Credit & Risk

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.  

In my last blog, I discussed the basic concept of a maturation curve, as illustrated below: Exhibit 1 In Exhibit 1, we examine different vintages beginning with those loans originated by year during Q2 2002 through Q2 2008. The purpose of the vintage analysis is to identify those vintages that have a steeper slope towards delinquency, which is also known as delinquency maturation curve. The X-axis represents a timeline in months, from month of origination.  Furthermore, the Y-axis represents the 90+ delinquency rate expressed as a percentage of balances in the portfolio.  Those vintage analyses that have a steeper slope have reached a normalized level of delinquency sooner, and could in fact, have a trend line suggesting that they overshoot the expected delinquency rate for the portfolio based upon credit quality standards. So how can you use a maturation curve as a useful portfolio management tool? As a consultant, I spend a lot of time with clients trying to understand issues, such as why their charge-offs are higher than plan (budget).  I also investigate whether the reason for the excess credit costs are related to collections effectiveness, collections strategy, collections efficiency, credit quality or a poorly conceived budget. I recall one such engagement, where different functional teams within the client’s organization were pointing fingers at each other because their budget evaporated. One look at their maturation curves and I had the answers I needed. I noticed that two vintages per year had maturation curves that were pointed due north, with a much steeper curve than all other months of the year. Why would only two months or vintages of originations each year be so different than all other vintage analyses in terms of performance? I went back to my career experiences in banking, where I worked for a large regional bank that ran marketing solicitations several times yearly. Each of these programs was targeted to prospects that, in most instances, were out-of-market, or in other words, outside of the bank’s branch footprint. Bingo! I got it! The client was soliciting new customers out of his market, and was likely getting adverse selection. While he targeted the “right” customers – those with credit scores and credit attributes within an acceptable range, the best of that targeted group was not interested in accepting their offer, because they did not do business with my client, and would prefer to do business with an in-market player. Meanwhile, the lower grade prospects were accepting the offers, because it was a better deal than they could get in-market. The result was adverse selection...and what I was staring at was the "smoking gun" I’d been looking for with these two-a-year vintages (vintage analysis) that reached the moon in terms of delinquency. That’s the value of building a maturation curve analysis – to identify specific vintages that have characteristics that are more adverse than others.  I also use the information to target those adverse populations and track the performance of specific treatment strategies aimed at containing losses on those segments. You might use this to identify which originations vintages of your home equity portfolio are most likely to migrate to higher levels of delinquency; then use credit bureau attributes to identify specific borrowers for an early lifecycle treatment strategy. As that beer commercial says – “brilliant!”  

By: Tom Hannagan Understanding RORAC and RAROC I was hoping someone would ask about these risk management terms…and someone did. The obvious answer is that the “A” and the “O” are reversed. But, there’s more to it than that. First, let’s see how the acronyms were derived. RORAC is Return on Risk-Adjusted Capital. RAROC is Risk-Adjusted Return on Capital. Both of these five-letter abbreviations are a step up from ROE. This is natural, I suppose, since ROE, meaning Return on Equity of course, is merely a three-letter profitability ratio. A serious breakthrough in risk management and profit performance measurement will have to move up to at least six initials in its abbreviation. Nonetheless, ROE is the jumping-off point towards both RORAC and RAROC. ROE is generally Net Income divided by Equity, and ROE has many advantages over Return on Assets (ROA), which is Net Income divided by Average Assets. I promise, really, no more new acronyms in this post. The calculations themselves are pretty easy. ROA tends to tell us how effectively an organization is generating general ledger earnings on its base of assets.  This used to be the most popular way of comparing banks to each other and for banks to monitor their own performance from period to period. Many bank executives in the U.S. still prefer to use ROA, although this tends to be those at smaller banks. ROE tends to tell us how effectively an organization is taking advantage of its base of equity, or risk-based capital. This has gained in popularity for several reasons and has become the preferred measure at medium and larger U.S. banks, and all international banks. One huge reason for the growing popularity of ROE is simply that it is not asset-dependent. ROE can be applied to any line of business or any product. You must have “assets” for ROA, since one cannot divide by zero. Hopefully your Equity account is always greater than zero. If not, well, lets just say it’s too late to read about this general topic. The flexibility of basing profitability measurement on contribution to Equity allows banks with differing asset structures to be compared to each other.  This also may apply even for banks to be compared to other types of businesses. The asset-independency of ROE can also allow a bank to compare internal product lines to each other. Perhaps most importantly, this permits looking at the comparative profitability of lines of business that are almost complete opposites, like lending versus deposit services. This includes risk-based pricing considerations. This would be difficult, if even possible, using ROA. ROE also tells us how effectively a bank (or any business) is using shareholders equity. Many observers prefer ROE, since equity represents the owners’ interest in the business. As we have all learned anew in the past two years, their equity investment is fully at-risk. Equity holders are paid last, compared to other sources of funds supporting the bank. Shareholders are the last in line if the going gets rough. So, equity capital tends to be the most expensive source of funds, carrying the largest risk premium of all funding options. Its successful deployment is critical to the profit performance, even the survival, of the bank. Indeed, capital deployment, or allocation, is the most important executive decision facing the leadership of any organization. So, why bother with RORAC or RAROC? In short, it is to take risks more fully into the process of risk management within the institution. ROA and ROE are somewhat risk-adjusted, but only on a point-in-time basis and only to the extent risks are already mitigated in the net interest margin and other general ledger numbers. The Net Income figure is risk-adjusted for mitigated (hedged) interest rate risk, for mitigated operational risk (insurance expenses) and for the expected risk within the cost of credit (loan loss provision). The big risk management elements missing in general ledger-based numbers include: market risk embedded in the balance sheet and not mitigated, credit risk costs associated with an economic downturn, unmitigated operational risk, and essentially all of the strategic risk (or business risk) associated with being a banking entity. Most of these risks are summed into a lump called Unexpected Loss (UL). Okay, so I fibbed about no more new acronyms. UL is covered by the Equity account, or the solvency of the bank becomes an issue. RORAC is Net Income divided by Allocated Capital. RORAC doesn’t add much risk-adjustment to the numerator, general ledger Net Income, but it can take into account the risk of unexpected loss. It does this, by moving beyond just book or average Equity, by allocating capital, or equity, differentially to various lines of business and even specific products and clients. This, in turn, makes it possible to move towards risk-based pricing at the relationship management level as well as portfolio risk management.  This equity, or capital, allocation should be based on the relative risk of unexpected loss for the different product groups. So, it’s a big step in the right direction if you want a profitability metric that goes beyond ROE in addressing risk. And, many of us do. RAROC is Risk-Adjusted Net Income divided by Allocated Capital. RAROC does add risk-adjustment to the numerator, general ledger Net Income, by taking into account the unmitigated market risk embedded in an asset or liability. RAROC, like RORAC, also takes into account the risk of unexpected loss by allocating capital, or equity, differentially to various lines of business and even specific products and clients. So, RAROC risk-adjusts both the Net Income in the numerator AND the allocated Equity in the denominator. It is a fully risk-adjusted metric or ratio of profitability and is an ultimate goal of modern risk management. So, RORAC is a big step in the right direction and RAROC would be the full step in management of risk. RORAC can be a useful step towards RAROC. RAROC takes ROE to a fully risk-adjusted metric that can be used at the entity level.  This  can also be broken down for any and all lines of business within the organization. Thence, it can be further broken down to the product level, the client relationship level, and summarized by lender portfolio or various market segments. This kind of measurement is invaluable for a highly leveraged business that is built on managing risk successfully as much as it is on operational or marketing prowess.

Many compliance regulations such the Red Flags Rule, USA Patriot Act, and ESIGN require specific identity elements to be verified and specific high risk conditions to be detected. However, there is still much variance in how individual institutions reconcile referrals generated from the detection of high risk conditions and/or the absence of identity element verification. With this in mind, risk-based authentication, (defined in this context as the “holistic assessment of a consumer and transaction with the end goal of applying the right authentication and decisioning treatment at the right time") offers institutions a viable strategy for balancing the following competing forces and pressures: • Compliance – the need to ensure each transaction is approved only when compliance requirements are met; • Approval rates – the need to meet business goals in the booking of new accounts and the facilitation of existing account transactions; • Risk mitigation – the need to minimize fraud exposure at the account and transaction level. A flexibly-designed risk-based authentication strategy incorporates a robust breadth of data assets, detailed results, granular information, targeted analytics and automated decisioning. This allows an institution to strike a harmonious balance (or at least something close to that) between the needs to remain compliant, while approving the vast majority of applications or customer transactions and, oh yeah, minimizing fraud and credit risk exposure and credit risk modeling. Sole reliance on binary assessment of the presence or absence of high risk conditions and identity element verifications will, more often than not, create an operational process that is overburdened by manual referral queues. There is also an unnecessary proportion of viable consumers unable to be serviced by your business. Use of analytically sound risk assessments and objective and consistent decisioning strategies will provide opportunities to calibrate your process to meet today’s pressures and adjust to tomorrow’s as well.  

The value of a good decision can generate $150 or more in customer net present value, while the cost of a bad decision can cost you $1,000 or more.  For example, acquiring a new and profitable customer by making good prospecting and approval and pricing decisions and decisioning strategies may generate $150 or much more in customer net present value and help you increase net interest margin and other key metrics.  While the cost of a bad decision (such as approving a fraudulent applicant or inappropriately extending credit that ultimately results in a charge-off) can cost you $1,000 or more. Why is risk management decisioning important? This issue is critical because average-sized financial institutions or telecom carriers make as many as eight million customer decisions each year (more than 20,000 per day!).  To add to that, very large financial institutions make as many as 50 billion customer decisions annually.  By optimizing decisions, even a small 10-to-15 percent improvement in the quality of these customer life cycle decisions can generate substantial business benefit. Experian recommends that clients examine the types of decisioning strategies they leverage across the customer life cycle, from prospecting and acquisition, to customer management and collections.  By examining each type of decision, you can identify those opportunities for improvement that will deliver the greatest return on investment by leveraging credit risk attributes, credit risk modeling, predictive analytics and decision-management software.        

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 the VantageScore® credit score* 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. *The VantageScore® credit score model 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.    

Analyzing recent trends from vintage analysis published in the Experian-Oliver Wyman Market Intelligence Reports.

As I wrote in my previous posting, a key Red Flags Rule challenge facing many institutions is one that manages the number of referrals generated from the detection of Red Flags conditions.  The big ticket item in referral generation is the address mismatch condition. Identity Theft Prevention Program I’ve blogged previously on the subject of risk-based authentication and risk-based pricing, so I won’t rehash that information.  What I will suggest, however, is that those institutions who now have an operational Identity Theft Prevention Program (if you don’t, I’d hurry up) should continue to explore the use of alternate data sources, analytics and additional authentication tools (such as knowledge-based authentication) as a way to detect Red Flags conditions and reconcile them all within the same real-time transaction. Referral rates Referral rates stemming from address mismatches (a key component of the Red Flags Rule high risk conditions) can approach or even surpass 30 percent.  That is a lot.  The good news is that there are tools which employ additional data sources beyond a credit profile to “find” that positive address match.  The use of alternate data sources can often clear the majority of these initial mismatches, leaving the remaining transactions for treatment with analytics and knowledge-based authentication and Identity Theft Prevention Program. Whatever “referral management” process you have in place today, I’d suggest exploring risk-based authentication tools that allow you to keep the vast majority of those referrals out of the hands of live agents, and distanced from the need to put your customers through the authentication wringer.  In the current marketplace, there are many services that allow you to avoid high referral costs and risks to customer experience.  Of course, we think ours are pretty good.  

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.  

While the FACT Act’s Red Flags Rule seems to capture all of the headlines these days, it’s just one of a number of compliance challenges that banks, credit unions, and a myriad of other institutions face on a daily basis.  And meeting today’s regulatory requirements is more complicated than ever.  Risk managers and compliance officers are asked to consider many questions, including: 1. Do FACTA Sections 114 and 315 apply to me? 2. What do I have to do to comply? 3. What impact does this have on the customer’s experience? 4. What is this going to cost me in terms of people and process? Interpretation of the law or guideline – including who it applies to and to whom it does not - varies widely.  Which types of businesses are subject to the Red Flags Rule?  What is a “covered account?”  If you’re not sure, you’re not alone - it’s a primary reason why the Federal Trade Commission (FTC) continues to postpone enforcement of the rule, while this healthy debate continues. And by the way, FTC – it’s almost November 1st…aren’t we about due for another delay? But we’re not talking about just protecting consumers from identity theft and reducing fraud and protecting themselves using the Identity Theft Prevention Program. The USA Patriot Act and “Know Your Customer” requirements have been around much longer, but there are current challenges of interpretation and practical application when it comes to identifying customers and performing due diligence to deter fraud and money laundering.  Since Customer Identification Programs require procedures based on the bank’s own “assessment of the relevant risks,” including types of accounts opened, methods of opening, and even the bank’s “size, location, and customer base,” it’s safe to say that each program will differ slightly – or even greatly. So it’s clear there’s a lack of specificity in the regulations of the Red Flags Rule which cause heartburn for those tasked with compliance…but are there some common themes and requirements across the two?  The short answer is Yes.  In my next post, I’ll talk about the elements in common and how authentication products can play a part in addressing both.  

When reviewing offers for prospective clients, lenders often deal with a significant amount of missing information in assessing the outcomes of lending decisions, such as: Why did a consumer accept an offer with a competitor? What were the differentiating factors between other offers and my offer, i.e. what were their credit score trends? What happened to consumers that we declined? Do they perform as expected or better than anticipated? What were their credit risk models? While lenders can easily understand the implications of the loans they have offered and booked with consumers, they often have little information about two important groups of consumers: 1. Lost leads: consumers to whom they made an offer but did not book 2. Proxy performance: consumers to whom financing was not offered, but where the consumer found financing elsewhere. Performing a lost lead analysis on the applications approved and declined, can provide considerable insight into the outcomes and credit performance of consumers that were not added to the lender’s portfolio. Lost lead analysis can also help answer key questions for each of these groups: How many of these consumers accepted credit elsewhere? What were their credit attributes? What are the credit characteristics of the consumers we're not booking? Were these loans booked by one of my peers or another type of lender? What were the terms and conditions of these offers? What was the performance of the loans booked elsewhere? Who did they choose for loan origination? Within each of these groups, further analysis can be conducted to provide lenders with actionable feedback on the implications of their lending policies, possibly identifying opportunities for changes to better fulfill lending objectives. Some key questions can be answered with this information: Are competitors offering longer repayment terms? Are peers offering lower interest rates to the same consumers? Are peers accepting lower scoring consumers to increase market share? The results of a lost lead analysis can either confirm that the competitive marketplace is behaving in a manner that matches a lender’s perspective.  It can also shine a light into aspects of the market where policy changes may lead to superior results. In both circumstances, the information provided is invaluable in making the best decision in today’s highly-sensitive lending environment.

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.