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By: Kari Michel The U.S. government and mortgage lenders have developed various loan modification programs to help homeowners better manage their mortgage debt so that they can meet their monthly payment obligations. Given these new programs, what is the impact to the consumer’s score? Do consumer scores drop more if they work with their lenders to get their mortgage loan restructured or if they file for bankruptcy? The finding from a study conducted by VantageScore ® Solutions* reveals that a delinquency on a mortgage has a greater impact on the consumer’s score than a loan modification. Bankruptcy, short sale, and foreclosure have the greatest impact to a score. A bankruptcy or poor bankruptcy score can negatively impact a consumer for a minimum of seven years with a potential score decrease of 365 points. However, with a loan modification, consumers can rehabilitate their scores to an acceptable risk level within nine months. This depends on them bringing all their delinquent accounts to current status. Loan modifications have little impact on their consumer credit score and the influence on their score can range from a 20 point decrease to an increase of 30 points. Lenders should proactively seek out a mortgage loan modification before consumers experience severe delinquency in their credit files and credit score trends. The restructured mortgage should provide sufficient cash availability to remain with the consumer. This ensures that any other delinquent debts can be updated to current status. Whenever possible, bankruptcy should be avoided because it has the greatest consequences for the lender and the consumer. *For more detailed information on this study, Credit Scoring and Mortgage Modifications: What lenders need to know, please click on this link to access an archived file of a recent webinar: http://register.sourcemediaconferences.com/click/clickReg.cfm?URLID=5258
By: Kennis Wong It\'s true that intent is difficult to prove. It\'s also true that financial situations change. That\'s why financial institutions have not, yet, successfully fought off first-party fraud. However, there are some tell-tale signs of intent when you look at the consumer\'s behavior as a whole, particularly across all his/her financial relationships. For example, in a classic bust out case, you would see that the consumer, with pristine credit history, applies for more and more credit cards while maintaining a relatively low balance and utilization across all issuers. If you graph the number of credit cards and number of credit applications over time, you would see two hockey-stick lines. When the accounts go bad, they do so at almost the same time. This pattern is not always apparent at the time of origination, that\'s why it\'s important to monitor frequently for account review and fraud database alerts. On the other hand, consumers with financial difficulties have different patterns. They might have more credit lines over time, but you would see that some credit lines may go delinquent while others don\'t. You might also see that consumers cure some lines after delinquencies…you can see their struggle of trying to pay. Of course the intent \"pattern\" is not always clear. When dealing with fraudsters in fraud account management, even with the help of the fraud database, fraud trends and fraud alert, change their behaviors and use new techniques.
By: Tracy Bremmer There has been a lot of hype these days about people strategically defaulting on their mortgage loans. In other words, a consumer is underwater on their house and so he/she makes a strategic decision to walk away from it. In these instances, the consumer is current on all of their non-mortgage accounts, but because the value of their home is less than what they owe, they make the decision to default on their mortgage loan. Experian and Oliver Wyman teamed up to really dig into this population and determine these issues: • Does this population really exist? • If so, what are the characteristics of this population, such as assessing credit risk or bankruptcy scores? • How should loan modification strategies be differentiated based on this population? This blog will be one of a three-part series that addresses these questions. Let’s begin with the first question. 1. Does this population really exist? The quick answer is yes – this population does indeed exist. In fact, in 2008 strategic defaulters represented 18 percent of all mortgage defaults, up 500 percent from 2004. When we conducted our study we found there were varying populations that also existed when it came to mortgage defaults. In fact, we classified mortgage defaulters into five categories: strategic defaulter, cash flow manager, distressed defaulter, no non-real estate trades, and pay-downs. We defined these populations as follows: • Strategic defaulter - Borrowers who are delinquent on their mortgages, even when they can afford the payment, because their loan balance exceeds the value of their home, • Cash flow manager - Borrowers facing delinquency issues with their mortgage because of temporary distress, but continue to make payments on all credit obligations, • Distressed defaulter - Borrowers facing potential affordability issues that go delinquent on their mortgage along with other credit obligations, • No non-real estate trades – Borrowers who are delinquent on their mortgage, however they do not have any other non-mortgage trades to evaluate if they have strategically defaulted or are in distress, • Pay-downs – Borrowers who pay down their mortgage loan. In my next blog, I will address the characteristic differences in behavior between these populations. Specifically, I will evaluate what characteristics make strategic defaulters stand out from the rest and what is unique about the cash flow managers. Source: Experian-Oliver Wyman Market Intelligence Reports; Understanding Strategic Default in Mortgage topical study / webinar. August 2009.
By: Kari Michel Most lenders use a credit scoring model in their decision process for opening new accounts; however, between 35 and 50 million adults in the US may be considered unscoreable with traditional credit scoring models. That is equivalent to 18-to-25 percent of the adult population. Due to recent market conditions and shrinking qualified candidates, lenders have placed a renewed interest in assessing the risk of this under served population. Unscoreable consumers could be a pocket of missed opportunity for many lenders. To assess these consumers, lenders must have the ability to better distinguish between consumers with a clear track record of unfavorable credit behaviors versus those that are just beginning to develop their credit history and credit risk models. Unscoreable consumers can be divided into three populations: • Infrequent credit users: Consumers who have not been active on their accounts for the past six months, and who prefer to use non-traditional credit tools for their financial needs. • New entrants: Consumers who do not have at least one account with more than six months of activity; including young adults just entering the workforce, recently divorced or widowed individuals with little or no credit history in their name, newly arrived immigrants, or people who avoid the traditional system by choice. • Thin file consumers: Consumers who have less than three accounts and rarely utilize traditional credit and likely prefer using alternative credit tools and credit score trends. A study done by VantageScore® Solutions, LLC shows that a large percentage of the unscoreable population can be scored with VantageScore* and a portion of these are credit-worthy (defined as the population of consumers who have a cumulative likelihood to become 90 days or more delinquent is less than 5 percent). The following is a high-level summary of the findings for consumers who had at least one trade: Lenders can review their credit decisioning process to determine if they have the tools in place to assess the risk of those unscoreable consumers. As with this population there is an opportunity for portfolio expansion as demonstrated by the VantageScore study. *VantageScore is a generic credit scoring model introduced to meet the market demands for a highly predictive consumer score. Developed as a joint venture among the three major credit reporting companies (CRCs) – Equifax, Experian and TransUnion.
By: Wendy Greenawalt In the last installment of my three part series dispelling credit attribute myths, we’ll discuss the myth that the lift achieved by utilizing new attributes is minimal, so it is not worth the effort of evaluating and/or implementing new credit attributes. First, evaluating accuracy and efficiency of credit attributes is hard to measure. Experian data experts are some of the best in the business and, in this edition, we will discuss some of the methods Experian uses to evaluate attribute performance. When considering any new attributes, the first method we use to validate statistical performance is to complete a statistical head-to-head comparison. This method incorporates the use of KS (Kolmogorov–Smirnov statistic), Gini coefficient, worst-scoring capture rate or odds ratio when comparing two samples. Once completed, we implement an established standard process to measure value from different outcomes in an automated and consistent format. While this process may be time and labor intensive, the reward can be found in the financial savings that can be obtained by identifying the right segments, including: • Risk models that better identify “bad” accounts and minimizing losses • Marketing models that improve targeting while maximizing campaign dollars spent • Collections models that enhance identification of recoverable accounts leading to more recovered dollars with lower fixed costs Credit attributes Recently, Experian conducted a similar exercise and found that an improvement of 2-to-22 percent in risk prediction can be achieved through the implementation of new attributes. When these metrics are applied to a portfolio where several hundred bad accounts are now captured, the resulting savings can add up quickly (500 accounts with average loss rate of $3,000 = $1.5M potential savings). These savings over time more than justify the cost of evaluating and implementing new credit attributes.
By: Wendy Greenawalt In the second installment of my three part series, dispelling credit attribute myths, we will discuss why attributes with similar descriptions are not always the same. The U.S. credit reporting bureaus are the most comprehensive in the world. Creating meaningful attributes requires extensive knowledge of the three credit bureaus’ data. Ensuring credit attributes are up-to-date and created by informed data experts. Leveraging complete bureau data is also essential to obtaining long-term strategic success. To illustrate why attributes with similar names may not be the same let’s discuss a basic attribute, such as “number of accounts paid satisfactory.” While the definition, may at first seem straight forward, once the analysis begins there are many variables that must be considered before finalizing the definition, including: Should the credit attributes include trades currently satisfactory or ever satisfactory? Do we include paid charge-offs, paid collections, etc.? Are there any date parameters for credit attributes? Are there any trades that should be excluded? Should accounts that have a final status of \"paid” be included? These types of questions and many others must be carefully identified and assessed to ensure the desired behavior is captured when creating credit attributes. Without careful attention to detail, a simple attribute definition could include behavior that was not intended. This could negatively impact the risk level associated with an organization’s portfolio. Our recommendation is to complete a detailed analysis up-front and always validate the results to ensure the desired outcome is achieved. Incorporating this best practice will guarantee that credit attributes created are capturing the behavior intended.
By: Wendy Greenawalt This blog kicks off a three part series exploring some common myths regarding credit attributes. Since Experian has relationships with thousands of organizations spanning multiple industries, we often get asked the same types of questions from clients of all sizes and industries. One of the questions we hear frequently from our clients is that they already have credit attributes in place, so there is little to no benefit in implementing a new attribute set. Our response is that while existing credit attributes may continue to be predictive, changes to the type of data available from the credit bureaus can provide benefits when evaluating consumer behavior. To illustrate this point, let’s discuss a common problem that most lenders are facing today-- collections. Delinquency and charge-off continue to increase and many organizations are having difficulty trying to determine the appropriate action to take on an account because consumer behavior has drastically changed regarding credit attributes. New codes and fields are now reported to the credit bureaus and can be effectively used to improve collection-related activities. Specifically, attributes can now be created to help identify consumers who are rebounding from previous account delinquencies. In addition, lenders can evaluate the number and outstanding balances of collection or other types of trades. This can be achieved while considering the percentage of accounts that are delinquent and the specific type of accounts affected after assessing credit risk. The utilization of this type of data helps an organization to make collection decisions based on very granular account data. This is done while considering new consumer trends such as strategic defaulters. Understanding all of the consumer variables will enable an organization to decide if the account should be allowed to self-cure. If so, immediate action should be taken or modification of account terms should be contemplated. Incorporating new data sources and updating attributes on a regular basis allows lenders to react to market trends quickly by proactively managing strategies.
By: Kennis Wong In Part 1 of Generic fraud score, we emphasized the importance of a risk-based approach when it comes to fraud detection. Here are some further questions you may want to consider. What is the performance window? When a model is built, it has a defined performance window. That means the score is predicting a certain outcome within that time period. For example, a traditional risk score may be predicting accounts that are decreasing in twenty-four months. That score may not perform well if your population typically worsens in two months. This question is particularly important when it relates to scoring your population. For example, if a bust-out score has a performance window of three months, and you score your accounts at the time of acquisition, it would only catch accounts that are busting-out within the next three months. As a result, you should score your accounts during periodic account reviews in addition to the time of acquisition to ensure you catch all bust-outs. Therefore, bust out fraud is an important indicator. Which accounts should I score? While it’s typical for creditors to use a fraud score on every applicant at the time of acquisition, they may not score all their accounts during review. For example, they may exclude inactive accounts or older accounts assuming those with a long history means less likelihood of fraud. This mistake may be expensive. For instance, the typical bust-out behavior is for fraudsters to apply for cards way before they intend to bust out. This may be forty-eight months or more. So when you think they are good and profitable customers, they can strike and leave you with seriously injury. Make sure that your fraud database is updated and accurate. As a result, the recommended approach is to score your entire portfolio during account review. How often do I validate the score? The answer is very often -- this may be monthly or quarterly. You want to understand whether the score is working for you – do your actual results match the volume and risk projections? Shifts of your score distribution will almost certainly occur over time. To meet your objectives over the long run, continue to monitor and adjust cutoffs. Keep your fraud database updated at all times.
By: Kennis Wong In this blog entry, we have repeatedly emphasized the importance of a risk-based approach when it comes to fraud detection. Scoring and analytics are essentially the heart of this approach. However, unlike the rule-based approach, where users can easily understand the results, (i.e. was the S.S.N. reported deceased? Yes/No; Is the application address the same as the best address on the credit bureau? Yes/No), scores are generated in a black box where the reason for the eventual score is not always apparent even in a fraud database. Hence more homework needs to be done when selecting and using a generic fraud score to make sure they satisfy your needs. Here are some basic questions you may want to ask yourself: What do I want the score to predict? This may seem like a very basic question, but it does warrant your consideration. Are you trying to detect these areas in your fraud database? First-party fraud, third-party fraud, bust out fraud, first payment default, never pay, or a combination of these? These questions are particularly important when you are validating a fraud model. For example, if you only have third-party fraud tagged in your test file, a bust out fraud model would not perform well. It would just be a waste of your time. What data was used for model development? Other important questions you may want to ask yourself include: Was the score based on sub-prime credit card data, auto loan data, retail card data or another fraud database? It’s not a definite deal breaker if it was built with credit card data, but, if you have a retail card portfolio, it may still perform well for you. If the scores are too far off, though, you may not have good result. Moreover, you also want to understand the number of different portfolios used for model development. For example, if only one creditor’s data is used, then it may not have the general applicability to other portfolios.
By: Kristan Keelan Most financial institutions are well underway in complying with the FTC’s ID Theft Red Flags Rule by: 1. Identifying covered accounts 2. Determining what red flags need to be monitored 3. Implementing a risk based approach However, one of the areas that seems to be overlooked in complying with the rule is the area of commercial accounts. Did your institution include commercial accounts when identifying covered accounts? You’re not alone if you focused only on consumer accounts initially. Keep in mind that commercial credit and deposit accounts also can be included as covered accounts when there is a “reasonably foreseeable risk” of identity theft to customers or to safety and soundness. Start by determining if there is a reasonably foreseeable risk of identity theft in a business or commercial account, especially in small business accounts. Consider the risk of identity theft presented by the methods used to open business accounts, the methods provided to access business accounts, and previous experiences with identity theft on a business account. I encourage you to revisit your institution’s compliance program and review whether commercial accounts have been examined closely enough.
By: Kristan Keelan What do you think of when you hear the word “fraud”? Someone stealing your personal identity? Perhaps the recent news story of the five individuals indicted for gaining more than $4 million from 95,000 stolen credit card numbers? It’s unlikely that small business fraud was at the top of your mind. Yet, just like consumers, businesses face a broad- range of first- and third-party fraud behaviors, varying significantly in frequency, severity and complexity. Business-related fraud trends call for new fraud best practices to minimize fraud. First let’s look at first-party fraud. A first-party, or victimless, fraud profile is characterized by having some form of material misrepresentation (for example, misstating revenue figures on the application) by the business owner without that owner’s intent or immediate capacity to pay the loan item. Historically, during periods of economic downturn or misfortune, this type of fraud is more common. This intuitively makes sense — individuals under extreme financial pressure are more likely to resort to desperate measures, such as misstating financial information on an application to obtain credit. Third-party commercial fraud occurs when a third party steals the identification details of a known business or business owner in order to open credit in the business victim’s name. With creditors becoming more stringent with credit-granting policies on new accounts, we’re seeing seasoned fraudsters shift their focus on taking over existing business or business owner identities. Overall, fraudsters seem to be migrating from consumer to commercial fraud. I think one of the most common reasons for this is that commercial fraud doesn’t receive the same amount of attention as consumer fraud. Thus, it’s become easier for fraudsters to slip under the radar by perpetrating their crimes through the commercial channel. Also, keep in mind that businesses are often not seen as victims in the same way that consumers are. For example, victimized businesses aren’t afforded the protections that consumers receive under identity theft laws, such as access to credit information. These factors, coupled with the fact that business-to-business fraud is approximately three-to-ten times more “profitable” per occurrence than consumer fraud, play a role in leading fraudsters increasingly toward commercial fraud.
By: Kari Michel In August, consumer bankruptcy filings were up by 24 percent over the past year and are expected to increase to 1.4 million this year. “Consumers continue to turn to bankruptcy as a shield from the sustained financial pressures of today’s economy,” said American Bankruptcy Institute’s Executive Director Samuel J. Gerdano. What are lenders doing to protect themselves from bankruptcy losses? In my last blog, I talked about the differences and advantage of using both risk and bankruptcy scores. Many lenders are mitigating and managing bankruptcy losses by including bankruptcy scores into their standard account management programs. Here are some ways lenders are using bankruptcy scores: • Incorporating them into existing internal segmentation schemes for enhanced separation and treatment assessment of high risk accounts; • Developing improved strategies to act on high-bankruptcy-risk accounts • In order to manage at-risk consumers proactively and • Assessing low-risk customers for up-sell opportunities. Implementation of a bankruptcy score is recommended given the economic conditions and expected rise in consumer bankruptcy. When conducting model validations/assessments, we recommend that you use the model that best rank orders bankruptcy or pushes more bankruptcies into the lowest scoring ranges. In validating our Experian/Visa BankruptcyPredict score, results showed BankruptcyPredict was able to identify 18 to 30 percent more bankruptcy compared to other bankruptcy models. It also identified 12 to 33 percent more bankruptcy compared to risk scores in the lowest five percent of the score range. This supports the need to have distinct bankruptcy scores in addition to risk scores.
By: Kennis Wong As I said in my last post, when consumers and the media talk about fraud and fraud risk, they are usually referring to third-party frauds. When financial institutions or other organizations talk about fraud and fraud best practices, they usually refer to both first- and third-party frauds. The lesser-known fraud cousin, first-party fraud, does not involve stolen identities. As a result, first-party fraud is sometimes called victimless fraud. However, being victimless can’t be further from the truth. The true victims of these frauds are the financial institutions that lose millions of dollars to people who intentionally defraud the system. First-party frauds happen when someone uses his/her own identity or a fictitious identity to apply for credit without the intention to fulfill their payment obligation. As you can imagine, fraud detection of this type is very difficult. Since fraudsters are mostly who they say they are, you can’t check the inconsistencies of identities in their applications. The third-party fraud models and authentication tools will have no effect on first-party frauds. Moreover, the line between first-party fraud and regular credit risk is very fuzzy. According to Wikipedia, credit risk is the risk of loss due to a debtor\'s non-payment of a loan or other line of credit. Doesn’t the definition sound similar to first-party fraud? In practice, the distinction is even blurrier. That’s why many financial institutions are putting first-party frauds in the risk bucket. But there is one subtle difference: that is the intent of the debtor. Are the applicants planning not to pay when they apply or use the credit? If not, that’s first-party fraud. To effectively detect frauds of this type, fraud models need to look into the intention of the applicants.
By: Kennis Wong When consumers and the media talk about fraud and fraud risk, nine out of ten times they are referring to third-party frauds. When financial institutions or other organizations talk about fraud, fraud best practices, or their efforts to minimize fraud, they usually refer to both first- and third-party frauds. The difference between the two fraud types is huge. Third-party frauds happen when someone impersonates the genuine identity owner to apply for credit or use existing credit. When it’s discovered, the victim, or the genuine identity owner, may have some financial loss -- and a whole lot of trouble fixing the mess. Third-party frauds get most of the spotlight in newspaper reporting primarily because of large-scale identity data losses. These data losses may not result in frauds per se, but the perception is that these consumers are now more susceptible to third-party frauds. Financial institutions are getting increasingly sophisticated in using fraud models to detect third-party frauds at acquisition. In a nutshell, these fraud models are detecting frauds by looking at the likelihood of applicants being who they say they are. Institutions bounce the applicants’ identity information off of internal and external data sources such as: credit; known fraud; application; IP; device; employment; business relationship; DDA; demographic; auto; property; and public record. The risk-based approach takes into account the intricate similarities and discrepancies of each piece of data element. In my next blog entry, I’ll discuss first-party fraud.
By: Ken Pruett I find it interesting that the media still focuses all of their attention on identity theft when it comes to credit-related fraud. Don’t get me wrong. This is still a serious problem and is certainly not going away any time soon. But, there are other types of financial fraud that are costing all of us money, indirectly, in the long run. I thought it would be worth mentioning some of these today. Although third party fraud, (which involves someone victimizing a consumer), gets most of the attention, first party fraud (perpetrated by the actual consumer) can be even more costly. “Never pay” and “bust out” are two fraud scenarios that seem to be on the rise and warrant attention when developing a fraud prevention program. Never Pay A growing fraud problem that occurs during the acquisition stage of the customer life cycle is “never pay”. This is also classified as first payment default fraud. Another term we often hear to describe this type of perpetrator is “straight roller”. This type of fraudster is best described as someone who signs up for a product or service -- and never makes a payment. This fraud problem occurs when a consumer makes an application for a loan or credit card. The consumer provides true identification information but changes one or two elements (such as the address or social security number). He does this so that he can claim later that he did not apply for the credit. When he’s granted credit, he often makes purchases close to the limit provided on the account. (Why get the 32 inch flat screen TV when the 60 inch is on the next store shelf -- when you know you are not going to pay for it anyway?) These fraudsters never make any payments at all on these accounts. The accounts usually end up in collections. Because standard credit risk scores look at long term credit, they often are not effective in predicting this type of fraud. The best approach is to use a fraud model specifically targeted for this issue. Bust Out Fraud Of all the fraud scenarios, bust out fraud is one of the most talked about topics when we meet with credit card companies. This type of fraud occurs during the account management phase of the customer lifecycle. It is characterized by a person obtaining credit, typically a loan or credit card, and maintaining a good credit history with the account holder for a reasonable period of time. Just prior to the bust out point, the fraudster will pay off the majority of the balance, often by using a bad check. She will then run the card up close to the limit again -- and then disappear. Losses for this type of fraud are higher than average credit card losses. Losses between 150 to 200 percent of the credit limit are typical. We’ve seen this pattern at numerous credit card institutions across many of their accounts. This is a very difficult type of fraud to prevent. At the time of application, the customer typically looks good from a credit and fraud standpoint. Many companies have some account management tools in place to help prevent this type of fraud, but their systems only have a view into the one account tied to the customer. A best practice for preventing this type of fraud is to use tools that look at all the accounts tied to the consumer -- along with other metrics such as recent inquiries. When taking all of these factors into consideration, one can better predict this growing fraud type.
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