Credit & Risk

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.

In a recent article, www.CNNMoney.com reported that Federal Reserve Chairman, Ben Bernanke, said that the pace of recovery in 2010 would be moderate and added that the unemployment rate would come down quite slowly, due to headwinds on ongoing credit problems and the effort by families to reduce household debt.’ While some media outlets promote an optimistic economic viewpoint, clearly there are signs that significant challenges lie ahead for lenders. As Bernanke forecasts, many issues that have plagued credit markets will sustain themselves in the coming years. Therefore lenders need to be equipped to monitor these continued credit problems if they wish to survive this protracted time of distress. While banks and financial institutions are implementing increasingly sophisticated and thorough processes to monitor fluctuations in credit trends, they have little intelligence to compare their credit performance to that of their peers.  Lenders frequently cite that they are concerned about their lack of awareness or intelligence regarding the credit performance and status of their peers.  Marketing intelligence solutions are important for management of risk, loan portfolio monitoring and related decisioning strategies. Currently, many vendors offer data on industry-wide trends, but few vendors provide the information needed to allow a lender to understand its position relative to a well-defined group of firms that it considers its peers. As a result, too many lenders are performing benchmarking using data sources that are biased, incomplete, inaccurate, or that lack the detail necessary to derive meaningful conclusions. If you were going to measure yourself personally against a group to understand your comparative performance, why would you perform that comparison against people who had little or nothing in common with you? Does an elite runner measure himself against a weekend warrior to gauge his performance? No; he segments the runners by gender, age, and performance class to understand exactly how he stacks up. Today’s lending environment is not forgiving enough for lenders to make broad industry comparisons if they want to ensure long-term success. Lenders cannot presume they are leading the pack, when, in fact, the race is closer than ever.  

Vintage analysis, specifically vintage pools, present numerous useful opportunities to further understand the risks within specific portfolios.

-- by Heather Grover I’m often asked in various industry forums to give talks about, or opinions on, the latest fraud trends and fraud best practices. Let’s face it –  fraudsters are students of their craft and continue to study the latest defenses and adapt to controls that may be in place. You may be surprised, then, to learn that our clients’ top-of-mind issues are not only how to fight the latest fraud trends, but how they can do so while maximizing use of automation, managing operational costs, and preserving customer experience -- all while meeting compliance requirements. Many times, clients view these goals as being unique goals that do not affect one another. Not only can these be accomplished simultaneously, but, in my opinion, they can be considered causal. Let me explain. By looking at fraud detection as its own goal, automation is not considered as a potential way to improve this metric. By applying analytics, or basic fraud risk scores, clients can easily incorporate many different potential risk factors into a single calculation without combing through various data elements and reports. This calculation or score can predict multiple fraud types and risks with less effort, than could a human manually, and subjectively reviewing specific results. Through an analytic score, good customers can be positively verified in an automated fashion; while only those with the most risky attributes can be routed for manual review. This allows expensive human resources and expertise to be used for only the most risky consumers. Compliance requirements can also mandate specific procedures, resulting in arduous manual review processes. Many requirements (Patriot Act, Red Flag, eSignature) mandate verification of identity through match results. Automated decisioning based on these results (or analytic score) can automate this process – in turn, reducing operational expense. While the above may seem to be an oversimplification or simple approach, I encourage you to consider how well you are addressing financial risk management.  How are you managing automation, operational costs, and compliance – while addressing fraud?  

By: Kari Michel Bankruptcies continue to rise and are expected to exceed 1.4 million by the end of this year, according to American Bankruptcy Institute Executive Director, Samuel J. Gerdano.  Although, the overall bankruptcy rates for a lender’s portfolio is small (about 1 percent), bankruptcies result in high dollar losses for lenders.  Bankruptcy losses as a percentage of total dollar losses are estimated to range from 45 percent for bankcard portfolios to 82 percent for credit unions.  Additionally, collection activity is restricted because of legislation around bankruptcy.  As a result, many lenders are using a bankruptcy score in conjunction with their new applicant risk score to make better acquisition decisions. This concept is a dual score strategy.  It is key in management of risk, to minimize fraud, and in managing the cost of credit. Traditional risk scores are designed to predict risk (typically predicting 90 days past due or greater).  Although bankruptcies are included within this category, the actual count is relatively small.   For this reason the ability to distinguish characteristics typical of a “bankruptcy” are more difficult.  In addition, often times a consumer who filed bankruptcy was in “good standings” and not necessarily reflective of a typical risky consumer.   By separating out bankrupt consumers, you can more accurately identify characteristics specific to bankruptcy.  As mentioned previously, this is important because they account for a significant portion of the losses. Bankruptcy scores provide added value when used with a risk score. A matrix approach is used to evaluate both scores to determine effective cutoff strategies.   Evaluating applicants with both a risk score and a bankruptcy score can identify more potentially profitable applicants and more high- risk accounts.  

By: Wendy Greenawalt In my last blog post I discussed the value of leveraging optimization within your collections strategy. Next, I would like to discuss in detail the use of optimizing decisions within the account management of an existing portfolio. Account Management decisions vary from determining which consumers to target with cross-sell or up-sell campaigns to line management decisions where an organization is considering line increases or decreases.  Using optimization in your collections work stream is key. Let’s first look at lines of credit and decisions related to credit line management. Uncollectible debt, delinquencies and charge-offs continue to rise across all line of credit products. In response, credit card and home equity lenders have begun aggressively reducing outstanding lines of credit.    One analyst predicts that the credit card industry will reduce credit limits by $2 trillion by 2010. If materialized, that would represent a 45 percent reduction in credit currently available to consumers. This estimate illustrates the immediate reaction many lenders have taken to minimize loss exposure. However, lenders should also consider the long-term impacts to customer retention, brand-loyalty and portfolio profitability before making any account management decision. Optimization is a fundamental tool that can help lenders easily identify accounts that are high risk versus those that are profit drivers. In addition, optimization provides precise action that should be taken at the individual consumer level. For example, optimization (and optimizing decisions) can provide recommendations for: • when to contact a consumer; • how to contact a consumer; and • to what level a credit line could be reduced or increased... …while considering organizational/business objectives such as: • profits/revenue/bad debt; • retention of desirable consumers; and • product limitations (volume/regional). In my next few blogs I will discuss each of these variables in detail and the complexities that optimization can consider.  

By: Kari Michel This blog completes my discussion on monitoring new account decisions with a final focus: scorecard monitoring and performance.  It is imperative to validate acquisitions scorecards regularly to measure how well a model is able to distinguish good accounts from bad accounts. With a sufficient number of aged accounts, performance charts can be used to: • Validate the predictive power of a credit scoring model; • Determine if the model effectively ranks risk; and • Identify the delinquency rate of recently booked accounts at various intervals above and below the primary cutoff score. To summarize, successful lenders maximize their scoring investment by incorporating a number of best practices into their account acquisitions processes: 1. They keep a close watch on their scores, policies, and strategies to improve portfolio strength. 2. They create monthly reports to look at population stability, decision management, scoring models and scorecard performance. 3. They update their strategies to meet their organization’s profitability goals through sound acquisition strategies, scorecard monitoring and scorecard management.

There are a lot of areas covered in your comment: efficiency; credit quality (human side or character in an impersonal environment); and policy adherence. We define efficiency and effectiveness using these metrics: • Turnaround time from application submission to decision; • Resulting delinquencies based upon type of underwriting (centralized vs. decentralized); • Production levels between centralized and decentralized; • Performance of the portfolio based upon type of underwriting; and • Turnaround time from application submission to decision Due to the nature of Experian’s technology, we are able to capture start and stop times of the typical activities related to loan origination.  After analyzing the data from 160+ financial institutions of all sizes, Experian publishes an annual small business benchmark report that documents loan origination process efficiencies and inefficiencies, benchmarking these as industry standards. Turnaround Time From the benchmark report, we’ve seen that institutions that are centralized have consistently had a turnaround time that is half of those with decentralized environments. Interestingly, turnaround time is also much faster for the larger institutions than for smaller.  This is confusing because the smaller community banks tend to promote the close relationship they have with their clients and their communities. Yet, when it comes to actually making a loan decision, it tends to take longer. In addition to speed, another aspect of turnaround is consistency.  We all can think of situations where we were able to beat the stated turnaround times of the larger or the centralized institutions.  Unfortunately, these tend to be isolated instances versus the consistent performance that is delivered in the centralized environment. Resulting delinquencies based upon type of underwriting/Performance of the portfolio based upon type of underwriting Again, referring to the annual small business lending benchmark report, delinquencies in a centralized environment are 50% of those in a decentralized environment. I have worked with a number of institutions that allow the loan officer/relationship manager to “reverse the decision” made by a centralized underwriting group.  The thinking is that the human aspect is otherwise missing in centralized underwriting.  When the data is collected, though, the incremental business/portfolio that is approved by the loan officer (who is close to the client and knows the human side) is not profitable from a credit quality perspective.  Specifically, this incremental portfolio typically has a net charge-off rate that exceeds the net interest margin -- and this is before we even consider the non-interest expense incurred. Your choice: is the incremental business critical to your success…or could you more fruitfully direct your relationship officer’s attention elsewhere? Production levels between centralized and decentralized Not to beat a dead horse, but the multiple of two comes into play here too.  As one looks at the throughput of each role (data entry, underwriter, relationship manager/lender), the production levels of a centralized environment are typically double that of a decentralized. It’s clear that the data point to the efficiency and effectiveness of a centralized environment    

By: Kari Michel This blog is a continuation of my previous discussion about monitoring your new account acquisition decisions with a focus on decision management. Decision management reports provide the insight to make more targeted decisions that are sound and profitable. These reports are used to identify: which lending decisions are consistent with scorecard recommendations; the effectiveness of overrides; and/or whether cutoffs should be adjusted. Decision management reports include: • Accept versus decline score distributions • Override rates • Override reason report • Override by loan officer • Decision by loan officer Successful lending organizations review this type of information regularly to make better lending policy decisions.  Proactive monitoring provides feedback on existing strategies and helps evaluate if you are making the most effective use of your score(s). It helps to identify areas of opportunity to improve portfolio profitability. In my next blog, I will discuss the last set of monitoring reports, scorecard performance.  

By: Tracy Bremmer In our last blog (July 30), we covered the first three stages of model development which are necessary whether developing a custom or generic model.  We will now discuss the next three stages, beginning with the “baking” stage:  scorecard development. Scorecard development begins as segmentation analysis is taking place and any reject inference (if needed) is put into place. Considerations for scorecard development are whether the model will be binned (divides predictive attributes into intervals) or continuous (variable is modeled in its entirety), how to account for missing values (or “false zeros”), how to evaluate the validation sample (hold-out sample vs. an out-of-time sample), avoidance of over-fitting the model, and finally what statistics will be used to measure scorecard performance (KS, Gini coefficient, divergence, etc.). Many times lenders assume that once the scorecard is developed, the work is done.   However, the remaining two steps are critical to development and application of a predictive model:  implementation/documentation and scorecard monitoring.   Neglecting these two steps is like baking a cake but never taking a bite to make sure it tastes good. Implementation and documentation is the last stage in developing a model that can be put to use for enhanced decisioning. Where the model will be implemented will determine the timeliness and complexity for when the models can be put into practice. Models can be developed in an in-house system, a third-party processor, a credit reporting agency, etc. Accurate documentation outlining the specifications of the model will be critical for successful implementation and model audits. Scorecard monitoring will need to be put into place once the model is developed, implemented and put into use. Scorecard monitoring evaluates population stability, scorecard performance, and decision management to ensure that the model is performing as expected over the course of time. If at any time there are variations based on initial expectations, then scorecard monitoring allows for immediate modifications to strategies. With all the right ingredients, the right approach, and the checks and balances in place, your model development process has the potential to come out “just right!”