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By: Mike Horrocks In 1950 Alice Stewart, a British medical professor, embarked on a study to identify what was causing so many cases of cancer in children. Her broad study covered many aspects of the lives of both child and mother, and the final result was that a large spike in the number of children struck with cancer came from mothers that were x-rayed during pregnancy. The data was clear and statistically beyond reproach and yet for nearly 25 more years, the practice of using x-rays during pregnancy continued. Why didn't doctors stop using x-rays? They clearly thought the benefits outweighed the risk and they also had a hard time accepting Dr. Stewart’s study. So how, did Dr. Stewart gain more acceptance of the study – she had a colleague, George Kneale, whose sole job was to disprove her study. Only by challenging her theories, could she gain the confidence to prove them right. I believe that theory of challenging the outcome carries over to the practice of risk management as well, as we look to avoid or exploit the next risk around the corner. So how can we as risk managers find the next trends in risk management? I don’t pretend to have all the answers, but here are some great ideas. Analyze your analysis. Are you drawing conclusions off of what would be obvious data sources or a rather simplified hypothesis? If you are, you can bet your competitors are too. Look for data, tools and trends that can enrich your analysis. In a recent discussion with a lending institution that has a relationship with a logistics firm, they said that the insights they get from the logistical experts has been spot-on in terms of regional business indicators and lending risks. Stop thinking about the next 90 days and start thinking about the next 9 quarters. Don’t get me wrong, the next 90 days are vital, but what is coming in the next 2+ years is critical. Expand the discussion around risk with a holistic risk team. Seek out people with different backgrounds, different ways of thinking and different experiences as a part of your risk management team. The broader the coverage of disciplines the more likely opportunities will be uncovered. Taking these steps may introduce some interesting discussions, even to the point of conflict in some meetings. However, when we look back at Dr. Stewart and Mr. Kneale, their conflicts brought great results and allowed for some of the best thinking at the time. So go ahead, open yourself and your organization to a little conflict and let’s discover the best thinking in risk management.
By: Teri Tassara The intense focus and competition among lenders for the super prime and prime prospect population has become saturated, requiring lenders to look outside of their safety net for profitable growth. This leads to the question “Where are the growth opportunities in a post-recession world?” Interestingly, the most active and positive movement in consumer credit is in what we are terming “emerging prime” consumers, represented by a VantageScore® of 701-800, or letter grade “C”. We’ve seen that of those consumers classified as VantageScore C in 3Q 2006, 32% had migrated to a VantageScore B and another 4% to an A grade over a 5-year window of time. And as more of the emerging prime consumers rebuild credit and recover from the economic downturn, demand for credit is increasing once again. Case in point, the auto lending industry to the “subprime” population is expected to increase the most, fueled by consumer demand. Lenders striving for market advantage are looking to find the next sweet spot, and ahead of the competition. Fortunately, lenders can apply sophisticated and advanced analytical methods to confidently segment the emerging prime consumers into the appropriate risk classification and predict their responsiveness for a variety of consumer loans. Here are some recommended steps to identifying consumers most likely to add significant value to a lender’s portfolio: Identify emerging prime consumers Understand how prospects are using credit Apply the most predictive credit attributes and scores for risk assessment Understand responsiveness level The stops and starts that have shaped this recovery have contributed to years of slow growth and increased competition for the same “super prime” consumers. However, these post-recession market conditions are gradually paving the way to opportunistic profitable growth. With advanced science, lenders can pair caution with a profitable growth strategy, applying greater rigor and discipline in their decision-making.
Last week, a group of us came together for a formal internal forum where we had the opportunity to compare notes with colleagues, hear updates on the challenges clients are facing and brainstorm solutions to client business problems across the discipline areas of analytics, fraud and software. As usual, fraud prevention and fraud analytics were key areas of discussion but what was also notable was how big a role compliance is playing as a business driver. First party fraud and identity theft detection are important components, sure, but as the Consumer Financial Protection Bureau (CFPB) gains momentum and more teeth, the demand for compliance accommodation and consistency grows critical as well. The role of good fraud management is to help accomplish regulatory compliance by providing more than just fraud risk scores, it can help to: Know Your Customer (KYC) or Customer Information Program (CIP) details such as the match results and level of matching across name, address, SSN, date of birth, phone, and Driver’s License. Understand the results of checks for high risk identity conditions such as deceased SSN, SSN more frequently used by another, address mismatches, and more. Perform a check against the Office of Foreign Asset Control’s SDN list and the details of any matches. And while some fraud solutions out there make use of these types of comparisons when generating a score or decision, they may not pass these along to their customers. And just think how valuable these details can be for both consistent compliance decisions and creating an audit trail for any possible audits.
The Fed’s Comprehensive Capital Analysis and Review (CCAR) and Capital Plan Review (CapPR) stress scenarios depict a severe recession that, although unlikely, the largest U.S. banks must now account for in their capital planning process. The bank holding companies’ ability to maintain adequate capital reserves, while managing the risk levels of growing portfolios are key to staying within the stress test parameters and meeting liquidity requirements. While each banks’ portfolios will perform differently, as a whole, the delinquency performance of major products such as Auto, Bankcard and Mortgage continues to perform well. Here is a comparison between the latest quarter results and two years ago from the Experian – Oliver Wyman Market Intelligence Reports. Although not a clear indication of how well a bank will perform against the hypothetical scenario of the stress tests, measures such as Probability of Default, Loss Given Default and Exposure at Default to indicate a bank’s risk may be dramatically improved from just a few years ago given recent delinquency trends in core portfolios. Recently we released a white paper that provides an introduction to Basel III regulation and discusses some of its impact on banks and the banking system. We also present a real business case showing how organizations turn these regulatory challenges into buisness opportunities by optimizing their credit strategies. Download the paper - Creating value in challenging times: An innovative approach to Basel III compliance.
By: Shannon Lois These are challenging times for large financial institutions. Still feeling the impact from the financial crisis of 2007, the banking industry must endure increased oversight, declining margins, and fierce competition—all in a lackluster economy. Financial institutions are especially subject to closer regulatory scrutiny. As part of this stepped-up oversight, the Federal Reserve Board (FRB) conducts annual assessments, including “stress tests”, of the capital planning processes and capital adequacy of BHCs to ensure that these institutions can continue operations in the event of economic distress. The Fed expects banks to have credible plans, which are evaluated across a range of criteria, showing that they have adequate capital to continue to lend, even under adverse economic conditions. Minimum capital standards are governed by both the FRB and under Basel III. The International Basel Committee established the Basel accords to provide revised safeguards following the financial crisis, as an effort to ensure that banks met capital requirements and were not overly leveraged. Using input data provided by the BHCs themselves, FRB analysts have developed stress scenario methodology for banks to follow. These models generate loss estimates and post-stress capital ratios. The CCAR includes a somewhat unnerving hypothetical scenario that depicts a severe recession in the U.S. economy with an unemployment rate of 13%, a 50% drop in equity prices, and 21% decline in housing market. Stress testing is intended to measure how well a bank could endure this gloomy picture. Between meeting the compliance requirements of both BASEL III and the Federal Reserve’s Comprehensive Capital Analysis and Review (CCAR), financial institutions commit sizeable time and resources to administrative tasks that offer few easily quantifiable returns. Nevertheless—in addition to ensuring they don’t suddenly discover themselves in a trillion-dollar hole—these audit responsibilities do offer some other benefits and considerations.
Contributed by: David Daukus As the economy is starting to finally turn around albeit with hiccups and demand for new credit picking up, creditors are loosening their lending criteria to grab market share. However, it is important for lenders to keep lessons from the past to avoid the same mistakes. With multiple government agencies such as the CFPB, OCC, FDIC and NCUA and new regulations, banking compliance is more complex than ever. That said, there are certain foundational elements, which hold true. One such important aspect is keeping a consistent and well-balanced risk management approach. Another key aspect is around concentration risk. This is where a significant amount of risk is focused in certain portfolios across specific regions, risk tiers, etc. (Think back to 2007/2008 where some financial institutions focused on making stated-income mortgages and other riskier loans.) In 2011, the Federal Reserve Board of Governors released a study outlining the key reasons for bank failures. This review focused mainly on 20 bank failures from June 29, 2009 thru June 30, 2011 where more in-depth reporting and analysis had been completed after each failure. According to the Federal Reserve Board of Governors, here are the four key reasons for the failed banks: (1) Management pursuing robust growth objectives and making strategic choices that proved to be poor decisions; (2) Rapid loan portfolio growth exceeding the bank’s risk management capabilities and/or internal controls; (3) Asset concentrations tied to commercial real estate or construction, land, and land development (CLD) loans; (4) Management failing to have sufficient capital to cushion mounting losses. So, what should be done? Besides adherence to new regulations, which have been sprouting up to save us all from another financial catastrophe, diversification of risk maybe the name of the game. The right mix of the following is needed for a successful risk management approach including the following steps: Analyze portfolios and needs Predict high risk accounts Create comprehensive credit policies Decision for risk and retention Refresh scores/attributes and policies So, now is a great time to renew your focus. Source: Federal Reserve Board of Governors: Summary Analysis of Failed Bank Reviews (9/2011)
By: Stacy Schulman Earlier this week the CFPB announced a final rule addressing its role in supervising certain credit reporting agencies, including Experian and others that are large market participants in the industry. To view this original content, Experian and the CFPB - Both Committed to Helping Consumers. During a field hearing in Detroit, CFPB Director Richard Cordray’s spoke about a new regulatory focus on the accuracy of the information received by the credit reporting companies, the role they play in assembling and maintaining that information, and the process available to consumers for correcting errors. We look forward to working with CFPB on these important priorities. To read more about how Experian prioritizes these information essentials for consumers, clients and shareholders, read more on the Experian News blog. Learn more about Experian's view of the Consumer Financial Protection Bureau. ___________________ Original content provided by: Tony Hadley, Senior Vice President of Government Affairs and Public Policy About Tony: Tony Hadley is Senior Vice President of Government Affairs and Public Policy for Experian. He leads the corporation’s legislative, regulatory and policy programs relating to consumer reporting, consumer finance, direct and digital marketing, e-commerce, financial education and data protection. Hadley leads Experian’s legislative and regulatory efforts with a number of trade groups and alliances, including the American Financial Services Association, the Direct Marketing Association, the Consumer Data Industry Association, the U.S. Chamber of Commerce and the Interactive Advertising Bureau. Hadley is Chairman of the National Business Coalition on E-commerce and Privacy.
With the constant (and improving!) changes in the consumer credit landscape, understanding the latest trends is vital for institutions to validate current business strategies or make adjustments to shifts in the marketplace. For example, a recent article in American Banker described how a couple of housing advocates who foretold the housing crisis in 2005 are now promoting a return to subprime lending. Good story lead-in, but does it make sense for “my” business? How do you profile this segment of the market and its recent performance? Are there differences by geography? What other products are attracting this risk segment that could raise concerns for meeting a new mortgage obligation? There is a proliferation of consumer loan and credit information online from various associations and organizations, but in a static format that still makes it challenging to address these types of questions. Fortunately, new web-based solutions are being made available that allow users to access and interrogate consumer trade information 24x7 and keep abreast of constantly changing market conditions. The ability to manipulate and tailor data by geography, VantageScore risk segments and institution type are just a mouse click away. More importantly, these tools allow users to customize the data to meet specific business objectives, so the next subprime lending headline is not just a story, but a real business opportunity based on objective, real-time analysis.
As a scoring manager, this question has always stumped me because there was never a clear answer. It simply meant less than prime – but how much less? What does the term actually mean? How do you quantify something so subjective? Do you assign it a credit score? Which one? There were definitely more questions than answers. But a new proposed ruling from the FDIC could change all that – at least when it comes to large bank pricing assessments. The proposed ruling does a couple of things to bring clarity to the murky waters of the subprime definition. First, it replaces the term “subprime” with “high-risk consumer loans”. Then they go one better: they quantify high-risk as having a 20% probability of default or higher. Finally, something we can calculate! The arbitrary 3-digit credit score that has been used in the past to define the line between prime and subprime has several flaws. First of all, if a subprime loan is defined as having any particular credit score, it has to be for a specific version of a specific model at a specific time. That’s because the default rates associated to any given score is relative to the model used to calculate it. There are hundreds of custom-build and generic scoring models in use by lenders today – does that single score represent the same level of risk to all of them? Absolutely not. And even if all risk models were calibrated exactly the same, just assigning credit risk a number has no real meaning over time. We all know what scores shift, that consumer credit behavior is not the same today as it was just 6 years ago. In 2006, if a score of X represented a 15% likelihood of default, that same score today could represent 20% or more. It is far better to align a definition of risk with its probability of default to begin with! While it only currently applies to the large bank pricing assessments with the FDIC, this proposed ruling is a great step in the right direction. As this new approach catches on, we may see it start to move into other polices and adopted by various organizations as they assess risk throughout the lending cycle.
By: Mike Horrocks This week, several key financial institutions will be submitting their “living wills” to Washington as part of the Dodd-Frank legislation. I have some empathy for how those institutions will feel as they submit these living wills. I don’t think that anyone would say writing a living will is fun. I remember when my wife and I felt compelled to have one in place as we realized that we did not want to have any questions unanswered for our family. For those not familiar with the concept of the living will, I thought I would first look at the more widely known medical description. The Mayo Clinic describes living wills as follows, “Living wills and other advance directives describe your preferences regarding treatment if you're faced with a serious accident or illness. These legal documents speak for you when you're not able to speak for yourself — for instance, if you're in a coma.” Now imagine a bank in a coma. I appreciate the fact that these living wills are taking place, but pulling back my business law books, I seem to recall that one of the benefits of a corporation versus say a sole proprietorship is that the corporation can basically be immortal or even eternal. In fact the Dictionary.com reference calls out that a corporation has “a continuous existence independent of the existences of its members”. So now imagine a bank eternally in a coma. Now, I cannot avoid all of those unexpected risks that will come up in my personal life, like an act of God, that may put me into a coma and invoke my living will, but I can do things voluntarily to make sure that I don’t visit the Emergency Room any time soon. I can exercise, eat right, control my stress and other healthy steps and in fact I meet with a health coach to monitor and track these things. Banks can take those same steps too. They can stay operationally fit, lend right, and monitor the stress in their portfolios. They can have their health plans in place and have a personal trainer to help them stay fit (and maybe even push them to levels of fitness they did not think they could reach). Now imagine a fit, strong bank. So as printers churn, inboxes get filled, and regulators read through thousands of pages of bank living wills, let’s think of the gym coach, or personal trainer that pushed us to improve and think about how we can be healthy and fit and avoid the not so pleasant alternatives of addressing a financial coma.
By: Joel Pruis From a score perspective we have established the high level standards/reporting that will be needed to stay on top of the resulting decisions. But there is a lot of further detail that should be considered and further segmentation that must be developed or maintained. Auto Decisioning A common misperception around auto-decisioning and the use of scorecards is that it is an all or nothing proposition. More specifically, if you use scorecards, you have to make the decision entirely based upon the score. That is simply not the case. I have done consulting after a decisioning strategy based upon this misperception and the results are not pretty. Overall, the highest percentage for auto-decisioning that I have witnessed has been in the 25 – 30% range. The emphasis is on the “segment”. The segments is typically the lower dollar requests, say $50,000 or less, and is not the percentage across the entire application population. This leads into the discussion around the various segments and the decisioning strategy around each segment. One other comment around auto-decisioning. The definition related to this blog is the systematic decision without human intervention. I have heard comments such as “competitors are auto-decisioning up to $1,000,000”. The reality around such comments is that the institution is granting loan authority to an individual to approve an application should it meet the particular financial ratios and other criteria. The human intervention comes from verifying that the information has been captured correctly and that the financial ratios make sense related to the final result. The last statement is the key to the disqualification of “auto-decisioning”. The individual is given the responsibility to ensure data quality and to ensure nothing else is odd or might disqualify the application from approval or declination. Once a human eye is looking at an application, judgment comes into the picture and we introduce the potential for inconsistencies and or extension of time to render the decision. Auto-decisioning is just that “Automatic”. It is a yes/no decision and is based upon objective factors that if met, allow the decision to be made. Other factors, if not included in the decision strategy, are not included. So, my fellow credit professionals, should you hear someone say they are auto-decisioning a high percent of their applications or a high dollar amount for an application, challenge, question and dig deeper. Treat it like the fishing story “I caught a fish THIS BIG”. No financials segment The highest volume of applications and the lowest total dollar production area of any business banking/small business product set. We had discussed the use of financials in the prior blog around application requirements so I will not repeat that discussion here. Our focus will be on the decisioning of these applications. Using score and application characteristics as the primary data source, this segment is the optimal segment for auto-decisioning. Speeds the decision process and provides the greatest amount of consistency in the decisions rendered. Two key areas for this segment are risk premiums and scorecard validations. The risk premium is important as you are going to accept a higher level of losses for the sake of efficiencies in the underwriting/processing of the application. The end result is lower operational costs, relatively higher credit losses but the end yield on this segment meets the required, yet practical, thresholds for return. The one thing that I will repeat from a prior blog is that you may request financials after the initial review but the frequency should be low and should also be monitored. The request of financials should not be the “belt and suspenders” approach. If you know what the financials are likely to show, then don’t request them. They are unnecessary. You are probably right and the collection of the financials will only serve to elongate the response time, frustrate everyone involved in the process and not change the expected results. Financials segment The relatively lower unit volume but the higher dollar volume segment. Likely this segment will have no auto-decisioning as the review of financials typically will mandate the judgmental review. From an operational perspective, these are high dollar and thus the manual review does not push this segment into a losing proposition. From a potential operational lift perspective, the ability to drive a higher volume of applications into auto-decisioning is simply not available as we are talking probably less than 40% (if not fewer) of all applications in this segment. In this segment, the consistency becomes more difficult as the underwriter tends to want to put his/her own approach on the deal. Standardization of the analysis approach (at least initially) is critical for this segment. Consistency in the underwriting and the various criteria allows for greater analysis to determine where issues are developing or where we are realizing the greatest success. My recommended approach is to standardize (via automation in the origination platform) the various calculations in a manner that will generate the most conservative approach. Bluntly put, my approach was to attempt to make the deal as ugly as possible and if it still passed the various criteria, no additional work was needed nor was there any need for detailed explanation around how I justified the deal/request. Only if it did not meet the criteria using the most conservative approach would I need to do any work and only if it was truly going to make a difference. Basic characteristics in this segment include – business cash flow, personal debt to income, global cash flow and leverage. Others may be added but on a case by case basis. What about the score? If I am doing so much judgmental underwriting, why calculate the score in this segment? In a nutshell, to act as the risk rating methodology for the portfolio approach. Even with the judgmental approach, we do not want to fall into the trap thinking we are going to be able to adequately monitor this segment in a proactive fashion to justify the risk rating at any point in time after the loan is booked. We have been focusing on the origination process in this blog series but I need to point out that since we are not going to be doing a significant amount of financial statement monitoring in the small business segment, we need to begin to move away from the 1 – 8 (or 9 or 10 or whatever) risk rating method for the small business segment. We cannot be granular enough with this rating system nor can we constantly stay on top of what may be changing risk levels related to the individual clients. But I am going to save the portfolio management area for a future blog. Regardless of the segment, please keep in mind that we need to be able to access the full detail of the information that is being captured during the origination process along with the subsequent payment performance. As you are capturing the data, keep in mind, the abilities to Access this data for purposes of analysis Connect the data from origination to the payment performance data to effectively validate the scorecard and my underwriting/decisioning strategies Dive into the details to find the root cause of the performance problem or success The topic of decisioning strategies is broad so please let me know if you have any specific topics that you would like addressed or questions that we might be able to post for responses from the industry.
Recently we released a white paper that emphasizes the need for better, more granular indicators of local home-market conditions and borrower home equity, with a very interesting new finding on leading indicators in local-area credit statistics. Click here to download the white paper Home-equity indicators with new credit data methods for improved mortgage risk analytics Experian white paper, April 2012 In the run-up to the U.S. housing downturn and financial crisis, perhaps the greatest single risk-management shortfall was poorly predicted home prices and borrower home equity. This paper describes new improvements in housing market indicators derived from local-area credit and real-estate information. True housing markets are very local, and until recently, local real-estate data have not been systematically available and interpreted for broad use in modeling and analytics. Local-area credit data, similarly, is relatively new, and its potential for new indicators of housing market conditions is studied here in Experian’s Premier Aggregated Credit Statistics.SM Several examples provide insights into home-equity indicators for improved mortgage models, predictions, strategies, and combined LTV measurement. The paper finds that for existing mortgages evaluated with current combined LTV and borrower credit score, local-area credit statistics are an even stronger add-on default predictor than borrower credit attributes. Click here to download the white paper Authors: John Straka and Chuck Robida, Experian Michael Sklarz, Collateral Analytics
Previously, we looked at the various ways a dual score strategy could help you focus in on an appropriate lending population. Find your mail-to population with a prospecting score on top of a risk score; locate the riskiest of all consumers by layering a bankruptcy score with your risk model. But other than multiple scores, what other tools can be used to improve credit scoring effectiveness? Credit attributes add additional layers of insight from a risk perspective. Not everyone who scores an 850 represent the same level of risk once you start interrogating their broader profile. How much total debt are they carrying? What is the nature of it - is it mortgage or mostly revolving? A credit score may not fully articulate a consumer as high risk, but if their debt obligations are high, they may represent a very different type of risk than from another consumer with the same 850 score. Think of attribute overlays in terms of tuning the final score valuation of an individual consumer by making the credit profile more transparent, allowing a lender to see more than just the risk odds associated with the initial score. Attributes can also help you refine offers. A consumer may be right for you in terms of risk, but are you right for them? If they have 4 credit cards with $20K limits each, they’re likely going to toss your $5K card offer in the trash. Attributes can tell us these things, and more. For example, while a risk score can tell us what the risk of a consumer is within a set window, certain credit attributes can tell us something about the stability of that consumer to remain within that risk band. Recent trends in score migration – the change in a level of creditworthiness of a consumer subsequent to generation of a current credit score – can undermine the most conservative of risk management policies. At the height of the recession, VantageScore® Solutions LLC studied the migration of scores across all risk bands and was able to identify certain financial management behaviors found within their credit files. These behaviors (signaling, credit footprint, and utility) assess the consumer’s likelihood of improving, significantly deteriorating, or maintaining a stable score over the next 12 months. Knowing which subgroup of your low-risk population is deteriorating, or which high risk groups are improving, can help you make better decision today.
One of the most successful best practices for improving agency performance is the use of scorecards for assessing and rank ordering performance of agencies in competition with each other. Much like people, agencies thrive when they understand how they are evaluated, how to influence those factors that contribute to success, and the recognition and reward for top tier performance. Rather than a simple view of performance based upon a recovery rate as a percentage of total inventory, best practice suggests that performance is more accurately reflected in vintage batch liquidation and peer group comparisons to the liquidation curve. Why? In a nutshell, differences in inventory aging and the liquidation curve. Let’s explain this in greater detail. Historically, collection agencies would provide their clients with better performance reporting than their clients had available to them. Clients would know how much business was placed in aggregate, but not by specific vintage relating to the month or year of placement. Thus, when a monthly remittance was received, the client would be incapable of understanding whether this month’s recoveries were from accounts placed last month, this year, or three years ago. This made forecasting of future cash flows from recoveries difficult, in that you would have no insight into where the funds were coming from. We know that as a charged off debt ages, its future liquidation rate generally downward sloping (the exception is auto finance debt, as there is a delay between the time of charge-off and rehabilitation of the debtor, thus future flows are higher beyond the 12-24 month timeframe). How would you know how to predict future cash flows and liquidation rates without understanding the different vintages in the overall charged off population available for recovery? This lack of visibility into liquidation performance created another issue. How do you compare the performance of two different agencies without understanding the age of the inventory and how it is liquidating? An as example, let’s assume that Agency A has been handling your recovery placements for a few years, and has an inventory of $10,000,000 that spans 3+ years, of which $1,500,000 has been placed this year. We know from experience that placements from 3 years ago experienced their highest liquidation rate earlier in their lifecycle, and the remaining inventory from those early vintages are uncollectible or almost full liquidated. Agency A remits $130,000 this month, for a recovery rate of 1.3%. Agency B is a new agency just signed on this year, and has an inventory of $2,000,000 assigned to them. Agency B remits $150,000 this month, for a recovery rate of 7.5%. So, you might assume that Agency B outperformed Agency A by a whopping 6.2%. Right? Er … no. Here’s why. If we had better visibility of Agency A’s inventory, and from where their remittance of $130,000 was derived, we would have known that only a couple of small insignificant payments came from the older vintages of the $10,000,000 inventory, and that of the $130,000 remitted, over $120,000 came from current year inventory (the $1,500,000 in current year placements). Thus, when analyzed in context with a vintage batch liquidation basis, Agency A collected $120,000 against inventory placed in the current year, for a liquidation rate of 8.0%. The remaining remittance of $10,000 was derived from prior years’ inventory. So, when we compare Agency A with current year placements inventory of $1,500,000 and a recovery rate against those placements of 8.0% ($120,000) versus Agency B, with current year placements inventory of $2,000,000 and a recovery rate of 7.5% ($150,000), it’s clear that Agency A outperformed Agency B. This is why the vintage batch liquidation model is the clear-cut best practice for analysis and MI. By using a vintage batch liquidation model and analyzing performance against monthly batches, you can begin to interpret and define the liquidation curve. A liquidation curve plots monthly liquidation rates against a specific vintage, usually by month, and typically looks like this: Exhibit 1: Liquidation Curve Analysis Note that in Exhibit 1, the monthly liquidation rate as a percentage of the total vintage batch inventory appears on the y-axis, and the month of funds received appears on the x-axis. Thus, for each of the three vintage batches, we can track the monthly liquidation rates for each batch from its initial placement throughout the recovery lifecycle. Future monthly cash flow for each discrete vintage can be forecasted based upon past performance, and then aggregated to create a future recovery projection. The most sophisticated and up to date collections technology platforms, including Experian’s Tallyman™ and Tallyman Agency Management™ solutions provide vintage batch or laddered reporting. These reports can then be used to create scorecards for comparing and weighing performance results of competing agencies for market share competition and performance management. Scorecards As we develop an understanding of liquidation rates using the vintage batch liquidation curve example, we see the obvious opportunity to reward performance based upon targeted liquidation performance in time series from initial placement batch. Agencies have different strategies for managing client placements and balancing clients’ liquidation goals with agency profitability. The more aggressive the collections process aimed at creating cash flow, the greater the costs. Agencies understand the concept of unit yield and profitability; they seek to maximize the collection result at the lowest possible cost to create profitability. Thus, agencies will “job slope” clients’ projects to ensure that as the collectability of the placement is lower (driven by balance size, customer credit score, date of last payment, phone number availability, type of receivable, etc.) For utility companies and other credit grantors with smaller balance receivables, this presents a greater problem, as smaller balances create smaller unit yield. Job sloping involves reducing the frequency of collection efforts, employing lower cost collectors to perform some of the collection efforts, and where applicable, engaging offshore resources at lower cost to perform collection efforts. You can often see the impact of various collection strategies by comparing agency performance in monthly intervals from batch placement. Again, using a vintage batch placement analysis, we track performance of monthly batch placements assigned to competing agencies. We compare the liquidation results on these specific batches in monthly intervals, up until the receivables are recalled. Typical patterns emerge from this analysis that inform you of the collection strategy differences. Let’s look at an example of differences across agencies and how these strategy differences can have an impact on liquidation: As we examine the results across both the first and second 30-day phases, we are likely to find that Agency Y performed the highest of the three agencies, with the highest collection costs and its impact on profitability. Their collection effort was the most uniform over the two 30-day segments, using the dialer at 3-day intervals in the first 30-day segment, and then using a balance segmentation scheme to differentiate treatment at 2-day or 4-day intervals throughout the second 30-day phase. Their liquidation results would be the strongest in that liquidation rates would be sustained into the second 30-day interval. Agency X would likely come in third place in the first 30-day phase, due to a 14-day delay strategy followed by two outbound dialer calls at 5-day intervals. They would have a better performance in the second 30-day phase due to the tighter 4-day intervals for dialing, likely moving into second place in that phase, albeit at higher collection costs for them. Agency Z would come out of the gates in the first 30-day phase in first place, due to an aggressive daily dialing strategy, and their takeoff and early liquidation rate would seem to suggest top tier performance. However, in the second 30-day phase, their liquidation rate would fall off significantly due to the use of a less expensive IVR strategy, negating the gains from the first phase, and potentially reducing their over position over the two 30-day segments versus their peers. The point is that with a vintage batch liquidation analysis, we can isolate performance of a specific placement across multiple phases / months of collection efforts, without having that performance insight obscured by new business blended into the analysis. Had we used the more traditional current month remittance over inventory value, Agency Z might be put into a more favorable light, as each month, they collect new paper aggressively and generate strong liquidation results competitively, but then virtually stop collecting against non-responders, thus “creaming” the paper in the first phase and leaving a lot on the table. That said, how do we ensure that an Agency Z is not rewarded with market share? Using the vintage batch liquidation analysis, we develop a scorecard that weights the placement across the entire placement batch lifecycle, and summarizes points in each 30-day phase. To read Jeff's related posts on the topic of agency management, check out: Vendor auditing best practices that will help your organization succeed Agency managment, vendor scorecards, auditing and quality monitoring
Up to this point, I’ve been writing about loan originations and the prospects and challenges facing bankcard, auto and real estate lending this year. While things are off to a good start, I’ll use my next few posts to discuss the other side of the loan equation: performance. If there’s one thing we learned during the post-recession era is that growth can have consequences if not managed properly. Obviously real estate is the poster child for this phenomenon, but bankcards also realized significant and costly performance deterioration following the rapid growth generated by relaxed lending standards. Today, bankcard portfolios are in expansion mode once again, but with delinquency rates at their lowest point in years. In fact, loan performance has improved nearly 50% in the past three years through a combination of tighter lending requirements and consumers’ self-imposed deleveraging. Lessons learned from issuers and consumers have created a unique climate in which growth is now balanced with performance. Even areas with greater signs of payment stress have realized significant improvements. For example, the South Atlantic region’s 4.2% 30+ DPD performance is 11% higher than the national average, but down 27% from a year ago. Localized economic factors definitely play a part in performance, but the region’s higher than average origination growth from a broader range of VantageScore® credit score consumers could also explain some of the delinquency stress here. And that is the challenge going forward: maintaining bankcard’s recent growth while keeping performance in check. As the economy and consumer confidence improves, this balancing act will become more difficult as issuers will want to meet the consumer’s appetite for spending and credit. Increased volume and utilization is always good for business, but it won’t be until the performance of these loans materializes that we’ll know whether it was worth it.