraudsters are shifting from consumer to business/commercial fraud and they’re stepping up their game. One of the schemes mentioned by an attendee included fraudsters obtaining parcel provider’s tracking numbers to reroute shipments meant for their B2B customer.  The perpetrator calls the business’s call center, impersonates the legitimate business customer to place an order, obtains the tracking number, and then calls back with the tracking number to request that the shipment be rerouted. Often the new shipping location is a residential address where an individual has been recruited for a work-at-home employment opportunity.   The individual is instructed to sign for deliveries and then reship merchandise to a freight company within the country or directly to destinations outside the United States.  The fraud is uncovered once the legitimate B2B customer receives an invoice for goods which they never ordered or received. I encourage you to take a look at your business’s policies and procedures on handling change of address shipment  requests.  What tools do you employ to verify the individual making the request? Are you verifying who the new address belongs to?  You may also want to ask your parcel provider about account setting options available for when your employees submit reroute requests.  While a shipping reroute request isn’t always indicative of fraud, I recommend you assess your fraud risk and consider whether your fraud-related business processes need refining. Keep an eye out here for postings on these topics: known fraud, bust out fraud, and how best to minimize fraud loss.        

By: Staci Baker As more people have become underwater on their mortgage, the decision to stay or not stay in their home has evolved to consider a number of influences that impact consumer credit decisions.  Research is revealing that much of an individual’s decision to meet his credit obligations is based on his trust in the economy, moral obligation, and his attitude about delinquency and the effect it will have on his credit score. Recent findings suggest that moral obligation keeps the majority of homeowners from walking away from their homes.  According to the 2009 Fannie Mae National Housing Survey (i) – “Nearly nine in ten Americans (88%), including seven in ten who are delinquent on their own mortgages, do not believe it is acceptable for people to stop making payments on an underwater mortgage, while 8% believe it is acceptable.”  It appears that there is a sense of owning up to one’s responsibilities; having signed a contract and the presumed stigma of walking away from that obligation. Maintaining strong creditworthiness by continuing to make payments on an underwater mortgage is motivation to sustain mortgage payments.  “Approximately 74% of homeowners believe it is very important to maintain good credit and this can be a factor in encouraging them not to walk away (ii).”  Once a homeowner defaults on their mortgage, their credit score can drop 150 to 250 points (iii), and the cost of credit in the future becomes much higher via increased interest rates once credit scores trend down. Although consumers expect to keep investing in the housing market (70% said buying a home continues to be one of the safest investments available (iv)) they will surely continue optimizing decisions that consider both the moral and credit implications of their decisions. i     December, 2009, Fannie Mae National Housing Survey ii  4/30/10, Financial Trust Index at 23% While Strategic Defaults Continue to Rise, The Chicago Booth/Kellogg School Financial Trust Index iii  http://www.creditcards.com/credit-card-news/mortgage-default-credit-scores-1270.php iv  December, 2009, Fannie Mae National Housing Survey    

By: Kari Michel The Federal Reserve’s decision to permit card issuers to use income estimation models to meet the Accountability, Responsibility, and Disclosure (CARD) Act requirements to assess a borrower’s ability to repay a loan makes good sense. But are income estimation models useful for anything other than supporting compliance with this new regulation? Yes; in fact these types of models offer many advantages and uses for the financial industry. They provide a range of benefits including better fraud mitigation, stronger risk management, and responsible provision of credit. Using income estimation models to understand your customers’ complete financial picture is valuable in all phases of the customer lifecycle, including: • Loan Origination – use as a best practice for determining income capacity • Prospecting – target customers within a specific income range • Acquisitions – set line assignments for approved customers • Account Management – assess repayment ability before approving line increases • Collections – optimize valuation and recovery efforts One of the key benefits of income estimation models is they validate consumer income in real time and can be easily integrated into current processes to reduce expensive manual verification procedures and increase your ROI. But not all scoring models are created equal. When considering an income estimation model, it’s important to consider the source of the income data upon which the model was developed. The best models rely on verified income data and cover all income sources, including wages, rent, alimony, and Social Security. To lean more about how income estimation models can help with risk management strategies, please join the following webinar: Ability to pay:  Going beyond the Credit CARD on June 8, 2010. http://www.bulldogsolutions.net/ExperianConsumerInfo/EXC1001/frmRegistration.aspx?bdls=24143    

Well, here we are about two weeks from the Federal Trade Commission’s June 1, 2010 Red Flags Rule enforcement date.  While this date has been a bit of a moving target for the past year or so, I believe this one will stick.  It appears that the new reality is one in which individual trade associations and advocacy groups will, one by one, seek relief from enforcement and related penalties post-June 1.  Here’s why I say that: The American Bar Association has already file suit against the FTC, and in October, 2009, The U.S. District Court for the District of Columbia ruled that the Red Flags Rule is not applicable to attorneys engaged in the practice of law.  While an appeal of this case is still pending, in mid-March, the U.S. District Court for the District of Columbia issued another order declaring that the FTC should postpone enforcement of the Red Flags Rule “with respect to members of the American Institute of Certified Public Accountants” engaged in practice for 90 days after the U.S. Court of Appeals for the District of Columbia renders an opinion in the American Bar Association’s case against the FTC.” Slippery slope here.  Is this what we can expect for the foreseeable future? A rather ambiguous guideline that leaves openings for specific categories of “covered entities” to seek exemption?  The seemingly innocuous element to the definition of “creditor” that includes “businesses or organizations that regularly defer payment for goods or services or provide goods or services and bill customers later” is causing havoc among peripheral industries like healthcare and other professional services. Those of you in banking are locked in for sure, but it ought to be an interesting year as the outliers fight to make sense of it all while they figure out what their identity theft prevention programs should or shouldn’t be.  

By: Kari Michel Credit quality deteriorated across the credit spectrum during the recession that began in December, 2007. As the recession winds down, lenders must start strategically assessing credit risk and target creditworthy consumer segments for lending opportunities, while avoiding those segments where consumer credit quality could continue to slip. Studies and analyses by VantageScore® Solutions, LLC demonstrate that there are more than 60 million creditworthy borrowers in the United States - 7 million of whom cannot be identified using standard scoring models. Leveraging methods using the VantageScore® credit score in conjunction with consumer credit behaviors can effectively identify profitable opportunities and segments that require increased risk mitigation thus optimizing decisions. VantageScore Solutions examined how consumers credit scores changed over a 12 month period.  The study focused on three areas of consumer behavior: Stable:  consumers that stay within the same credit tier for one year Improving:  consumers that move to a higher credit tier in any quarter and remain at a high credit tier for the remainder of the timeframe Deteriorating: consumers that move to a lower credit tier in any quarter and remain at a lower credit tier for the remainder of the timeframe Through a segmentation approach, using the three credit behaviors above and credit quality tiers, emerges a clearer picture into profitable segments for acquisitions and existing account management strategies. Download the white paper, “Finding creditworthy consumers in a changing economic climate”, for more information on finding creditworthy consumers from VantageScore Solutions. Lenders can use a similar segmentation analysis on their own population to identify pockets of opportunity to move beyond recession-based management strategies and intelligently re-enter into the world of originations and maximize portfolio profitability.

By: Wendy Greenawalt The auto industry has been hit hard by this Great Recession. Recently, some good news has emerged from the captive lenders, and the industry is beginning to rebound from the business challenges they have faced in the last few years.  As such, many lenders are looking for ways to improve risk management and strategically grow their portfolio as the US economy begins to recover. Due to the economic decline, the pool of qualified consumers has shrunk, and competition for the best consumers has significantly increased. As a result, approval terms at the consumer level need to be more robust to increase loan origination and booking rates of new consumers. Leveraging optimized decisions is a way lenders can address regional pricing pressure to improve conversion rates within specific geographies. Specifically, lenders can perform a deep analysis of specific competitors such as captives, credit unions and banks to determine if approved loans are being lost to specific competitor segments. Once the analysis is complete, auto lenders can leverage optimization software to create robust pricing, loan amount and term account strategies to effectively compete within specific geographic regions and grow profitable portfolio segments. Optimization software utilizes a mathematical decisioning approach to identify the ideal consumer level decision to maximize organizational goals while considering defined constraints. The consumer level decisions can then be converted into a decision tree that can be deployed into current decisioning strategies to improve profitability and meet key business objectives over time.  

By: Staci Baker With the shift in the economy, it has become increasingly more difficult to gauge -- in advance -- what a consumer is going to do when it comes to buying an automobile.  However, there are tools available that allow auto lenders to gain insight into auto loans/leases that were approved but did not book, and for assessing credit risk of their consumers.  By gaining competitive insight and improving  risk management, an auto lender is able to positively impact loan origination strategies by determining the proper loan or lease term, what the finance offer should be and proactively address each unique market and risk segment. As the economy starts to rebound, the auto industry needs to take a more proactive approach in the way its members acquire business; the days of business-as-usual are gone.  All factors except the length of the loan being the same, if one auto dealer is extending 60-month loans per its norm and the dealer down the road is extending 72-month loans, a consumer may choose the longer loan period to help conserve cash for other items. This is one scenario for which auto dealers could leverage Experian’s Auto Prospect Intelligence(SM).  By performing a thorough analysis of approved loans that booked with other auto lenders, and their corresponding terms, auto lenders will receive a clear picture of who they are losing their loans to.  This information will allow an organization to compare account terms within specific peer group or institution type (captive/banks/credit union) and address discrepancies by creating more robust pricing structures and enhanced loan terms, which will result in strategic portfolio growth.    

Since 2007, when the housing and credit crises started to unfold, we’ve seen unemployment rates continue to rise (9.7% in March 2010 *)  with very few indicators that they will return to levels that indicate a healthy economy any time soon. I’ve also found myself reading about the hardship and challenge that people are facing in today’s economy, and the question of creditworthiness keeps coming into my mind, especially as it relates to employment, or the lack thereof, by a consumer. Specifically, I can’t help but sense that there is a segment of the unemployed that will soon possess a better risk profile than someone who has remained employed throughout this crisis. In times of consistent economic performance, the static state does not create the broad range of unique circumstances that comes when sharp growth or decline occurs. For instance, the occurrence of strategic default is one circumstance where the capacity to pay has not been harmed, but the borrower defaults on the commitment anyway. Strategic defaults are rare in a stable market. In contrast, many unemployed individuals who have encountered unfortunate circumstances and are now out of work may have repayment issues today, but do possess highly desirable character traits (willingness to pay) that enhance their long-term desirability as a borrower. Although the use of credit score trends, credit risk modeling and credit attributes are essential in assessing the risk within these different borrowers, I think new risk models and lending policies will need to adjust to account for the growing number of individuals who might be exceptions to current policies. Will character start to account for more than a steady job? Perhaps. This change in lending policy, may in turn, allow lenders to uncover new and untapped opportunities for growth in segments they wouldn’t traditionally serve. *  Source: US Department of Labor. http://www.bls.gov/bls/unemployment.htm

A common request for information we receive pertains to shifts in credit score trends. While broader changes in consumer migration are well documented – increases in foreclosure and default have negatively impacted consumer scores for a group of consumers – little analysis exists on the more granular changes between the score tiers. For this blog, I conducted a brief analysis on consumers who held at least one mortgage, and viewed the changes in their score tier distributions over the past three years to see if there was more that could be learned from a closer look. I found the findings to be quite interesting. As you can see by the chart below, the shifts within different VantageScore® credit score tiers shows two major phases. Firstly, the changes from 2007 to 2008 reflect the decline in the number of consumers in VantageScore® credit score tiers B, C, and D, and the increase in the number of consumers in VantageScore® credit score tier F. This is consistent with the housing crisis and economic issues at that time. Also notable at this time is the increase in VantageScore® credit score tier A proportions. Loan origination trends show that lenders continued to supply credit to these consumers in this period, and the increase in number of consumers considered ‘super prime’ grew. The second phase occurs between 2008 and 2010, where there is a period of stabilization for many of the middle-tier consumers, but a dramatic decline in the number of previously-growing super-prime consumers. The chart shows the decline in proportion of this high-scoring tier and the resulting growth of the next highest tier, which inherited many of the downward-shifting consumers. I find this analysis intriguing since it tends to highlight the recent patterns within the super-prime and prime consumer and adds some new perspective to the management of risk across the score ranges, not just the problematic subprime population that has garnered so much attention. As for the true causes of this change – is unemployment, or declining housing prices are to blame? Obviously, a deeper study into the changes at the top of the score range is necessary to assess the true credit risk, but what is clear is that changes are not consistent across the score spectrum and further analyses must consider the uniqueness of each consumer.

By: Wendy Greenawalt Optimization has become somewhat of a buzzword lately being used to solve all sorts of problems. This got me thinking about what optimizing decisions really means to me? In pondering the question, I decided to start at the beginning and really think about what optimization really stands for. For me, it is an unbiased mathematical way to determine the most advantageous solution to a problem given all the options and variables. At its simplest form, optimization is a tool, which synthesizes data and can be applied to everyday problems such as determining the best route to take when running errands. Everyone is pressed for time these days and finding a few extra minutes or dollars left in our bank account at the end of the month is appealing. The first step to determine my ideal route was to identify the different route options, including toll-roads, factoring the total miles driven, travel time and cost associated with each option. In addition, I incorporated limitations such as required stops, avoid main street, don’t visit the grocery store before lunch and must be back home as quickly as possible. Optimization is a way to take all of these limitations and objectives and simultaneously compare all possible combinations and outcomes to determine the ideal option to maximize a goal, which in this case was to be home as quickly as possible. While this is by its nature a very simple example, optimizing decisions can be applied to home and business in very imaginative and effective means. Business is catching on and optimization is finding its way into more and more businesses to save time and money, which will provide a competitive advantage. I encourage all of you to think about optimization in a new way and explore the opportunities where it can be applied to provide improvements over business-as-usual as well as to improve your quality of life.