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Part II: Where are Models Most Needed Now in Mortgages? (Click here if you missed Part I of this post.) By: John Straka A first important question should always be are all of your models, model uses, and model testing strategies, and your non-model processes, sound and optimal for your business?  But in today’s environment, two areas in mortgage stand out where better models and decision systems are most needed now: mortgage servicing and loan-quality assurance.  I will discuss loan-quality assurance in a future installment. Mortgage servicing and loss mitigation are clearly one area where better models and new decision analytics continue to have a seemingly great potential today to add significant new value.  At the risk of oversimplifying, it is possible that a number of the difficulties and frustrations of mortgage servicers (and regulators) and borrowers in recent years may have been lessened through more efficient automated decision tools and optimization strategies.  And because these problems will continue to persist for quite some time, it is certainly not too late to envision and move now towards an improved future state of mortgage servicing, or to continue to advance your existing new strategic direction by adding to enhancements already underway. Much has been written about the difficulties faced by many mortgage servicers who have been overwhelmed by the demands of many more delinquent and defaulted borrowers and very extensive, evolving government involvements in new programs, performance incentives and standards.  A strategic question on the minds of many executives and others in the industry today seems to be, where is all of this going?  Is there a generally viable strategic direction for mortgage servicers that can help them to emerge from their current issues—perhaps similar to the improved data, standards, modeling, and technologies that allowed the mortgage industry in the 1990s to emerge overall quite successfully from the problems of the late 1980s and early 90s? To review briefly, mortgage industry problems of the early 1990s were less severe, of course—but really not dissimilar to the current environment.  There had been a major home-price correction in California, in New England, and in a number of large metro areas elsewhere.  A “low doc” mortgage era (and other issues) had left Citicorp nearly insolvent, for example, and caused other significant losses on top of the losses generated by the home prices.  A major source of most mortgage funding, the Savings & Loan industry, had largely collapsed, with losses having to be resolved by a special government agency. Statistical mortgage credit scoring and automated underwriting resulted from the improved data, standards, modeling, and technologies that allowed the mortgage industry to recover in the 1990s, allowing mortgages to catch up with the previously established use of this decision technology in cards, autos, etc., thus benefiting the mortgage industry with reduced costs and significant gains in efficiency and risk management.  An important question today is, is there a similar “renaissance,” so to speak, now in the offing or at hand for mortgage servicers?  Despite all of the still ongoing problems? Let me offer here a very simple analogy—with a disclaimer that this is only a basic starting viewpoint, an oversimplification, recognizing that mortgage servicing and loss mitigation is extraordinarily complex in its details, and often seems only to grow more complex by the day (with added constraints and uncertainties piling on). The simple analogy is this: consider your loan-level Net Present Value (NPV) or other key objective of loan-level decisions in servicing and loss mitigation to be analogous to the statistically based mortgage default “Score” of automated underwriting for originations in the 1990s.  Viewed in this way, a simple question stemming from the figure below is:  can you reduce costs and satisfy borrowers and performance standards better by automating and focusing your servicing representatives more, or primarily, on the “Refer” group of borrowers?  A corollary question is can more automated model-based decision engines confidently reduce the costs and achieve added insights and efficiencies in servicing the lowest and highest NPV delinquent borrowers and the Refer range?  Another corollary question is, are new government-driven performance standards helpful or hindering (or even preventing) particular moves toward this type of objective. Is this a generally viable strategic direction for the future (or even the present) of mortgage servicing?  Is it your direction today?  What is your vision for the future of your quality mortgage servicing?

By: Joel Pruis One might consider this topic redundant to the last submission around application requirements and that assessment would be partially true.  As such we are not going to go over the data that has already been collected in the application such as the demographic information of the applicant and guarantors or the business financial information or personal financial information.  That discussion like Elvis has “left the building”. Rather, we will discuss the use of additional data to support the underwriting/decisioning process - namely: Personal/Consumer credit data Business data Scorecards Fraud data Let’s get a given out in the open.  Personal credit data has a high correlation to the payment performance of a small business.  The smaller the business the higher the correlation. “Your honor, counsel requests the above be stipulated in the court records.” “So stipulated for the record.” “Thank you, your honor.” With that put to rest (remember you can always comment on the blog if you have any questions or want to comment on any of the content). The real debate in small business lending revolves around the use of business data. Depth and availability of business data There are some challenges with the gathering and dissemination of business data for use in decisioning - mainly around the history of the data for the individual entity.  More specifically, while a consumer is a single entity and for the vast majority of consumers, one does not bankrupt one entity and then start a new person to refresh their credit history.  No, that is actually bankruptcy and the bankruptcy stays with the individual. Businesses, however, can and in fact do close one entity and start up another.  Restaurants and general contractors come to mind as two examples of individuals who will start up a business, go bankrupt and then start another business under a new entity repeating the cycle multiple times.  While this scenario is a challenge, one cannot refute the need to know how both the individual consumer as well as the individual business is handling its obligations whether they are credit cards, auto loans or trade payables. I once worked for a bank president in a small community bank who challenged me with the following mantra, “It’s not what you know that you don’t know that can hurt you, it is what you think you know but really don’t that hurts you the most.”  I will admit that it took me a while to digest that statement when I first heard it.  Once fully digested the statement was quite insightful.  How many times do we think we know something when we really don’t?  How many times do we act on an assumed understanding but find that our understanding was flawed?  How sound was our decision when we had the flawed understanding?  The same holds true as it relates to the use (or lack thereof) of business information.  We assume that we don’t need business information because it will not tell us much as it relates to our underwriting.  How can the business data be relevant to our underwriting when we know that the business performance is highly correlated to the performance of the owner? Let’s look at a study done a couple of years ago by the Business Information group at Experian.  The data comes from a whitepaper titled “Predicting Risk: the relationship between business and consumer scores” and was published in 2008.  The purpose of the study was to determine which goes bad first, the business or the owner.  At a high level the data shows the following:                 If you're interested, you can download the full study here. So while a majority of time and without any additional segmentation, the business will show signs of stress before the owner. If we look at the data using length of time in business we see some additional insights.               Figure: Distribution of businesses by years in business Interesting distinction is that based upon the age of the business we will see the owner going bad before the business if the business age is 5 years or less.  Once we get beyond the 5 year point the “first bad” moves to the business. In either case, there is no clear case to be made to exclude one data source in favor of the other to predict risk in a small business origination process.  While we can look at see that there is an overall majority where the business goes bad first or that if we have a young small business the owner will more likely go bad first, in either case, there is still a significant population where the inverse is true. Bottom line, gathering both the business and the consumer data allows the financial institution to make a better and more informed decision.  In other words, it prevents us from the damage caused by “thinking we know something when we really don’t”. Coming up next month – Decisioning Strategies. 

Part I: Types and Complexity of Models, and Unobservable or Omitted Variables or Relationships By: John Straka Since the financial crisis, it’s not unusual to read articles here and there about the “failure of models.” For example, a recent piece in Scientific American critiqued financial model “calibration,” proclaiming in its title, Why Economic Models are Always Wrong. In the mortgage business, for example, it is important to understand where models have continued to work, as well as where they failed, and what this all means for the future of your servicing and origination business. I also see examples of loose understanding about best practices in relation to the shortcomings of models that do work, and also about the comparative strengths and weaknesses of alternative judgmental decision processes.  With their automation efficiencies, consistency, valuable added insights, and testability for reliability and robustness, statistical business models driven by extensive and growing data remain all around us today, and they are continuing to expand.  So regardless of your views on the values and uses of models, it is important to have a clear view and sound strategies in model usage. A Categorization: Ten Types of Models Business models used by financial institutions can be placed in more than ten categories, of course, but here are ten prominent general types of models: Statistical credit scoring models (typically for default) Consumer- or borrower-response models Consumer- or borrower-characteristic prediction models Loss given default (LGD) and Exposure at default (EAD) models Optimization tools (these are not models, per se, but mathematical algorithms that often use inputs from models) Loss forecasting and simulation models and Value-at-risk (VAR) models Valuation, option pricing, and risk-based pricing models Profitability forecasting and enterprise-cash-flow projection models Macroeconomic forecasting models Financial-risk models that model complex financial instruments and interactions Types 8, 9 and 10, for example, are often built up from multiple component models, and for this reason and others, these model categories are not mutually exclusive.  Types 1 through 3, for example, can also be built from individual-level data (typical) or group-level data.  No categorical type listing of models is perfect, and this listing is also not intended to be completely exhaustive. The Strain of Complexity (or Model Ambition) The principle of Occam’s razor in model building, roughly translated, parallels the business dictum to “keep it simple, stupid.”  Indeed, the general ordering of model types 1 through 10 above (you can quibble on the details) tends to correspond to growing complexity, or growing model ambition. Model types 1 and 2 typically forecast a rank-ordering, for example, rather than also forecasting a level.  Credit scores and credit scoring typically seek to rank-order consumers in their default, loss, or other likelihoods, without attempting to project the actual level of default rates, for example, across the score distribution.  Scoring models that add the dimension of level prediction increase this layer of complexity. In addition, model types 1 through 3 are generally unconditional predictors.  They make no attempt to add the dimension of predicting the time path of the dependent variable.  Predicting not just a consumer’s relative likelihood of an event over a future time period as a whole, for example, but also the event’s frequency level and time path of this level each year, quarter, or month, is a more complex and ambitious modeling endeavor.  (This problem is generally approached through continuous or discrete hazard models.) While generalizations can be hazardous (exceptions can typically be found), it is generally true that, in the events leading up to and surrounding the financial crisis, greater model complexity and ambition was correlated with greater model failure.  For example, at what is perhaps an extreme, Coval, Jurek, and Stafford (2009) have demonstrated how, for model type 10, even slight unexpected changes in default probabilities and correlations had a substantial impact on the expected payoffs and ratings of typical collateralized debt obligations (CDOs) with subprime residential mortgage-backed securities as their underlying assets.  Nonlinear relationships in complex systems can generate extreme unreliability of system predictions. To a lesser but still significant degree, the mortgage- or housing-related models included or embedded in types 6 through 10 were heavily dependent on home-price projections and risk simulation, which caused significant “expected”-model failures after 2006.  Home-price declines in 2007-2009 reached what had previously only been simulated as extreme and very unlikely stress paths.  Despite this clear problem, given the inescapable large impact of home prices on any mortgage model or decision system (of any kind), it is generally acceptable to separate the failure of the home-price projection from any failure of the relative default and other model relationships built around the possible home-price paths.  In other words, if a model of type 8, for example, predicted the actual profitability and enterprise cash flow quite well given the actual extreme path of home prices, then this model can be reasonably regarded as not having failed as a model per se, despite the clear, but inescapable reliance of the model’s level projections on the uncertain home-price outcomes. Models of type 1, statistical credit scoring models, generally continued to work well or reasonably well both in the years preceding and during the home-price meltdown and financial crisis.  This is very largely due to these models’ relatively modest objective of simply rank-ordering risks, in general.  To be sure, scoring models in mortgage, and more generally, were strongly impacted by the home price declines and unusual events of the bubble and subsequent recession, with deteriorated strength in risk separation.  This can be seen, for example, in the recent VantageScore® credit score stress-test study, VantageScore® Stress Testing, which shows the lowest risk separation ability in the states with the worst home-price and unemployment outcomes (CA, AZ, FL, NV, MI).  But these kinds of significant but comparatively modest magnitudes of deterioration were neither debilitating nor permanent for these models.   In short, even in mortgage, scoring models generally held up pretty well, even through the crisis—not perfectly, but comparatively better than the more complex level-, system-, and path-prediction models. (see footnote 1) Scoring models have also relied more exclusively on microeconomic behavioral stabilities, rather than including macroeconomic risk modeling.  Fortunately the microeconomic behavioral patterns have generally been much more stable.  Weak-credit borrowers, for example, have long tended to default at significantly higher rates than strong credit borrowers—they did so preceding, and right through, the financial crisis, even as overall default levels changed dramatically; and they continue to do so today, in both strong and weak housing markets. (see footnote 2) As a general rule overall, the more complex and ambitious the model, the more complex are the many questions that have to be asked concerning what could go wrong in model risks.  But relative complexity is certainly not the only type of model risk.  Sometimes relative simplicity, otherwise typically desirable, can go in a wrong direction. Unobservable or Omitted Variables or Relationships No model can be perfect, for many reasons.  Important determining variables may be unmeasured or unknown.  Similarly, important parameters and relationships may differ significantly across different types of populations, and different time periods.  How many models have been routinely “stress tested” on their robustness in handling different types of borrower populations (where unobserved variables tend to lurk) or different shifts in the mix of borrower sub-populations?  This issue is more or less relevant depending on the business and statistical problem at hand, but overall, modeling practice has tended more often than not to neglect robustness testing (i.e., tests of validity and model power beyond validation samples). Several related examples from the last decade appeared in models that were used to help evaluate subprime loans.  These models used generic credit scores together with LTV, and perhaps a few other variables (or not), to predict subprime mortgage default risks in the years preceding the market meltdown.  This was a hazardous extension of relatively simple model structures that worked better for prime mortgages (but had also previously been extended there).  Because, for example, the large majority of subprime borrowers had weak credit records, generic credit scores did not help nearly as much to separate risk.  Detailed credit attributes, for example, were needed to help better predict the default risks in subprime.  Many pre-crisis subprime models of this kind were thus simplified but overly so, as they began with important omitted variables. This was not the only omitted-variables problem in this case, and not the only problem.  Other observable mortgage risk factors were oddly absent in some models.  Unobserved credit risk factors also tend to be correlated with observed risk factors, creating greater volatility and unexplained levels of higher risk in observed higher-credit-risk populations.  Traditional subprime mortgages also focused mainly on poor-credit borrowers who needed cashout refinancing for debt consolidation or some other purpose.  Such borrowers, in shaky financial condition, were more vulnerable to economic shocks, but a debt consolidating cashout mortgage could put them in a better position, with lower total monthly debt payments that were tax deductible.  So far, so good—but an omitted capacity-risk variable was the number of previous cashout refinancings done (which loan brokers were incented to “churn”).  The housing bubble allowed weak-capacity borrowers to sustain themselves through more extracted home equity, until the music stopped.  Rate and fee structures of many subprime loans further heightened capacity risks.  A significant population shift also occurred when subprime mortgage lenders significantly raised their allowed LTVs and added many more shaky purchase-money borrowers last decade; previously targeted affordable-housing programs from the banks and conforming-loan space had instead generally required stronger credit histories and capacity.  Significant shifts like this in any modeled population require very extensive model robustness testing and scrutiny.  But instead, projected subprime-pool losses from the major purchasers of subprime loans, and the ratings agencies, went down in the years just prior to the home-price meltdown, not up (to levels well below those seen in widely available private-label subprime pool losses from 1990’s loans). Rules and Tradition in Lieu of Sound Modeling Interestingly, however, these errant subprime models were not models that came into use in lender underwriting and automated underwriting systems for subprime—the front-end suppliers of new loans for private-label subprime mortgage-backed securities.  Unlike the conforming-loan space, where automated underwriting using statistical mortgage credit scoring models grew dramatically in the 1990s, underwriting in subprime, including automated underwriting, remained largely based on traditional rules. These rules were not bad at rank-ordering the default risks, as traditional classifications of subprime A-, B, C and D loans showed.  However, the rules did not adapt well to changing borrower populations and growing home-price risks either.  Generic credit scores improved for most subprime borrowers last decade as they were buoyed by the general housing boom and economic growth.  As a result, subprime-lender-rated C and D loans largely disappeared and the A- risk classifications grew substantially. Moreover, in those few cases where statistical credit scoring models were estimated on subprime loans, they identified and separated the risks within subprime much better than the traditional underwriting rules.  (I authored an invited article early last decade, which included a graph, p. 222, that demonstrated this, Journal of Housing Research.)  But statistical credit scoring models were scarcely or never used in most subprime mortgage lending. In Part II, I’ll discuss where models are most needed now in mortgages. Footnotes: [1] While credit scoring models performed better than most others, modelers can certainly do more to improve and learn from the performance declines at the height of the home-price meltdown.  Various approaches have been undertaken to seek such improvements. [2] Even strategic mortgage defaults, while comprising a relatively larger share of strong-credit borrower defaults, have not significantly changed the traditional rank-ordering, as strategic defaults occur across the credit spectrum (weaker credit histories include borrowers with high income and assets).  

By: Staci Baker Just before the holidays, the Fed released proposed rules, which implement Sections 165 and 166 of the Dodd-Frank Act. According to The American Bankers Association, “The proposals cover such issues as risk-based capital requirements, leverage, resolution planning, concentration limits and the Fed’s plans to regulate large, interconnected financial institutions and nonbanks.” How will these rules affect you? One of the biggest concerns that I have been hearing from institutions is the affect that the proposed rules will have on profitability. Greater liquidity requirements, created by both the Dodd-Frank Act and Basel III Rules, put pressure on banks to re-evaluate which lending segments they will continue to participate in, as well as impact the funds available for lending to consumers.   What are you doing to proactively combat this? Within the Dodd-Frank Act is the Durbin Amendment, which regulates the interchange fee an issuer can charge a consumer. As I noted in my prior blog detailing the fee cap associated with the Durbin Amendment, it’s clear that these new regulations in combination with previous rulings will continue to put downward pressures on bank profitability. With all of this to consider, how will banks modify their business models to maintain a healthy bottom line, while keeping customers happy? Over my next few blog posts, I will take a look at the Dodd-Frank Act’s affect on an institution’s profitability and highlight best practices to manage the impact to your organization.

By: Staci Baker Just before the holidays, the Fed released proposed rules, which implement Sections 165 and 166 of the Dodd-Frank Act. According to The American Bankers Association, “The proposals cover such issues as risk-based capital requirements, leverage, resolution planning, concentration limits and the Fed’s plans to regulate large, interconnected financial institutions and nonbanks.” How will these rules affect you? One of the biggest concerns that I have been hearing from institutions is the affect that the proposed rules will have on profitability. Greater liquidity requirements, created by both the Dodd-Frank Act and Basel III Rules, put pressure on banks to re-evaluate which lending segments they will continue to participate in, as well as impact the funds available for lending to consumers.   What are you doing to proactively combat this? Within the Dodd-Frank Act is the Durbin Amendment, which regulates the interchange fee merchants are charged. As I noted in my prior blog detailing the fee cap associated with the Durbin Amendment, it’s clear that these new regulations in combination with previous rulings will continue to put downward pressures on bank profitability. With all of this to consider, how will banks modify their business models to maintain a healthy bottom line, while keeping customers happy? Over my next few blog posts, I will take a look at the Dodd-Frank Act’s affect on an institution’s profitability and highlight best practices to manage the impact to your organization.

By: Joel Pruis Small Business Application Requirements The debate on what constitutes a small business application is probably second only to the ongoing debate around centralized vs. decentralized loan authority (but we will get to that topic in a couple of blogs later). We have a couple of topics that need to be considered in this discussion, namely: 1.     When is an application an application? 2.     Do you process an incomplete application? When is an application an application? Any request by a small business with annual sales of $1,000,000 or less falls under Reg B.  As we all know because of this regulation we have to maintain proper records of when we received an application and when a decision on the application was made as well as communicated to the client. To keep yourself out of trouble, I recommend that there be a small business application form (paper or electronic) and that you have clearly stated the information required for a completed application in your small business application procedures. The form removes ambiguities in the application process and helps with the compliance documentation. One thing is for certain – when you request a personal credit bureau on the small business owner(s)/guarantor(s) and you currently do not have any credit exposure to the individual(s) – you have received an application and to this there is no debate. Bottom line is that you need to define your application and do so using objective criteria. Subjective criteria leaves room for interpretation and individual interpretation leaves doubt in the compliance area. Information requirements Whether or not you use a generic or custom small business scorecard or no scorecard at all, there are some baseline data segments that are important to collect on the small business applicant: ·         Requested amount and purpose for the funds ·         Collateral (if necessary based upon the product terms and conditions) ·         General demographics on the business o    Name and location o    Business Entity type (corporation, llc, partnership, etc.) o    Product and/or service provided o    Length of time in business o    Current banking relationship ·         General demographics on the owners/guarantors o    Names and addresses o    Current banking relationship o    Length of time with the business ·         External data reports on the business and/or guarantors o    Business Report o    Personal Credit Bureau on the owners/guarantors ·         Financial Statements (?) – we’ll talk about that in part II of this post. The demographics and the existing banking relationship are likely not causing any issues with anyone and the requested amount and use of funds is elementary to the process. Probably the greatest debate is around the collection of financial information and we are going to save that debate for the next post. The non-financial information noted above provides sufficient data to pull personal credit bureaus on the owners/guarantors and the business bureau on the actual borrower. We have even noted some additional data informing us the length of time the business has been in existence and where the banking relationship is currently held for both the business and the owners. But what additional information should be requested or should I say required? We have to remember that the application is not only to support the ability to render a decision but also supports the ability to document the loan and maybe even serve as a portion of the loan documentation.  We need to consider the following: ·         How standardized are the products we offer? ·         Do we allow for customization of collateral to be offered? ·         Do we have standard loan/fee pricing? ·         Is automatic debit for the loan payments required? Optional? Not available? ·         Are personal guarantees required? Optional? We again go back to the 80/20 rule. Product standardization is beneficial and optimal when we have high volumes and low dollars. The smaller the dollar size of the request/relationship the more standardized we need to have our products and as a result our application can be more streamlined. When we do not negotiate rate, we do not need to have a space to note requested rate. When we do not negotiate on personal guarantees we always require the personal financial information be collected on all owners of the business (some exceptions for very small ownership interests). Auto-debit for the loan payments means we always need to have some form of a DDA account with our institution. I think you get the point that for the highest volume of applications we standardize and thus streamline the process through the removal of ambiguity. Do you process an incomplete application? The most common argument for processing an incomplete application is that if we know we are going to decline the application based upon information on the personal credit bureau, why go through the effort of collecting and spreading the financial information. Two significant factors make this argument moot:   customer satisfaction and fair lending regulation. Customer satisfaction This is based upon the ease of doing business with the financial institution. More specifically the number of contact points or information requests that are required during the process. Ideally the number of contact points that are required once the applicant has decided to make a financing request should be minimal the information requirements clearly communicated up front and fully collected prior to rendering a decision. The idea that a quick no is preferable to submitting a full application actually is working to make the declination process more efficient than the actual approval process. So in other words we are making the process more efficient and palatable for those clients we do NOT consider acceptable versus those clients that ARE acceptable. Secondly, if we accept and process incomplete applications, we are actually mis-prioritizing the application volume. Incomplete applications should never be processed ahead of completed packages yet under the quick no objective, the incomplete application is processed ahead of completed applications simply based upon date and time of submission. Consequently we are actually incenting and fostering the submission of incomplete applications by our lenders. Bluntly this is a backward approach that only serves to make the life of the relationship manager more efficient and not the client. Fair lending regulation This perspective poses a potential issue when it comes to consistency.  In my 10 years working with hundreds of financial institutions, only a very small minority of times have I encountered a financial institution that is willing to state with absolute certainty that a particular characteristic will cause an application to e declined 100% of the time. As a result, I wish to present this scenario: ·         Applicant A provides an incomplete application (missing financial statements, for example). o    Application is processed in an incomplete status with personal and business bureaus pulled. o    Personal credit bureau has blemishes which causes the financial institution to decline the application o    Process is complete ·         Applicant B provides a completed application package with financial statements o    Application is processed with personal and business bureaus pulled, financial statements spread and analysis performed o    Personal credit bureau has the same blemishes as Applicant A o    Financial performance prompts the underwriter or lender to pursue an explanation of why the blemishes occurred and the response is acceptable to the lender/underwriter. Assuming Applicant A had similar financial performance, we have a case of inconsistency due to a portion of the information that we “state” is required for an application to be complete yet was not received prior to rendering the decision. Bottom line the approach causes doubt with respect to inconsistent treatment and we need to avoid any potential doubt in the minds of our regulators. Let’s go back to the question of financial statements. Check back Thursday for my follow-up post, or part II, where we’ll cover the topic in greater detail.

By: Joel Pruis The debate on what constitutes a small business application is probably second only to the ongoing debate around centralized vs. decentralized loan authority (but we will get to that topic in a couple of blogs later).  We have a couple of topics that need to be considered in this discussion, namely:      1.      When is an application an application?      2.     Do you process an incomplete application? When is an application an application? Any request by a small business with annual sales of $1,000,000 or less falls under Reg B.  As we all know because of this regulation we have to maintain proper records of when we received an application and when a decision on the application was made as well as communicated to the client.  To keep yourself out of trouble, I recommend that there be a small business application form (paper or electronic) and that you have clearly stated the information required for a completed application in your small business application procedures.  The form removes ambiguities in the application process and helps with the compliance documentation. One thing is for certain – when you request a personal credit bureau on the small business owner(s)/guarantor(s) and you currently do not have any credit exposure to the individual(s) – you have received an application and to this there is no debate. Bottom line is that you need to define your application and do so using objective criteria.  Subjective criteria leaves room for interpretation and individual interpretation leaves doubt in the compliance area. Information requirements Whether or not you use a generic or custom small business scorecard or no scorecard at all, there are some baseline data segments that are important to collect on the small business applicant: Requested amount and purpose for the funds Collateral (if necessary based upon the product terms and conditions) General demographics on the business Name and location Business Entity type (corporation, llc, partnership, etc.) Product and/or service provided Length of time in business Current banking relationship General demographics on the owners/guarantors Names and addresses Current banking relationship Length of time with the business External data reports on the business and/or guarantors Business Report Personal Credit Bureau on the owners/guarantors Financial Statements (??) – we’ll talk about that in part II of this post. The demographics and the existing banking relationship are likely not causing any issues with anyone and the requested amount and use of funds is elementary to the process.  Probably the greatest debate is around the collection of financial information and we are going to save that debate for the next post. The non-financial information noted above provides sufficient data to pull personal credit bureaus on the owners/guarantors and the business bureau on the actual borrower.  We have even noted some additional data informing us the length of time the business has been in existence and where the banking relationship is currently held for both the business and the owners.  But what additional information should be requested or should I say required? We have to remember that the application is not only to support the ability to render a decision but also supports the ability to document the loan and maybe even serve as a portion of the loan documentation.  We need to consider the following: How standardized are the products we offer? Do we allow for customization of collateral to be offered? Do we have standard loan/fee pricing? Is automatic debit for the loan payments required?  Optional? Not available? Are personal guarantees required?  Optional? We again go back to the 80/20 rule.  Product standardization is beneficial and optimal when we have high volumes and low dollars.  The smaller the dollar size of the request/relationship the more standardized we need to have our products and as a result our application can be more streamlined.  When we do not negotiate rate, we do not need to have a space to note requested rate.  When we do not negotiate on personal guarantees we always require the personal financial information be collected on all owners of the business (some exceptions for very small ownership interests).  Auto-debit for the loan payments means we always need to have some form of a DDA account with our institution.  I think you get the point that for the highest volume of applications we standardize and thus streamline the process through the removal of ambiguity. Do you process an incomplete application? The most common argument for processing an incomplete application is that if we know we are going to decline the application based upon information on the personal credit bureau, why go through the effort of collecting and spreading the financial information.  Two significant factors make this argument moot: customer satisfaction and fair lending regulation. Customer satisfaction This is based upon the ease of doing business with the financial institution.  More specifically the number of contact points or information requests that are required during the process.  Ideally the number of contact points that are required once the applicant has decided to make a financing request should be minimal the information requirements clearly communicated up front and fully collected prior to rendering a decision.  The idea that a quick no is preferable to submitting a full application actually is working to make the declination process more efficient than the actual approval process.  So in other words we are making the process more efficient and palatable for those clients we do NOT consider acceptable versus those clients that ARE acceptable.  Secondly, if we accept and process incomplete applications, we are actually mis-prioritizing the application volume.  Incomplete applications should never be processed ahead of completed packages yet under the quick no objective, the incomplete application is processed ahead of completed applications simply based upon date and time of submission.  Consequently we are actually incenting and fostering the submission of incomplete applications by our lenders.  Bluntly this is a backward approach that only serves to make the life of the relationship manager more efficient and not the client. Fair lending regulation This perspective poses a potential issue when it comes to consistency.  In my 10 years working with hundreds of financial institutions, only a very small minority of times have I encountered a financial institution that is willing to state with absolute certainty that a particular characteristic will cause an application to e declined 100% of the time.  As a result, I wish to present this scenario: Applicant A provides an incomplete application (missing financial statements, for example).  {C}Application is processed in an incomplete status with personal and business bureaus pulled. Personal credit bureau has blemishes which causes the financial institution to decline the application Process is complete Applicant B provides a completed application package with financial statements Application is processed with personal and business bureaus pulled, financial statements spread and analysis performed Personal credit bureau has the same blemishes as Applicant A Financial performance prompts the underwriter or lender to pursue an explanation of why the blemishes occurred and the response is acceptable to the lender/underwriter. Assuming Applicant A had similar financial performance, we have a case of inconsistency due to a portion of the information that we “state” is required for an application to be complete yet was not received prior to rendering the decision.  Bottom line the approach causes doubt with respect to inconsistent treatment and we need to avoid any potential doubt in the minds of our regulators. Let’s go back to the question of financial statements.  Check back Thursday for my follow-up post, or part II, where we’ll cover the topic in greater detail. 

Within the world of cyber security, a great deal of attention has been focused lately on the escalating hazards and frequency of data breaches, with considerable discussion on the high cost of such breaches.  But as the industry has assessed the financial toll of breaches, it has never taken into account how data breaches harm reputations, brand image, and consequently a company's bottom line. Until now. A recently released Ponemon Institute study, sponsored by Experian’s Data Breach Resolution and believed to be the first of its kind, explores the “Reputation Impact of a Data Breach” to provide more context for the full scope of data breaches.  The findings draw enlightening conclusions around the financial toll that data breaches wreak upon harmed corporate reputations, including these key takeaways: Reputation is one of an organization’s most important and valuable assets. Reputation and brand image are perceived as very valuable…and highly vulnerable to negative events, including a data breach. Calculating the value of reputation and brand reveals how valuable these assets are to an organization. The average value of brand and reputation for the study’s participating organizations was determined to be approximately $1.5 billion.  Depending upon the type of information lost as a result of the breach, the average loss in the value of the brand ranged from $184 million to more than $330 million. Depending upon the type of breach, the value of brand and reputation could decline as much as 17 percent to 31 percent. Not all data breaches are equal. Some breaches are more devastating than others to an organization’s reputation and brand image, with the loss or theft of customer information ranked as the most devastating (followed by confidential financial business information and confidential non-financial business information). Data breaches occur in most organizations represented in this study and have at least a moderate or a significant impact on reputation and brand image. According to 82 percent of respondents, their organizations had a data breach involving sensitive or confidential information.  Fifty-three percent say the data breaches had a moderate impact on reputation and brand image and 23 percent say it was significant. Most organizations in the study have had a data breach involving the theft of sensitive or confidential business information. On average these types of breaches have occurred 2.9 times in surveyed organizations, with the theft or loss of confidential financial information having the most significant impact on reputation and brand. Respondents strongly believe in understanding the root cause of the breach and protecting victims from identity theft. When asked what their organizations did following a breach to preserve or restore brand and reputation, the top three steps are: conduct investigations and forensics, work closely with law enforcement and protect those affected from potential harms such as identity theft. The Ponemon study clearly shows that when data breaches occur, the collateral damage of a company’s brand and reputation become significant hard costs that must be factored into the total financial loss. Download the Ponemon Reputation Impact Study

By: Joel Pruis Basic segmentation strategy for business banking asks the following questions: - Is there a uniform definition of small business across the industry? - How should small business be defined?  Sales size of the applicant?  Exposure to the financial institution? - Is small business/business banking a retail or commercial line of business? No One Size Fits All The notion of a single definition for small business for any financial institution is inappropriate as the intent for segmentation is to focus marketing efforts, establish appropriate products to support the segment, develop appropriate delivery methods and use appropriate risk management practices.  For the purpose of this discussion we will restrict our content to developing the definition of the segment and high level credit product terms and conditions to support the segment. The confusion on how to define the segment is typically due to the multiple sources of such definitions.  The Small Business Administration, developers of generic credit risk scorecards (such as Experian), marketing firms and the like all have multiple ways to define small business.  While they all have a different method of defining small business, the important factor to consider is that each definition serves the purpose of the creator.  As such, the definition of small business should serve the purpose of the specific financial institution. A general rule of thumb is the tried and true 80/20 rule.  Assess your financial institution’s business purpose portfolio by rank ordering individual relationships by total dollar exposure.  Using the 80/20 rule, determine the smallest 80% of the number of relationships by exposure.  Typically the result is that the largest 20% of relationships will cover approximately 80% of the total dollars outstanding in your business purpose portfolio.  Conversely the smallest 80% of relationships will cover only about 20% of the total dollars outstanding. Just from this basic analysis we can see the primary need for segmentation between the business banking and the commercial (middle market, commercial real estate, etc.) portfolios.  Assuming we do not segment we have a significant imbalance of effort vs. actual risk.  Meaning if we treat all credit relationships the same we are spending up to 80% of our time/resources on only 20% of our dollar risk.  Looking at this from the other direction we are only spending 20% of our credit resources assessing 80% of our total dollar risk.  Obviously this is a very basic analysis but any way that you look at it, the risk assessment (underwriting and portfolio management) must be “right-sized” in order to provide the appropriate risk management while working to maximize the return on such portfolio segments. The realities of the credit risk assessment practices without segmentation is that the small business segment will be managed by exception, at best.  Given the large number of relationships and the small impact that the small business segment has on traditional credit quality metrics such as past dues and charge offs, the performance of the small business portfolio can, in fact, be hidden.  Such metrics focus on percentage of dollars that are past due or charged off against the entire portfolio.  Since the largest dollars are in the 20% of relationships, it will take a significant number of individual small business relationships being delinquent or charged off before the overall metric would become alarming. Working with our clients in defining small business, one of the first exercises that we recommend is assessing the actual delinquency and charge off rates in the newly defined small business/business banking portfolio.  Simply put, determine the total dollars that fit the new definition and apply the charge-offs by borrowers that meet the definition that have occurred over the past 12 months divided by total outstanding in the new portfolio segment.  Similarly determine the current dollars past due of relationships meeting the definition of small business divided by the total outstanding of said segment.  Such results typically are quite revealing and will at least provide a baseline for which the financial institution can measure improvement and/or success.  Without such initial analysis, we have witnessed financial institutions laying blame on the new underwriting and portfolio management processes for such performance when it existed all along but was never measured. So basically our first attempt to define the segment has created a total credit exposure limit.  Such limits should be used to determine the appropriate underwriting and portfolio management methods (both of which we will discuss further subsequent blogs), but this type of a definition does little to support a business development effort as the typical small business does not always borrow nor can we accurately assess the potential dollar exposure of any given business until we actually gather additional data.  Thus for business development purposes we establish the definition of small business primarily by sales size.  Looking at the data from your existing relationships, your financial institution can get an accurate indication of the maximum sales size that should be considered in the business development efforts. As a result we have our business development definition by sales size of a given company and our underwriting and portfolio management defined by total exposure.  You may be thinking that such definitions are not always in sync with each other and you would be correct.  You will have some companies with total sales under your definition that borrower more than your total exposure limits while companies with total exposure that falls under small business but the total sales of such companies may exceed the business development limit.  It is impossible to catch every possibility and to do so is an exercise in futility.  Better that you start with the basics of the segmentation and then measure the new applications that exceed the total exposure or the relationships meeting the total exposure cap but exceed the sales limitation.  During the initial phase, judgment on a case by case basis will need to be used. Questions such as: Is the borrower that exceeds our sales limitations likely to need to borrow more in the near future? Is the exposure of the borrower that meets our sales size requirement likely to quickly reduce its exposure to meet our definition? Will our underwriting techniques be adequate to assess the risk of this relationship? Will our portfolio monitoring methods be sufficient to assess the changes in the risk profile after it has been booked? Will the relationship management structure be sufficient to support such a borrower? As you encounter these situations it will become obvious to the financial institution the frequency and consistency of such exceptions to the existing definition and prompt adjustments and/or exclusions.  But to try and create the exclusions before collecting the data or examining the actual application volumes is where the futility lies. Best to avoid the futility and act only on actual data. Further refinement of the segment definition will also be based on the above assessment.  Additional criteria will be added such as: Industry segments (Commercial Real Estate, for example) Product types (construction lending) Just know that the definition will not stay static.  Based upon the average credit request changes from 2006 to 2010, changes can and will be significant.  The following graph represents the average request amounts from 2010 data compared to the dollar amounts from 2006 (noted below the chart). So remember that where you start is not where you have to stay.  Keep measuring, keep adjusting and your segmentation strategy will serve you very well. Look for my next post on generating small business applications.  Specifically I’ll cover who should be involved in the outbound marketing efforts of your small business segment. I look forward to your continued comments, challenges and debate as we continue our discussion around small business/business banking.  And if you're interested, I'm hosting a 3-part Webinar series, Navigating Through The Challenges Affecting Portfolio Performance, that will evaluate how statistics and modeling, combined with strategies from traditional credit management, can create a stronger methodology and protect your bottom line.

By: Mike Horrocks Earlier this week, my wife and I were discussing the dinner plans for Thanksgiving.  The yams, cranberries, and pumpkin pies were purchased and the secret family recipes were pulled out of the cupboard.  Everything was ready…we thought.  Then the topic of the turkey was brought up.  In the buzz of work, family, kids, etc., both of us had forgotten to get the turkey.   We had each thought the other was covering this purchase and had scratched if off our respective lists.  Our Thanksgiving dinner was at risk!  This made me think of what best practices from our industry could be utilized if I was going to mitigate risks and pull off the perfect dinner.  So I pulled the page from the Basel Committee on Banking Supervision that defines operational risk as "the risk of loss resulting from inadequate or failed internal processes, people, systems or external events” and I have some suggestions that I think work for both your Thanksgiving dinner and for your existing loan portfolios. First, let’s cover “inadequate or failed processes”.  Clearly our shopping list process failed.   But how are your portfolio management processes?  Are they clearly documented and can they be implemented throughout the organization?  Your processes should be as well communicated and documented as the “Smashed Yam Bake” recipe or you may be at risk. Next, let focus on the “people and systems”.    People make mistakes – learn from them, correct them, and try to get the “systems” to make it so there are fewer mistakes.  For example, I don’t want the risk of letting the turkey cook too long, so I use a remote meat thermometer.  Ok, it is a little geeky; however the turkey has come out perfect every year.    What systems do you have in place to make your quarterly reviews of the portfolio more consistent and up to your standards?  Lastly, how do I mitigate those “external events”?  Odds are I will be able to still get a turkey tonight.  If not, I talked to a friend of mine who is a chef and I have the plans for a goose.   How flexible are your operations and how accessible are you to the subject matter experts that can get you out of those situations?  A solid risk management program takes into account unforeseen events and can make them into opportunities. So as the Horrocks family gathered in Norman Rockwell like fashion this Thanksgiving, a moment of thanks was given to the folks on the Basel committee.  Likewise in your next risk review, I hope you can give thanks for the minimized losses and mitigated risks.  Otherwise, we will have one thing very much in common…our goose will be cooked.

By: John Straka For many purposes, national home-price averages, MSA figures, or even zip code data cannot adequately gauge local housing markets. The higher the level of the aggregate, the less it reflects the true variety and constant change in prices and conditions across local neighborhood home markets. Financial institutions, investors, and regulators that seek out and learn how to use local housing market data will generally be much closer to true housing markets. When houses are not good substitutes from the viewpoint of most market participants, they are not part of the same housing market.  Different sizes and types and ages of homes, for example, may be in the same county, zip code, block, or even right next door to each other, but they are generally not in the same housing market when they are not good substitutes.  This highlights the importance of starting with detailed granular information on local-neighborhood home markets and homes.  To be sure, greater granularity in neighborhood home-market evaluation requires analysts and modelers to deal with much more data on literally hundreds of thousands of neighborhoods in the U.S. It is fair to ask if zip-code level data, for example, might not be generally sufficient. Most housing analysts and portfolio modelers, in fact, have traditionally assumed this, believing that reasonable insights can be gleaned from zip code, county-level, or even MSA data. But this is fully adequate, strictly speaking, only if neighborhood home markets and outcomes are homogenous—at least reasonably so—within the level of aggregation used. Unfortunately, even at zip-code level, the data suggests otherwise.  Examples All of the home-price and home-valuation data for this report was supplied by Collateral Analytics. I have focused on zip7s, i.e. zip+2s, which are a more granular neighborhood measure than zip codes. A Hodrick-Prescott (H-P) Filter was applied by Collateral Analytics to the raw home-price data in order to attenuate short-term variation and isolate the six-year trends. But as we’ll see this dampening still leaves an unrealistically high range of variation within zip codes, for reasons discussed below. Fortunately there is an easy way to control for this, which we’ll apply for final estimates of the range of within-zip variation in home-price outcomes.  The three charts below show the H-P filtered 2005-2011 percent changes in home-price per square foot of living area within three different types of zip codes in San Diego county. Within the first type of zip code, 92319 in this case, the home-price changes in recent years have been relatively homogenous, with a range of -56% to -40% home-price change across the zip7s (i.e., zip+2s) in 92319. But the second type of zip code, illustrated by 92078, is more typical. In this type of case the home-price changes across the zip7s have varied much more. The 2055-2011 zip7 %chg in home prices within 92078 have varied by over 40 percentage points, from -51% to -10%. In the third type of zip code, less frequent but surprisingly common, the home-price changes across the zip7s have had a truly remarkable range of variation. This is illustrated here by zip code 92024 in which the home price outcomes have varied from -51% to +21%, or a 71 percentage point range of difference—and this is not the zip code with the maximum range of variation observed! All of the San Diego County zip codes are summarized in the bar chart below. Nearly two-thirds of the zip codes, 65%, have more than 30 percentage points within-zip difference in the 2005-2011 zip7 %changes in home prices. 40% have more than a 40 percentage point range of different home-price outcomes, 23% have more than a 50 percentage point range, and 13% have more than a 70 percentage point range of differences. The average range of the zip7 within-zip code differences is a 37 percentage point median, 41 percentage-point mean. These high numbers are surprising, and are most likely unrealistically high. Summary of Within-Zip (Zip+2 level) Ranges of Variation in Home-Price Changes in San Diego: Percentage of Zips by Range Across Zip+2s in Home Price/Living Area %Change 2005-2011 Controlling for Factors Inflating the Range of Variation Such sizable differences within a typical single zip code clearly suggest materially different neighborhood home markets. While this qualitative conclusion is supported further below, the magnitudes of the within-zip variation in home-price changes shown above are quite likely inflated. There is a tendency for a limited number of observations in various zip7s to create statistical “noise” outliers, and the inclusion of distressed property sales here can create further outliers, with cases of both limited observations and distress sales particularly capable of creating more negative outliers that are not representative of the true price changes for most homes and their true range of variation within zip codes.  (My earlier blog on June 29th discussed the biases from including distressed property sales while trying to gauge general price trends for most properties.) Fortunately, I’ve been able to access a very convenient way to control for these factors by using the zip7 averages of Collateral Analytics’ AVM (Automated Valuation Model) values rather than simply the home price data summarized above. These industry-leading AVM home valuations have been designed, in part, to filter out statistical noise problems.  The bar chart below shows the still significant zip7 ranges within San Diego County zip codes using the AVM values, but the distribution is now shifted considerably, and more realistically, to a much smaller share of the zip codes with remarkably high zip7 variation. Compared with the chart above, now just 1% of the zips have a zip7 range greater than 60 percentage points, 5% greater than 50, and 11% greater than 40, but there are still 36% greater than 30. To be sure, this distribution, and the average range of zip7 differences—which is now a 25 percentage-point median, 26 percent age-point mean—do show a considerable range of local home market variation within zip codes. It seems fair to conclude that the typical zip code does not contain the uniformity in home price outcomes that most housing analysts and modelers have tended to simply assume. The difference between the effects on consumer wealth and behavior of a 10% home price decline, for example, vs. a 35 to 50% decline, would seem to be sizable in most cases. This kind of difference within a zip code is not at all unusual in these data. How About a Different Type of Urban Area—More Uniform? It might be thought that the diversity of topography, etc., across San Diego County (from the sea to the mountains) makes its variation of home market outcomes within zip codes unusually high. To take a quick gauge of this hypothesis, let’s look at a more topographically uniform urban area: Columbus, Ohio. When I informally polled some of my colleagues asking what their prior belief would be about the within-zip code variation in home price outcomes in Columbus vs. San Diego County, there was unanimous agreement with my prior belief. We all expected greater within-zip uniformity in Columbus. I find it interesting to report here that we were wrong. Both the H-P filtered raw home-price information and the AVM values from Collateral Analytics show relatively greater zip7 variation within Columbus (Franklin County) zip codes than in San Diego County.  The bar chart below shows the best-filtered, most attenuated results,  the AVM values. 5% of the Columbus zips have a zip7 range greater than 70 percentage points, 8% greater than 60, 23% greater than 50, 35% greater than 40, and 65% greater than 30. The average range of zip7 within-zip code differences in Columbus is a 35 percentage point median, 38 percentage-point mean. Conclusion These data seem consistent with what experienced appraisers and real estate agents have been trying to tell economists and other housing analysts, investors, and financial institutions and policymakers for quite a long time. Although they have quite reasonable uses for aggregate time-series and forecasting purposes, more aggregate-data based models of housing markets actually miss a lot of the very real and material variation in local neighborhood housing markets.  For home valuation and many other purposes, even models that use data which gets down to the zip code level of aggregation—which most analysts have assumed to be sufficiently disaggregated—are not really good enough. These models are not as good as they can or should be. These facts are indicative of the greater challenge to properly define local housing markets empirically, in such a way that better data, models, and analytics can be more rapidly developed and deployed for greater profitability, and for sooner and more sustainable housing market recoveries. I thank Michael Sklarz for providing the data for this report and for comments, and I thank Stacy Schulman for assistance in this post.

With the most recent guidance newly issued by the Federal Financial Institutions Examination Council (FFIEC) there is renewed conversation about knowledge based authentication. I think this is a good thing.  It brings back into the forefront some of the things we have discussed for a while, like the difference between secret questions and dynamic knowledge based authentication, or the importance of risk based authentication. What does the new FFIEC guidance say about KBA?  Acknowledging that many institutions use challenge questions, the FFIEC guidance highlights that the implementation of challenge questions can greatly impact efficacy of its usefulness. Chances are you already know this.  Of greater importance, though, is the fact that the FFIEC guidelines caution on the use of less sophisticated systems and information that can be easily guessed or obtained from an Internet search, given the amount of information available.    As mentioned above, the FFIEC guidelines call for questions that “do not rely on information that is often publicly available,” recommending instead a broad range of data assets on which to base questions.  This is an area knowledge based authentication users should review carefully.  At this point in time it is perfectly appropriate to ask, “Does my KBA provider rely on data that is publicly sourced”  If you aren’t sure, ask for and review data sources.  At a minimum, you want to look for the following in your KBA provider:     ·         Questions!  Diverse questions from broad data categories, including credit and noncredit assets ·         Consumer question performance as one of the elements within an overall risk-based decisioning policy ·         Robust performance monitoring.  Monitor against established key performance indicators and do it often ·         Create a process to rotate questions and adjust access parameters and velocity limits.  Keep fraudsters guessing! ·         Use the resources that are available to you.  Experian has compiled information that you might find helpful: www.experian.com/ffiec Finally, I think the release of the new FFIEC guidelines may have made some people wonder if this is the end of KBA.  I think the answer is a resounding “No.”  Not only do the FFIEC guidelines support the continued use of knowledge based authentication, recent research suggests that KBA is the authentication tool identified as most effective by consumers.  Where I would draw caution is when research doesn’t distinguish between “secret questions” and dynamic knowledge based authentication, which we all know is very different.   

By: Mike Horrocks Have you ever been struck by a turtle or even better burnt by water skies that were on fire?  If you are like me, these are not accidents that I think will ever happen to me and I'm not concerned that my family doctor didn't do a rotation in medical school to specialize in treating them. On October 1, 2013, however, doctors and hospitals across the U.S. will have ability to identify, log, bill, and track those accidents and thousands of other very specific medical events.  In fact the list will jump from a current 18,000 medical codes to 140,000 medical codes.  Some people hail this as a great step toward the management of all types of medical conditions, whereas others view it as a introduction of noise in a medical system already over burdened.  What does this have to do with credit risk management you ask? When I look at the amount of financial and non-financial data that the credit industry has available to understand the risk of our consumer or business clients, I wonder where we are in the range of “take two aspirins and call me in the morning” to “[the accident] occurred inside a chicken coop” (code: Y9272).   Are we only identifying a risky consumer after they have defaulted on a loan?  Or are we trying to find a pattern in the consumer's purchases at a coffee house that would correlate with some other data point to indicate risk when the moon is full? The answer is somewhere in between and it will be different for each institution.  Let’s start with what is known to be predictable when it comes to monitoring our portfolios - data and analytics, coupled with portfolio risk monitoring to minimize risk exposure - and then expand that over time.  Click here for a recent case study that demonstrates this quite successfully with one of our clients. Next steps could include adding in analytics and/or triggers to identify certain risks more specifically. When it comes to risk, incorporating attributes or a solid set of triggers, for example, that will identify risk early on and can drill down to some of the specific events, combined with technology that streamlines portfolio management processes - whether you have an existing system in place or in search of a migration - will give you better insight to the risk profile of your consumers. Think about where your organization lies on the spectrum.    If you are already monitoring your portfolio with some of these solutions, consider what the next logical step to improve the process is - is it more data, or advanced analytics using that data, a combination of both, or perhaps it's a better system in place to monitoring the risk more closely. Wherever you are, don’t let your institution have the financial equivalent need for these new medical codes W2202XA, W2202XD, and W2202XS (injuries resulting from walking into a lamppost once, twice, and sequentially).

By: Mike Horrocks Let’s all admit it, who would not want to be Warren Buffet for a day?  While soaking in the tub, the “Sage of Omaha” came up with the idea to purchase shares of Bank of America and managed to close the deal in under 24 hours (and also make $357 million in one day thanks to an uptick in the stock). Clearly investor opinions differ when picking investments, so what did Buffet see that was worth taking that large of a risk? In interviews Buffet simply states that he saw the fundamentals of a good bank (once they fix a few things), that will return his investment many times over. He has also said that he came to this conclusion based on years of seeing opportunities where others only see risk. So what does that have to do with risk management? First, ask yourself as you look at your portfolio of customers what ones are you  “short-selling”  and risk losing and what customers are you investing into and expect Buffet-like returns on in the future? Second, ask yourself how are you making that “investment” decision on your customers? And lastly, ask yourself how confident you are in that decision? If you’re not employing some mode of segmentation today on your portfolio stop and make that happen as soon as you are done reading this blog. You know what a good customer looks like or looked like once upon a time. Admit to yourself that not every customer looks as good as they used to before 2008 and while you are not “settling”, be open minded on who you would want as a customer in the future. Amazingly, Buffet did not have Bank of America’s CEO Brian Moynihan’s phone number when he wanted to make the deal. This is where you are heads and shoulders above Garot’s Steak House’s favorite customer.  You have deposit information, loan activity and performance history, credit data, and even the phone number of your customers. This gives you plenty of data and solutions to build that profile of what a good customer looks like – thereby knowing who to invest in. The next part is the hardest. How confident are you in your decision that you will put your money on it? For example, my wife invested in Bank of America the day before Warren put in his $5 billion. She saw some of the same signs that he did in the bank. However, the fact that I am writing this blog is an indicator that she clearly did not invest to the scale that Warren did. But what is stopping you from going all in and investing in your customers’ future? If the fundamentals of your customer segmenting are sound, any investment today into your customers will come back to you in loyalty and profits in the future. So at the risk of conjuring up a mental image, take the last lesson from Warren Buffet’s tub soaking investment process and get up and invest in those perhaps risky today, yet sound tomorrow customers or run the risk of future profits going down the drain.

By: Kari Michel The way medical debts are treated in scores may change with the introduction of June 2011, Medical Debt Responsibility Act. The Medical Debt Responsibility Act would require the three national credit bureaus to expunge medical collection records of $2,500 or less from files within 45 days of their being paid or settled. The bill is co-sponsored by Representative Heath Shuler (D-N.C.), Don Manzullo (R-Ill.) and Ralph M. Hall (R-Texas). As a general rule, expunging predictive information is not in the best interest of consumers or credit granters -- both of which benefit when credit reports and scores are as accurate and predictive as possible. If any type of debt information proven to be predictive is expunged, consumers risk exposure to improper credit products as they may appear to be more financially equipped to handle new debt than they truly are. Medical debts are never taken into consideration by VantageScore® Solutions LLC if the debt reporting is known to be from a medical facility. When a medical debt is outsourced to a third-party collection agency, it is treated the same as other debts that are in collection. Collection accounts of lower than $250, or ones that have been settled, have less impact on a consumer’s VantageScore® credit score. With or without the medical debt in collection information, the VantageScore® credit score model remains highly predictive.