A recent survey of 1,000 representative American consumers showed that while 78 percent of respondents are aware that they have more than one credit score, some key misperceptions remain: • Fewer than half (44 percent) understand that a credit score typically measures risk of not repaying loans rather than amount of debt (22 percent), financial resources (21 percent) or other factors. • More than half still think that a person's age (56 percent) and marital status (54 percent) are factors used to calculate credit scores, and 21 percent incorrectly believe that ethnic origin is a factor. Click here to get the facts on the types of credit scores and what influences them. Source: VantageScore® press release, May 2012. VantageScore® is owned by VantageScore Solutions, LLC.

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  

Our guest blogger this week is Karen Barney of the Identity Theft Resource Center (ITRC). The rise of online functionality and connectivity has in turn given rise to online security issues, which create the need for passwords and other defenses against information theft.  Most people today have multiple online accounts and accompanying passwords to protect those accounts.  I personally have accounts (and passwords) for sites I no longer even remember.  And while I have more accounts than most due to my profession, my hunch is that many people deal with the issue of password overload.  Password overload is when you attempt to use your Pinterest, Twitter, work email and university login passwords (one after another) to get into your Money Market Account only to be locked out.  Now you have to go into the branch with photo ID, or endure the dreaded “customer service hotline” (not-line) to prove that “you are you.”  I expect that you have experienced such “password overload” inconveniences, or you almost certainly know someone who has. The problem seems like it could be easily solved by using the same password for everything.  One password to remember, and no more jumbling through your notebook trying to find what password you used for your newest account creation or Facebook app.  The problem with this approach is that if you are using the same passwords for all (or even several) of your accounts, then if someone manages to get the password for say, your Instagram account, they would probably be able to then drain your savings account, phish your family for personal information (such as your Social Security Number), or rack up a warrant in your name for writing bad checks….  This could all happen because you logged into Facebook at an unsecured Wi-fi location, where your password for that one account is compromised, and it happens to be the same password you use for multiple accounts. So, what do you do if you don’t want to tattoo 25 passwords on your arm and you don’t want to end up cuffed for felony check fraud? The answer is a password manager.  This new service was created so that users can remember just one password, yet have access to all other passwords. The best part is that you can have access to these passwords from anywhere as most of the new password managers are internet based. As the need for password management increases, the options consumers have grown leaving even the strictest cybersecurity aficionado pleased with the service. A few things you should look for when finding a password manager are: Is it cross platform? Will it work on your iPhone and your PC? How is the information (your passwords) encrypted? Does the service sync automatically, or will the user need to update the password storage database every time they sign up for a new account? What is the initial authentication process and how strong is it? How reputable is the company who created the product and what is reported about the product itself? By asking yourself these questions you should be on your way to making sure that your passwords are protected and you won’t lose your mind trying to keep track of them all. Just make sure you protect your login credentials for your password manager…. like really, really well…

The dramatic transformation of the financial services industry requires new advances and innovation in credit strategies to respond to the growing number of underbanked customers who need to be served. The underbanked, or unbanked, market now represents nearly 64 million U.S. consumers who have limited or no traditional credit history. Take a quiz now to test your knowledge of America's underbanked. Source: Experian News, May 2012

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.

Mortgage origination volumes increased to $427 billion in Q4 2011 – a 31 percent quarterly gain. However, overall 2011 originations of $1.35 trillion were 16 percent lower than 2010 volumes. Sign up to attend our upcoming Webinar, which will focus on current credit trends and feature a closer look at the overleveraged consumer. Source: Experian-Oliver Wyman Market Intelligence Reports.

Year over year retail spend continues to trend up, translating into Bankcard balance growth and new originations. New Bankcard volumes (limits) came in at $59 billion in Q4 2011 – a 52 percent increase over the previous year. Register now for our upcoming credit trends webinar. Source: Experian Infographic: Bankcard and Retail Spending Trends.

Outstanding automotive loan balances were at $708 billion in Q1 2012 – a figure last seen two years ago. Banks and captive auto lenders hold two-thirds of the outstanding balances (34 percent and 33 percent respectively), while credit unions hold 21 percent. Listen to the latest automotive credit trends by attending our upcoming webinar. Source: Experian-Oliver Wyman Market Intelligence Reports.  

The average turnaround time to make a lending decision varies materially between financial institutions. Institutions with low-level automation are typically less competitive on price due to the higher cost of manual reviews. For customers, it leads to high levels of dissatisfaction, complaints and switching of institutions. To learn more practical insights and best practices for key areas of business banking and to look at the features of a leading-edge approach to customer management, download the full white paper. Source: Strategic customer management for business banking portfolios by Experian's Global Consulting Practice.

As part of its expanded guidance, the Office of the Comptroller of the Currency explicitly recommends that financial services firms utilizing predictive models and decision analytics run regular validations to gauge model efficacy. The VantageScore® credit score model was recently measured against the best credit score models from each of the three largest credit reporting companies (CRCs). When comparing KS values, there is exceptionally strong performance for mortgage originations, with the VantageScore® credit score model outperforming the CRC models in a range from 8 percent to 12 percent. The average range of outperformance is 3 percent to 4 percent across the board for most of the key industries. View the VantageScore® Webinar: Executing Effective Validations in 2011 and Beyond. Source: Executing Effective Validations, American Banker. VantageScore® is owned by VantageScore Solutions, LLC.

A vintage analysis comparing 60 or more days past due (DPD) delinquency performance at the one-year mark for mortgages originated between 2002 and 2010 shows that 2010 outperformed previous years, with a delinquency rate of 0.37 percent. The worst- performing vintage was 2006, with a 60 or more DPD delinquency rate of 3.84 percent – more than 10 times the delinquency rate of 2010. Listen to our recorded Webinar for a detailed look at the current state of strategic default in mortgage and an update on consumer credit trends. Source: Experian-Oliver Wyman Market Intelligence Reports

After increasing for the first time in nearly two years, the 30 and 60 days past due (DPD) mortgage delinquencies as a percentage of balances returned to their downward trend, with Q4 delinquency rates of 2.18 percent and 1.06 percent, respectively. This represents a decline of 3.5 percent for the 30 DPD category and a 2.8 percent decline for 60 DPD. Listen to our recorded Webinar for a detailed look at the current state of mortgage strategic default and an update on consumer credit trends from the Q4 2011 Experian-Oliver Wyman Market Intelligence Reports. Source: Experian-Oliver Wyman Market Intelligence Reports.

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