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Vintage analysis 101 The title of this edition, ‘The risk within the risk’ is a testament to the amount of information that can be gleaned from an assessment of the performances of vintage analysis pools. Vintage analysis pools offer numerous perspectives of risk. They allow for a deep appreciation of the effects of loan maturation, and can also point toward the impact of external factors, such as changes in real estate prices, origination standards, and other macroeconomic factors, by highlighting measurable differences in vintage to vintage performance. What is a vintage pool? By the Experian definition, vintage pools are created by taking a sample of all consumers who originated loans in a specific period, perhaps a certain quarter, and tracking the performance of the same consumers and loans through the life of each loan. Vintage pools can be analyzed for various characteristics, but three of the most relevant are: * Vintage delinquency, which allows for an understanding of the repayment trends within each pool; * Payoff trends, which reflect the pace at which pools are being repaid; and * Charge-off curves, which provide insights into the charge-off rates of each pool. The credit grade of each borrower within a vintage pool is extremely important in understanding the vintage characteristics over time, and credit scores are based on the status of the borrower just before the new loan was originated. This process ensures that the new loan origination and the performance of the specific loan do not influence the borrower’s credit score. By using this method of pooling and scoring, each vintage segment contains the same group of loans over time – allowing for a valid comparison of vintage pools and the characteristics found within. Once vintage pools have been defined and created, the possibilities for this data are numerous... Read more about our analysis opportunities for vintage analysis and our recent findings on vintage analysis.  

In addition to behavioral models, collections and account management groups need the ability to implement collections workflow strategies in order to effectively handle and process accounts, particularly when the optimization of resources is a priority. While the behavioral models will effectively evaluate and measure the likelihood that an account will become delinquent or result in a loss, strategies are the specific actions taken, based on the score prediction, as well as other key information that is available when those actions are appropriate. Identifying high-risk accounts, for example, may result in strategies designed to accelerate collections management activity and execute more aggressive actions. On the other hand, identifying low-risk accounts can help determine when to take advantage of cost-saving actions and focus on customer retention programs.  Effective strategies also address how to handle accounts that fall between the high- and low-risk extremes, as well as accounts that fall into special categories such as first payment defaults, recently delinquent accounts and unique customer or product segments. To accommodate lenders with systems that cannot support either behavioral scorecards or strategies, Experian developed the powerful service bureau solution, Portfolio Management Package, which is also referred to as PMP. To use this service, lenders send Experian customer master file data on a daily basis. Experian processes the data through the Portfolio Management Package system which includes calculating Fast Start behavior scores and identifying special handling accounts and electronically delivers the recommended strategies and actions codes within hours. Scoring and strategy parameters can be easily changed, as well as portfolio segmentation, special handling options and scorecard selections. PMP also supports Champion Challenger testing to enable users to learn which strategies are most effective. Comprehensive reports suites provide the critical information needed for lenders to design strategies and evaluate and compare the performance of those strategies.  

We’ve stopped taking phone applications and are using the out-of-wallet questions for Internet credit applications. Are we going overboard?The Red Flags Rule does not preclude phone applications or otherwise limit the manner in which you m ay accept applications for covered accounts. However, different methods to open covered accounts present different identity theft risks, and you must consider those differing risks in identifying the relevant Red Flags for each type of covered account that you provide.  

Understanding the Champion/Challenger testing strategy As the economic world continues to change, collection strategy testing becomes increasingly important. Champion/Challenger strategy testing is performed using a sample segment and the results provide a learning tool for determining which collections strategies are most effective. This allows strategies to be tested before rolling them out across the entire portfolio. The purpose of this experimental element to collections strategy management is to observe the effectiveness of new strategies, support continuous improvement of collection approaches and facilitate adaptability to changes in consumer behavior. The methodology behind testing is simple. First, the current environment should be assessed to identify specific areas for potential improvement. Then, a test plan is designed. The test plan should, at a minimum, include well-defined objectives and goals, proposed strategy design, determination of sample size, operational considerations, execution approach, success criteria, and evaluation timetable. After the framework for the test plan has been outlined, running “what if” scenarios will improve refinement of the collections strategy. In the next phase, implementation occurs following the directives of the test plan. Evaluating strategies commences after implementation and continues throughout the duration of the test. This includes analyzing metrics established during the test plan phase to identify trends and changes as a result of the new challenger strategy. The challenger strategy is declared the new champion if the test achieves or exceeds expectations. However, before proceeding with the new champion strategy over the entire portfolio, carefully consider any operational constraints that might hinder the success of the strategy on a grand scale. Once these operational constraints have been identified and their impact assessed, the new champion strategy should be executed.

Regardless of the specific checks and overall processes incorporated into your Red Flags Identity Theft Prevention Program, the use of an automated decisioning strategy or strategies will allow you to: Deliver consistent responses based on objective authentication results, while eliminating subjectivity often found in more manual review processes.  Save time and money associated with a manual review process currently attributed to Red Flag Rule referrals.  Provide examiners a detailed process flow including decision elements.  Create champion / challenger flows to test, compare and alter new strategies over time.  Revise, over time, the specific elements used in your decisioning to appropriately weight each from a fraud detection and/or compliance perspective. Experian's consumer authentication products provide hosted decisioning strategies that alleviate the burden on our clients associated with maintenance and development of those processes.  Whether you facilitate your own strategies or use a service provider's hosted strategies, it is important to ensure you are maximizing their ability to balance pass rates, fraud detection and compliance requirements.

Part 3 Reducing operational and overhead costs starts with the automation of tasks that would otherwise be performed by a human resource. By leveraging an advanced segmentation approach, it is possible to better identify accounts that will not require collector intervention. While automation is not a new concept to collections, significant benefits of modern systems include: • enabling more functions to be automated; • effectiveness of the automated functions to be validated; and • more changes made per year versus legacy systems. Fixing a bad phone number: The old way To illustrate effective automation, let’s use an example where an account is found to have a bad phone number. A common approach to this problem might be for the outbound collector to route the account to a skip specialist who can perform research. This often has the receiving party starting the process after the nightly batch process has transferred the account across departments. If a phone number is found, the account may be manually routed back to an outbound queue and if not, a no-contact letter may be generated. Additionally, there are tasks that need to be performed such as noting accounts that consume a collector’s time. Fixing a bad phone number: The new way A more efficient and cost-effective approach would be for the employee identifying the need for a new number to click a pre-defined button to let the collections system know of the issue. The system could then automatically call out to an external data source to: • collect the new number; • repopulate the appropriate field; • reroute the account back to the most appropriate outbound queue; • log a history of all automated functions performed, and • do all of this within just a few seconds! If the appropriate number cannot be located, the system would know which letter to send and then route the account to the most appropriate holding queue. Reducing operational costs After automation, the operational costs are further reduced by identifying which actions can be effectively replaced by lower-cost options that yield the same results, or even eliminating actions that present no substantial value. For example, why make a call when a letter will suffice? And what happens if we subsequently replace that letter with a text message or take no action at all? Intelligent features of modern systems such as champion/challenger testing can be employed to support a continuous learning process that increases the financial benefits of automation as experience and knowledge is gained. As new automation is introduced and validated as beneficial, other improvement theories can be tested and subsequently abandoned or adopted. Considering the possible impact of automation and action reductions on cost savings let’s assume that three dial attempts are made on the average delinquent account in the first 30 days at a cost of 25 cents each and on the fourth attempt there is a right party contact, which costs an additional $2.50 (assuming the talk time is five minutes). Adding one letter at 75 cents, we have a total cost to collect of $4.00 before the account hits 31 days past due. With 250,000 customers entering collections each month, we can save $200,000 each month in the early stage alone with just a 20 percent improvement. This result could easily be achieved by reducing talk time and eliminating unnecessary actions or unproductive call attempts. Annually that adds up to approximately $2.5 million dollars in savings, in this example. Champion/challenger tests, as well as, the improved functionality of modern systems can also be extended beyond the in-house work stream. Evaluating and comparing external agencies can significantly improve agency performance as well as enable the lender to better manage placement costs. For example, if a lender allocates 1,000 accounts to an external agency each month, with an average balance of $3,000, the total dollars allocated annually is $36 million. If 22 percent of the debt is collected and a 25 percent commission is charged, the net to the lender is nearly $6 million. Improving that return by a mere 4 percent through better allocation strategies, which is a conservative goal, we add another million to the bottom line each year. By factoring in the ability of next generation collections systems to automate most aspects of the placement process itself, including recalling accounts, we further improve efficiencies, free up valuable resources and allow management greater control of the process. Additional benefits of functionally rich modern systems also enable management to grant external resources various levels of remote access to the collections systems to better monitor activities and ensure that transactional data is properly captured. In addition to granting external agencies remote access, modern collections systems can also enable collectors to work from home-based workstations to further reduce operational costs. Many industry analysts see this as an emerging trend over the next few years, particularly when productivity can be monitored in real-time. My next blog will continue the discussion on the benefits of next generation collections systems and will provide details on improved change management processes.  

The difference between market risk and credit risk By: Tom Hannagan Market risk is different than credit risk. The bank’s assets are mostly invested in loans and securities (about 90% of average assets). These loans and securities have differing interest rate structures – some are fixed and some are floating. They also have differing maturities. Meanwhile, the bank’s liabilities, deposits and borrowings also have differing maturities and interest rate characteristics. If the bank’s (asset-based) interest income structure is not properly aligned with the (liability-based) interest expense structure, the result is interest rate risk. As market rates change (up or down), the bank’s earning are impacted (positively or negatively) based on the mismatch in its balance sheet structure. The bank can offset market risk by purchasing interest rate swaps or other interest rate derivatives. The impact of insufficient attention to interest rate risk can damage earnings and may, again, negatively affect the bank’s capital position. So, ultimately, the bank’s risk-based capital acts as the last line of defense against the negative impact from, you guessed it, unpredictable variability – or “risk.” That is why equity is considered risk-based capital. Good risk management, predicting and risk-based pricing leads to safer earnings performance and equity position.

 I’ve talked (sorry, blogged) previously about taking a risk-based approach to reconciling initial Red Flag Rule conditions in your applications, transactions, or accounts.  In short, that risk-based approach incorporates a more holistic view of a consumer in determining overall risk associated with that identity.  This risk can be assessed via an authentication score, alternate data sources and/or verification results.  I also want to point out the potential value of knowledge-based authentication (a.k.a. out-of-wallet questions) in providing an extra level of confidence in progressing a consumer transaction or application in light of an initially detected Red Flag condition. In Experian’s Fraud and Identity Solutions business, we have some clients who are effectively embedding the use of knowledge-based authentication into their overall Red Flags Identity Theft Prevention Program.  In doing so, they are able to identify the majority of higher risk conditions and transactions and positively authenticate those initiating consumers via a series of interactive questions designed to be more easily answered by a legitimate individual -- and more difficult for a fraudster.  Using knowledge-based authentication can provide the following values to your overall process: 1. Consistency: Utilizing a hosted and standard process can reduce potential subjectivity in decisioning.  Subjectivity is not a friend to examiners or to your bottom line. 2. Measurability: Question performance and reporting allows for ongoing monitoring and optimization of decisioning strategies.  Plus, examiners will appreciate the metrics. 3. Customer Experience: This is a buzzword these days for sure.  Better to place a customer through a handful of interactive questions, than to ask them to fax in documentation --or to take part in a face-to-face authentication. 4. Cost: See the three values above…Plus, a typical knowledge-based authentication session may well be more cost effective from an FTE/manual review perspective. Now, keep in mind that the use of knowledge-based authentication is certainly a process that should be approved by your internal compliance and legal teams for use in your Red Flags Identity Theft Prevention Program.  That said, with sound decisioning strategies based on authentication question performance in combination with overall authentication results and scores, you can be well-positioned to positively progress the vast majority of consumers into profitable accounts and transactions without incurring undue costs.

By: Tom Hannagan I was hoping someone would ask about this. Return on Equity (ROE) is generally net income divided by equity, while Return on Assets (ROA) is net income divided by average assets. There you have it. The calculations are pretty easy. But, what do they mean? ROA tends to tell us how effectively an organization is taking earnings advantage of its base of assets.  This used to be the most popular way of comparing banks to each other -- and for banks to monitor their own performance from period to period. Many banks and bank executives still prefer to use ROA…though typically at the smaller banks. ROE tends to tell us how effectively an organization is taking advantage of its base of equity, or capital. This has gained in popularity for several reasons and has become the preferred measure at larger banks. One huge reason for the growing popularity of ROE is, simply, that it is not asset-dependent. ROE can be applied to any line of business or any product. You must have “assets” for ROA, since one cannot divide by zero. This flexibility allows banks with differing asset structures to be compared to each other, or even for banks to be compared to other types of businesses. The asset-independency of ROE also allows a bank to compare internal product line performance to each other. Perhaps most importantly, this permits looking at the comparative profitability of lines of business like deposit services. This would be difficult, if even possible, using ROA. If you are interested in how well a bank is managing its assets, or perhaps its overall size, ROA may be of assistance. Lately, what constitutes a good and valid portrayal of assets has come into question at several of the largest banks. Any measure is only as good as its components. Be sure you have a good measure of asset value, including credit risk adjustments. ROE on the other hand looks at how effectively a bank (or any business) is using shareholders’ equity. Many observers like ROE, since equity represents the owners’ interest in the business. Their equity investment is fully at risk compared to other sources of funds supporting the bank. Shareholders are the last in line if the going gets rough. So, equity capital tends to be the most expensive source of funds, carrying the largest risk premium of all funding options. Its deployment is critical to the success, even the survival, of the bank. Indeed, capital allocation or deployment is the most important executive decision facing the leadership of any organization. If that isn’t enough, ROE is also Warren Buffet’s favorite measure of performance. Finally, there are the risk implications of the two metrics. ROA can be risk-adjusted up to a point. The net income figure can be risk adjusted for mitigated interest rate risk and for expected credit risk that is mitigated by a loan loss provision. The big missing element in even a well risk-adjusted ROA metric is unexpected loss (UL). Unexpected loss, along with any unmitigated expected loss, is covered by capital. Further, aside from the economic capital associated with unexpected loss, there are regulatory capital requirements. This capital is left out of the ROA metric. This is true at the entity level and for any line-of-business performance measures internally. Since ROE uses shareholder equity as its divisor, and the equity is risk-based capital, the result is, more or less, automatically risk-adjusted. In addition to the risk adjustments in its numerator, net income, ROE can use an economic capital amount. The result is a risk-adjusted return on capital, or RAROC. RAROC takes ROE to a fully risk-adjusted metric that can be used at the entity level and that can also be broken down for any and all lines of business within the organization. As discussed in the last post, ROE and RAROC help a bank get to the point where they are more fully “accounting” for risk – or “unpredictable variability”. Sorry about all of the alphabet soup, but there is a natural progression that I’m pointing to that we do see banks working their way through. That progression is being led by the larger banks that need to meet more sophisticated capital reporting requirements, and is being followed by other banks as they get more interested in risk-adjusted monitoring as a performance measurement. The better bank leadership is at measuring risk-adjusted performance, using ROE or RAROC, the better leadership can become at pricing for all risk at the client relationship and product levels.