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By: Margarita Lim Recently, the Social Security Administration (SSA) announced that it will change how Social Security numbers (SSN) will be issued, with a move toward a random method of assigning SSNs. Social Security numbers are historically 9 digits in length, and are comprised of a three-digit number that represents a geographic area, a two-digit number referred to as a Group number and a four digit serial number.You can go to http://www.ssa.gov/employer/randomization.html to learn more about this procedural change, but in summary, the random assignment of SSNs will affect: • The geographic significance of the first three digits of the SSN because it will no longer uniquely represent specific states • The correlation of the Group number (the fourth and fifth digits of the SSN) to an issuance date range. What does this mean? It means that if you’re a business or agency that uses any type of authentication product in order to minimize fraud losses, one of the components used to verify a consumer’s identity – Social Security number, will no longer be validated with respect to state and date.   However, one of the main advantages of utilizing a risk-based approach to authentication is the reduction in over-reliance on one identity element validation result.  Validation of SSN issuance date and state, while useful in determining certain levels of risk, is but one of many attributes and conditions utilized in detailed results, robust analytics, and risk-based decisioning.  It can also be argued that the randomization of SSN issuance, while somewhat impacting the intelligence we can glean from a specific number, may also prove to be beneficial to consumer protection and the overall confidence in the SSN issuance process.

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: Kari Michel As consumers and businesses continue to experience financial hardship, the likelihood of continued bankruptcy filings is fairly strong. Data from the Administrative Office of the U.S. Courts show there were 1,222,589 filings through September, versus 1,100,035 in the first nine months of 2009. According to American Bankruptcy Institute executive director Samuel J. Gerdano, "As the economy looks to climb out of the recent recession, businesses and consumers continue to file for bankruptcy to regain their financial footing. With unemployment hovering near 10% and access to credit remaining tight, total filings in 2010 will likely exceed 1.6 million." Given the bankruptcy trends, what can lenders do to protect themselves from acquiring consumers that are at risk for filing for bankruptcy? Bankruptcy scores are available, such as Bankruptcy PLUS, and are developed to accurately identify characteristics specific to a consumer filing for bankruptcy. Bankruptcy scores are typically used in conjunction with risk scores to set effective acquisition strategies. _________________ Source:  http://www.collectionscreditrisk.com/news/bankruptcy-filings-up-3003998-1.html  

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

As E-Government customer demand and opportunity increases, so too will regulatory requirements and associated guidance become more standardized and uniformly adopted.  Regardless of credentialing techniques and ongoing access management, all enrollment processes must continue to be founded in accurate and, most importantly, predictive risk-based authentication. Such authentication tools must be able to evolve as new technologies and data assets become available, as compliance requirements and guidance become more defined, and as specific fraud threats align with various access channels and unique customer segments. A risk-based fraud detection system allows institutions to make customer relationship and transactional decisions based not on a handful of rules or conditions in isolation, but on a holistic view of a customer’s identity and predicted likelihood of associated identity theft.  To implement efficient and appropriate risk-based authentication procedures, the incorporation of comprehensive and broadly categorized data assets must be combined with targeted analytics and consistent decisioning policies to achieve a measurably effective balance between fraud detection and positive identity proofing results. The inherent value of a risk-based approach to authentication lies in the ability to strike such a balance not only in a current environment, but as that environment shifts as do its underlying forces. The National Institute of Standards and Technology, in special publication 800-63, defines electronic authentication (E-authentication) as “the process of establishing confidence in user identities electronically presented to an information system”. Since, as stated in publication 800-63, “individuals are enrolled and undergo an identity proofing process in which their identity is bound to an authentication secret, called a token”, it is imperative that identity proofing is founded in an approach that generates confidence in the authentication process. Experian believes that a risk-based approach that can separate valid from invalid identities using a combination of data and proven quantitative techniques is best. As “individuals are remotely authenticated to systems and applications over an open network, using a token in an authentication protocol”, enrollment processes that drive ultimate provision of tokens must be implemented with an eye towards identity risk, and not simply a series of checks against one or more third party data assets. If the “keys to the kingdom” are housed in the ongoing use of tokens provided by Credentials Service Providers (CRA) and binding credentials to that token, trusted Registration Authorities (RA) must employ highly predictive identity proofing techniques designed to segment true, low-risk identities from identities that may have been manipulated, fabricated, or in true-form are subject to fraudulent use, abuse or victimization. Many compliance-oriented authentication requirements (ex. USA PATRIOT Act, FACTA Red Flags Rule) and resultant processes hinge upon identity element (ex. name, address, Social Security number, phone number) validation and verification checks. Without minimizing the importance of performing such checks, the purpose of a more risk-based approach to authentication is to leverage other data sources and quantitative techniques to further assess the probability of fraudulent behavior.

Experian recently contributed to a TSYS whitepaper focused on the various threats associated with first party fraud. I think the paper does a good job at summarizing the problem, and points out some very important strategies that can be employed to help both prevent first party fraud losses and detect those already in an institution’s active and collections account populations. I’d urge you to have a look at this paper as you begin asking the right questions within your own organization. Watch here The bad news is that first party fraud may currently account for up to 20 percent of credit charge-offs. The good news is that scoring models (using a combination of credit attributes and identity element analysis) targeted at various first party fraud schemes such as Bust Out, Never Pay, and even Synthetic Identity are quite effective in all phases of the customer lifecycle. Appropriate implementation of these models, usually involving coordinated decisioning strategies across both fraud and credit policies, can stem many losses either at account acquisition, or at least early enough in an account management stage, to substantially reduce average fraud balances. The key is to prevent these accounts from ending up in collections queues where they’ll never have any chance of actually being collected upon. A traditional customer information program and identity theft prevention program (associated, for example with the Red Flags Rule) will often fail to identify first party fraud, as these are founded in identity element verification and validation, checks that often ‘pass’ when applied to first party fraudsters.

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: Kennis Wong In the last entry, I mentioned that consumers’ participation in protecting their own identity information is an important aspect of an identity theft prevention program to minimize fraud loss.  Large financial institutions are starting to take charge in educating their customers, but others are having a hard time investing in such initiatives. I do understand that it is difficult to establish a direct linkage of revenue and positive return on investment for this type of activities. Business may view customer education of identity protection as a public service but not a necessity. After all, if my customer loses his identity information, it doesn’t necessarily mean that identity fraud will happen to my very own organization. But educating customers about identity protection and fraud trends can be a marketing tool and can increase customer loyalty, in additions to actual fraud prevention. Although consumers may not be aware of all the precautions they can take to protect their identity, undoubtedly identity theft is a hot topic in the media today. If there are two banks providing about the same service, but one of them goes an extra mile to provide me education on preventing identity theft, I would go with that bank. Also, as a financial institution, if my customers understand identity protection more, they would understand why I am putting some procedure in place and would be glad to comply with them. For example, they would be more patient when spending another minute in answering knowledge-based authentication questions, so that for their own protection, the bank can assure they are the true identity owners. Consumers can also actively monitor their credit report, whether through the bank or through other third party vendors. When consumers receive fraud alert from activities that could be a result of identity theft, they can actively contact the financial institutions about the situation. The sooner the identity fraud is discovered, the better off for both the consumers and the businesses.

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: Kennis Wong As a fraud management professional, naturally I am surrounded by fraud prevention topics and other professionals in the field all the time.  Financial, ecommerce, retail, telecommunication, government and other organizations are used to talking about performance, scoring models, ROI, false-positives, operational efficiency, customer satisfaction trade-off, loss provisioning, decisioning strategy or any other sophisticated measures when it comes to fraud management.  But when I bring up the topic of fraud outside of this circle, I am always surprised to see how little educated the general public is about an issue that is so critical to their financial health. I met a woman in an event several weeks ago. After learning about my occupation, she told me her story about someone from XYZ credit card company calling her and asking for her Social Security number, date of birth and other personal identifying information. Only days after she gave out the information that she realized things didn’t seem right. She called the credit card company and got her credit card re-issued. But at the time I talked to her, she still didn’t know enough to realize that the fraudster could now use her identity to start any new financial relationship under her name. As long as consumers are ignorant about protecting their identity information, businesses’ identity theft prevention program will not be complete and identity fraud will occur as a result of this weak link. To address this vulnerability and minimize fraud, consumers need to be educated.

-- by, Andrew Gulledge One of the quickest and easiest ways to reduce fraud in your portfolio is to incorporate question weighting into your out of wallet question strategy. To continue the use of knowledge based authentication without question weighting is to assign a point value of 100 points to each question. This is somewhat arbitrary (and a bit sloppy) when we know that certain questions consistently perform better than others. So if a fraudster gets 3 easier questions right, and 1 harder question wrong they will have an easier time passing your authentication process without question weighting. If, on the other hand, you adopt question weighting as part of your overall risk based authentication approach, that same fraudster would score much worse on the same KBA session. The 1 question that they got wrong would have cost them a lot of points, and the 3 easier questions they got right wouldn’t have given them as many points. Question weighting based on known fraud trends is more punitive for the fraudsters. Let’s say the easier questions were worth 50 points each, and the harder question was worth 150 points. Without question weighting, the fraudster would have scored 75% (300 out of 400 points). With question weighting, the fraudster would have scored 50% (150 out of 300 points correct). Your decisioning strategy might well have failed him with a score of 50, but passed him with a score of 75. Question weighting will often kick the fraudsters into the fail regions of your decisioning strategy, which is exactly what risk based authentication is all about. Consult with your fraud account management representative to see if you are making the most out of your KBA experience with the intelligent use of question weighting. It is a no-brainer way to improve your overall fraud prevention, even if you keep your overall pass rate the same. Question weighting is an easy way to squeeze more value of your knowledge based authentication tool.  

-- by, Andrew GulledgeThe intelligent use of question weighting in KBA should be a no-brainer for anyone using out of wallet questions. Here’s the deal: some authentication questions consistently give fraudsters a harder time than other questions. Why not capitalize on that knowledge?Question weighting is where each question type has a certain number of points associated with it. So a question that fraudsters have an easier time with might be worth only 50 points, while a question that fraudsters often struggle with might be worth 150 points. So the KBA score ends up being the total points correct divided by the total possible points. The point is to make the entire KBA session more punitive for the bad guys.Fraud analytics are absolutely essential to the use of intelligent question weighting. While fraud prevention vendors should have recommended question weights as part of their fraud best practices, if you can provide us with as many examples as possible of known fraud that went through the out of wallet questions, we can refine the best practice question weighting model to work better for your specific population.Even if we keep your pass rate the same, we can lower your fraud rate. On the other hand, we can up your pass rate while keeping the fraud rate consistent.  So whether your aim it to reduce your false positive rate (i.e., pass more of the good consumers) or to reduce your fraud rate (i.e., fail more of the fraudsters), or some combination of the two, question weighting will help you get there.

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

By: Margarita Lim You may be surprised to learn that identity theft isn’t just a crime committed by an individual or individuals. There are identity theft rings that are organized and operated like corporations. A recent Justice Department press release described such an operation in New Jersey that involved 53 individuals who took part in a known fraud activity called Bust Out Fraud. Basically, the fraud ring purchased valid social security cards and then sold the social security cards to customers who then obtained driver’s licenses and other proof of identity-type cards. The fraud ring then built up the credit scores of these customers by adding them to existing credit card accounts. Once the customers with the fraudulent identities achieved good credit scores, then they opened their own fraudulent bank accounts, credit cards, lines of credit, etc.  The credit cards were used to make fraudulent purchases or rack up charges with vendors in co-hoots with the fraud ring and the fraudulent bank accounts were used to pay off the charge accounts or the charges went unpaid. Fraud trends like these cost banks, credit card companies and many others millions of dollars – costs that ultimately get passed on to you and me, the consumers. Fortunately, Experian has Fraud Products that can help companies minimize fraud losses from Bust Out Fraud as well as other types of fraud. Our BustOut Score helps decrease bust out losses by predicting and detecting bust out frauds one to three months in advance of the event happening. In addition, we have Fraud Shield Indicators or fraud alerts available on credit reports that flag when there is a recent or new authorized user added to an established credit account. Experian supports Identity Theft Prevention Programs by offering highly accurate consumer identity verification services. We’re not reliant solely on credit bureau data and are able to use multi-sourced data to confirm different components of a consumer’s identity – name, address, date of birth, etc. Our consumer authentication and fraud prevention product, Precise ID, and our knowledge based authentication product, Knowledge IQ, are highly respected in the marketplace for their reliability, quality and accuracy.  

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