Financial Services

By: Heather Grover In past client and industry talks, I’ve discussed the increasing importance of retail branches to the growth strategy of the bank. Branches are the most utilized channel of the bank and they tend to be the primary tool for relationship expansion. Given the face-to-face nature, the branch historically has been viewed to be a relatively low-risk channel needing little (if any) identity verification – there are less uses of robust risk-based authentication or out of wallet questions. However, a now well-established fraud best practice is the process of doing proper identity verification and fraud prevention at the point of DDA account opening. In the current environment of declining credit application volumes and approval across the enterprise, there is an increased focus on organic growth through deposits.  Doing proper vetting during DDA account openings helps bring your retail process closer in line with the rest of your organization’s identity theft prevention program. It also provides assurance and confidence that the customer can now be cross-sold and up-sold to other products. A key industry challenge is that many of the current tools used in DDA are less mature than in other areas of the organization. We see few clients in retail that are using advanced fraud analytics or fraud models to minimize fraud – and even fewer clients are using them to automate manual processes - even though more than 90 percent of DDA accounts are opened manually. A relatively simple way to improve your branch operations is to streamline your existing ID verification and fraud prevention tool set: 1. Are you using separate tools to verify identity and minimize fraud? Many providers offer solutions that can do both, which can help minimize the number of steps required to process a new account; 2. Is the solution realtime? To the extent that you can provide your new account holders with an immediate and final decision, the less time and effort you’ll spend after they leave the branch finalizing the decision; 3. Does the solution provide detail data for manual review? This can help save valuable analyst time and provider costs by limiting the need to do additional searches. In my next post, we’ll discuss how fraud prevention in DDA impacts the customer experience.

The definition of account management authentication is:  Keep your customers happy, but don’t lose sight of fraud risks and effective tools to combat those risks. In my previous posting, I discussed some unique fraud risks facing institutions during the account management phase of their customer lifecycles.  As a follow up, I want to review a couple of effective tools that allow you to efficiently minimize fraud losses during post-application: Knowledge Based Authentication (KBA) — this process involves the use of challenge/response questions beyond "secret" or "traditional" internally derived questions (such as mother's maiden name or last transaction amount). This tool allows for measurably effective use of questions based on more broad-reaching data (credit and noncredit) and consistent delivery of those questions without subjective question creation and grading by call center agents. KBA questions sourced from information not easily accessible by call center agents or fraudsters provide an additional layer of security that is more impenetrable by social engineering. From a process efficiency standpoint, the use of automated KBA also can reduce online sessions for consumers, and call times as agents spend less time self-selecting questions, self-grading responses and subjectively determining next steps. Delivery of KBA questions via consumer-facing online platforms or via interactive voice response (IVR) systems can further reduce operational costs since the entire KBA process can be accommodated without call center agent involvement. Negative file and fraud database – performing checks against known fraudulent and abuse records affords institutions an opportunity to, in batch or real time, check elements such as address, phone, and SSN for prior fraudulent use or victimization.  These checks are a critical element in supplementing traditional consumer authentication processes, particularly in an account management procedure in which consumer and/or account information may have been compromised.  Transaction requests such as address or phone changes to an account are particularly low-hanging fruit as far as running negative file checks are concerned.    

--by Andrew Gulledge Intelligent use of features Question ordering: You want some degree of randomization in the questions that are included for each session. If a fraudster (posing as you) comes through Knowledge Based Authentication, for two or three sessions, wouldn’t you want them to answer new questions each time? At the same time, you want to try to use those questions that perform better more often. One way to achieve both is to group the questions into categories, and use a fixed category ordering (with the better-performing categories being higher up in the batting line up)—then, within each category, the question selection is randomized. This way, you can generally use the better questions more, but at the same time, make it difficult to come through Knowledge Based Authentication twice and get the same questions presented back to you. (You can also force all new questions in subsequent sessions, with a question exclusion strategy, but this can be restrictive and make the “failure to generate questions” rate spike.) Question weighting: Since we know some questions outperform others, both in terms of percentage correct and in terms of fraud separation, it is generally a good idea to weight the questions with points based on these performance metrics. Weighting can help to squeeze out some additional fraud detection from your Knowledge Based Authentication tool.  It also provides considerable flexibility in your decisioning (since it is no longer just “how many questions were answered correctly” but it is “what percentage of points were obtained”). Usage Limits: You should only allow a consumer to come through the Knowledge Based Authentication process a certain number of times before getting an auto-fail decision. This can take the form of x number of uses allowable within y number of hours/days/etc. Time out Limit: You should not allow fraudsters to research the questions in the middle of a Knowledge Based Authentication session. The real consumer should know the answers off the top of their heads. In a web environment, five minutes should be plenty of time to answer three to five questions. A call center environment should allow for more time since some people can be a bit chatty on the phone.  

Account management fraud risks: I “think” I know who I’m dealing with… Risk of fraudulent account activity does not cease once an application has been processed with even the most robust authentication products and tools available.  These are a few market dynamics are contributing to increased fraud risk to existing accounts: -          The credit crunch is impacting bad guys too! Think it’s hard to get approved for a credit account these days? The same tightened lending practices good consumers now face are also keeping fraudsters out of the “application approval” process too. While that may be a good thing in general, it has caused a migratory focus from application fraud to account takeover fraud.  -          Existing and viable accounts are now much more appealing to fraudsters given a shortage of application fraud opportunities, as financial institutions have reduced solicitation volume. A few other interesting challenges face organizations with regards to an institution’s ability to minimize fraud losses related to existing accounts: -  Social engineering — the "human element" is inherent in a call center environment and critical from a customer experience perspective. This factor offers the opportunity for fraudsters to manipulate representatives to either gain unauthorized access to accounts or, at the very least, collect consumer and account information that may help them perpetrate fraud later. - Automatic Number Identification (ANI) spoofing — this technology allows a caller to alter the true displayable number from which he or she is calling to a falsely portrayed number. It's difficult, if not impossible, to find a legitimate use for this technology. However, fraudsters find this capability quite useful as they try to circumvent what was once a very effective method of positively authenticating a consumer based on a "good" or known incoming phone number. With ANI spoofing in play, many call centers are now unable to confidently rely on this once cost-effective and impactful method of authenticating consumers.    

By: Amanda Roth To refine your risk-based pricing another level, it is important to analyze where your tiers are set and determine if they are set appropriately.  (We find many of the regulators / examiners are looking for this next level of analysis.) This analysis begins with the results of the scoring model validation.  Not only will the distributions from that analysis determine if the score can predict between good and delinquent accounts, but it will also highlight which score ranges have similar delinquency rates, allowing you to group your tiers together appropriately.  After all, you do not want to have applicants with a 1 percent chance of delinquency priced the same as someone with an 8 percent chance of delinquency.  By reviewing the interval delinquency rates as well as the odds ratios, you should be able to determine where a significant enough difference occurs to warrant different pricing. You will increase the opportunity for portfolio profitability through this analysis, as you are reducing the likelihood that higher risk applicants are receiving lower pricing.  As expected, the overall risk management of the portfolio will increase when a proper risk-based pricing program is developed. In my next post we will look the final level of validation which does provide insight into pricing for profitability.  

By: Amanda Roth As discussed earlier, the validation of a risk based-pricing program can mean several different things. Let’s break these options down. The first option is to complete a validation of the scoring model being used to set the pricing for your program. This is the most basic validation of the program, and does not guarantee any insight on loan profitability expectations. A validation of this nature will help you to determine if the score being used is actually helping to determine the risk level of an applicant. This analysis is completed by using a snapshot of new booked loans received during a period of time usually 18–24 months prior to the current period. It is extremely important to view only the new booked loans taken during the time period and the score they received at the time of application. By maintaining this specific population only, you will ensure the analysis is truly indicative of the predictive nature of your score at the time you make the decision and apply the recommended risk-base pricing. By analyzing the distribution of good accounts vs. the delinquent accounts, you can determine if the score being used is truly able to separate these groups. Without acceptable separation, it would be difficult to make any decisions based on the score models, especially risk-based pricing. Although beneficial in determining whether you are using the appropriate scoring models for pricing, this analysis does not provide insight into whether your risk-based pricing program is set up correctly or not. Please join me next time to take a look at another option for this analysis.

In a recent presentation conducted by The Tower Group, “2010 Top 10 Business Drivers, Strategic Responses, and IT Initiatives in Bank Cards,” the conversation covered many of the challenges facing the credit card business in 2010.  When discussing the shift from “what it was," to “what it is now” for many issues in the card industry, one specific point caught my attention – the perception of unused credit lines – and the change in approach from lenders encouraging balance load-up to the perception that unused credit lines now represent unknown vulnerability to lenders. Using market intelligence assets at Experian, I thought I would take a closer look at some of the corresponding data credit score profile trends to see what color I could add to this insight. Here is what I found: • Total unused bankcard limits have decreased by $750 billion from Q3 2008 to Q3 2009 • By risk segment, the largest decline in unused limits has been within the VantageScore® credit score A consumer – the super prime consumer – where unused limits have dropped by $420 billion • More than 82 percent of unused limits reside with VantageScore® credit score A and B consumers – the super-prime and prime consumer segments So what does this mean to risk management today? If you subscribe to the approach that unused limits now represent unknown vulnerability, then this exposure does not reside with traditional “risky” consumers, rather it resides with consumers usually considered to be the least risky. So this is good news, right? Well, maybe not. Vintage analysis of recent credit trends shows that vulnerability within the top score tiers might represent more risk than one would suspect. Delinquency trends for VantageScore® credit score A and B consumers within recent vintages (2006 through 2008) show deteriorating rates of delinquency from each year’s vintage to the next. Despite a shift in loan origination volumes towards this group, the performance of recent prime and super-prime originations shows deterioration and underperformance against historical patterns. If The Tower Group’s read on the market is correct, and unused credit now represents vulnerability and not opportunity, it would be wise for lenders to reconsider where and how yesterday’s opportunity has become today’s risk.  

By: Kari Michel   Lenders are looking for ways to improve their collections strategy as they continue to deal with unprecedented consumer debt, significant increases in delinquency, charge-off rates and unemployment and, declining collectability on accounts. Improve collections To maximize recovered dollars while minimizing collections costs and resources, new collections strategies are a must. The standard assembly line “bucket” approach to collection treatment no longer works because lenders can not afford the inefficiencies and costs of working each account equally without any intelligence around likelihood of recovery. Using a segmentation approach helps control spend and reduces labor costs to maximize the dollars collected. Credit based data can be utilized in decision trees to create segments that can be used with or without collection models. For example, below is a portion of a full decision tree that shows the separation in the liquidation rates by applying an attribute to a recovery score This entire segment has an average of 21.91 percent liquidation rate. The attribute applied to this score segment is the aggregated available credit on open bank card trades updated within 12 months. By using just this one attribute for this score band, we can see that the liquidation rates range from 11 to 35 percent. Additional attributes can be applied to grow the tree to isolate additional pockets of customers that are more recoverable, and identify segments that are not likely to be recovered. From a fully-developed segmentation analysis, appropriate collections strategies can be determined to prioritize those accounts that are most likely to pay, creating new efficiencies within existing collection strategies to help improve collections.

By: Roger Ahern It’s been proven in practice many times that by optimizing decisions (through improved decisioning strategies, credit risk modeling, risk-based pricing, enhanced scoring models, etc.) you will realize significant business benefits in key metrics, such as net interest margin, collections efficiency, fraud referral rates and many more.  However, given that a typical company may make more than eight million decisions per year, which decisions should one focus on to deliver the greatest business benefit? In working with our clients, Experian has compiled the following list of relevant types of decisions that can be improved through improvements in decision analytics.  As you review the list below, you should identify those decisions that are relevant to your organization, and then determine which decision types would warrant the greatest opportunity for improvement. • Cross-sell determination • Prospect determination • Prescreen decision • Offer/treatment determination • Fraud determination • Approve/decline decision • Initial credit line/limit/usage amount • Initial pricing determination • Risk-based pricing • NSF pay/no-pay decision • Over-limit/shadow limit authorization • Credit line/limit/usage/ management • Retention decisions • Loan/payment modification • Repricing determination • Predelinquency treatment • Early/late-stage delinquency treatment • Collections agency placement • Collection/recovery treatment  

--by Andrew Gulledge General configuration issues Question selection- In addition to choosing questions that generally have a high percentage correct and fraud separation, consider any questions that would clearly not be a fit to your consumer population. Don’t get too trigger-happy, however, or you’ll have a spike in your “failure to generate questions” rate. Number of questions- Many people use three or four out-of-wallet questions in a Knowledge Based Authentication session, but some use more or less than that, based on their business needs. In general, more questions will provide a stricter authentication session, but might detract from the customer experience. They may also create longer handling times in a call center environment. Furthermore, it is harder to generate a lot of questions for some consumers, including thin-file types. Fewer Knowledge Based Authentication questions can be less invasive for the consumer, but limits the fraud detection value of the KBA process. Multiple choice- One advantage of this answer format is that it relies on recognition memory rather than recall memory, which is easier for the consumer. Another advantage is that it generally prevents complications associated with minor numerical errors, typos, date formatting errors and text scrubbing requirements. A disadvantage of multiple-choice, however, is that it can make educated guessing (and potentially gaming) easier for fraudsters. Fill in the blank- This is a good fit for some KBA questions, but less so with others. A simple numeric answer works well with fill in the blank (some small variance can be allowed where appropriate), but longer text strings can present complications. While undoubtedly difficult for a fraudster to guess, for example, most consumers would not know the full, official and (correct spelling) of the name to which they pay their monthly auto payment. Numeric fill in the blank questions are also good candidates for KBA in an IVR environment, where consumers can use their phone’s keypad to enter the answers.  

A recent New York Times (1) article outlined the latest release of credit borrowing by the Federal Reserve, indicating that American’s borrowed less for the ninth-straight month in October. Nested within the statistics released by the Federal Reserve were metrics around reduced revolving credit demand and comments about how “Americans are borrowing less as they try to replenish depleted investments.” While this may be true, I tend to believe that macro-level statements are not fully explaining the differences between consumer experiences that influence relationship management choices in the current economic environment. To expand on this, I think a closer look at consumers at opposite ends of the credit risk spectrum tells a very interesting story. In fact, recent bank card usage and delinquency data suggests that there are at least a couple of distinct patterns within the overall trend of reducing revolving credit demand: • First, although it is true that overall revolving credit balances are decreasing, this is a macro-level trend that is not consistent with the detail we see at the consumer level. In fact, despite a reduction of open credit card accounts and overall industry balances, at the consumer-level, individual balances are up – that’s to say that although there are fewer cards out there, those that do have them are carrying higher balances. • Secondly, there are significant differences between the most and least-risky consumers when it comes to changes in balances. For instance, consumers who fall into the least-risky VantageScore® tiers, Tier A and B, show only 12 percent and 4 percent year-over-year balance increases in Q3 2009, respectively. Contrast that to the increase in average balance for VantageScore F consumers, who are the most risky, whose average balances increased more than 28 percent for the same time period. So, although the industry-level trend holds true, the challenges facing the “average” consumer in America are not average at all – they are unique and specific to each consumer and continue to illustrate the challenge in assessing consumers' credit card risk in the current credit environment. 1 http://www.nytimes.com/2009/12/08/business/economy/08econ.html  

I have already commented on “secret questions” as the root of all evil when considering tools to reduce identity theft and minimize fraud losses.  No, I’m not quite ready to jump off  that soapbox….not just yet, not when we’re deep into the season of holiday deals, steals and fraud.  The answers to secret questions are easily guessed, easily researched, or easily forgotten.  Is this the kind of security you want standing between your account and a fraudster during the busiest shopping time of the year? There is plenty of research demonstrating that fraud rates spike during the holiday season.  There is also plenty of research to demonstrate that fraudsters perpetrate account takeover by changing the pin, address, or e-mail address of an account – activities that could be considered risky behavior in decisioning strategies.  So, what is the best approach to identity theft red flags and fraud account management?  A risk based authentication approach, of course! Knowledge Based Authentication (KBA) provides strong authentication and can be a part of a multifactor authentication environment without a negative impact on the consumer experience, if the purpose is explained to the consumer.  Let’s say a fraudster is trying to change the pin or e-mail address of an account.  When one of these risky behaviors is initiated, a Knowledge Based Authentication session begins. To help minimize fraud, the action is prevented if the KBA session is failed.  Using this same logic, it is possible to apply a risk based authentication approach to overall account management at many points of the lifecycle: • Account funding • Account information change (pin, e-mail, address, etc.) • Transfers or wires • Requests for line/limit increase • Payments • Unusual account activity • Authentication before engaging with a fraud alert representative Depending on the risk management strategy, additional methods may be combined with KBA; such as IVR or out-of-band authentication, and follow-up contact via e-mail, telephone or postal mail.  Of course, all of this ties in with what we would consider to be a comprehensive Red Flag Rules program. Risk based authentication, as part of a fraud account management strategy, is one of the best ways we know to ensure that customers aren’t left singing, “On the first day of Christmas, the fraudster stole from me…”  

--by Andrew Gulledge Where does Knowledge Based Authentication fit into my decisioning strategy? Knowledge Based Authentication can fit into various parts of your authentication process. Some folks choose to put every consumer through KBA, while others only send their riskier transactions through the out-of-wallet questions. Some people use Knowledge Based Authentication to feed a manual review process, while others use a KBA failure as a hard-decline. Uses for KBA are as sundry and varied as the questions themselves. Decision Matrix- As discussed by prior bloggers, a well-engineered fraud score can provide considerable lift to any fraud risk strategy. When possible, it is a good idea to combine both score and questions into the decisioning process. This can be done with a matrixed approach—where you are more lenient on the questions if the applicant has a good fraud score, and more lenient on the score if the applicant did well on the questions. In a decision matrix, a set decision code is placed within various cells, based on fraud risk. Decision Overrides- These provide a nice complement to your standard fraud decisioning strategy. Different fraud solution vendors provide different indicators or flags with which decisioning rules can be created. For example, you might decide to fail a consumer who provides a social security number that is recorded as deceased. These rules can help to provide additional lift to the standard decisioning strategy, whether it is in addition to Knowledge Based Authentication questions alone, questions and score, etc. The overrides can be along the lines of both auto-pass and auto-fail.  

For the past couple years, the deterioration of the real estate market and the economy as a whole has been widely reported as a national and international crisis. There are several significant events that have contributed to this situation, such as, 401k plans have fallen, homeowners have simply abandoned their now under-valued properties, and the federal government has raced to save the banking and automotive sectors. While the perspective of most is that this is a national decline, this is clearly a situation where the real story is in the details. A closer look reveals that while there are places that have experienced serious real estate and employment issues (California, Florida, Michigan, etc.), there are also areas (Texas) that did not experience the same deterioration in the same manner. Flash forward to November, 2009 – with signs of recovery seemingly beginning to appear on the horizon – there appears to be a great deal of variability between areas that seem poised for recovery and those that are continuing down the slope of decline. Interestingly though, this time the list of usual suspects is changing. In a recent article posted to CNN.com, Julianne Pepitone observes that many cities that were tops in foreclosure a year ago have since shown stabilization, while at the same time, other cities have regressed. A related article outlines a growing list of cities that, not long ago, considered themselves immune from the problems being experienced in other parts of the country. Previous economic success stories are now being identified as economic laggards and experiencing the same pains, but only a year or two later. So – is there a lesson to be taken from this? From a business intelligence perspective, the lesson is generalized reporting information and forecasting capabilities are not going to be successful in managing risk. Risk management and forecasting techniques will need to be developed around specific macro- and micro-economic changes.  They will also need to incorporate a number of economic scenarios to properly reflect the range of possible future outcomes about risk management and risk management solutions. Moving forward, it will be vital to understand the differences in unemployment between Dallas and Houston and between regions that rely on automotive manufacturing and those with hi-tech jobs. These differences will directly impact the performance of lenders’ specific footprints, as this year’s “Best Place to Live” according to Money.CNN.com can quickly become next year’s foreclosure capital. ihttp://money.cnn.com/2009/10/28/real_estate/foreclosures_worst_cities/index.htm?postversion=2009102811 iihttp://money.cnn.com/galleries/2009/real_estate/0910/gallery.foreclosures_worst_cities/2.html  

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.