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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.

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.

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

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  

The average bankcard balance per consumer rose to $4,359 in Q1 2012 – an 8 percent increase from the previous quarter. The increase resulted primarily from balance increases to VantageScore® A and B segments, which increased 31 percent and 11 percent, respectively. Download the latest Experian industry white papers. VantageScore® is owned by VantageScore Solutions, LLC.

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.

The economy is accelerating at a sluggish pace, and world headlines cause business leaders to swing between optimism and pessimism daily. Risk managers must look more closely and much more frequently at their customers' behavior to stay ahead of emerging credit problems. Some tips: Use all customer information when making decisions. Combining both internal and external data can paint a clearer picture of your customers. Identify the customer relationships that have value and should be retained. Apply resources accordingly. Implement daily triggers so you have the latest customer information around bankruptcy, repossession or loan delinquency, as well as positive information such as payments made to other financial institutions. Spend more time examining consumers who are delinquent on their home mortgage payments to determine their behavior on your portfolio. Use next-generation collections software to keep collectors up to date on account-level strategies. Download our white paper on how changes in the economy have impacted consumer credit behavior and what risk managers should analyze in order to determine portfolio strategies. Source: Experian News

Despite low demand and a shrinking pool of qualified candidates, loan growth priorities continue to rank high for most small-business lenders – both for small to midsize banks and large financial institutions. Between 2006 and 2010, overall loan applications were down 5 percent where large financial institutions saw small-business loan applications rise 36.5 percent. Banks and credit unions with assets less than $500 million showed the most significant increase of 65 percent. Read more of the blog series: "Getting back in the game – Generating small business applications." Source: Decision Analytics' Blog: Relationship and Transactional Lending Best Practices

Last month, I wrote about seeking ways to ensure growth without increasing risk.  This month, I’ll present a few approaches that use multiple scores to give a more complete view into a consumer’s true profile. Let’s start with bankruptcy scores. You use a risk score to capture traditional risk, but bankruptcy behavior is significantly different from a consumer profile perspective. We’ve seen a tremendous amount of bankruptcy activity in the market. Despite the fact that filings were slightly lower than 2010 volume, bankruptcies remain a serious threat with over 1.3 million consumer filings in 2011; a number that is projected for 2012.  Factoring in a bankruptcy score over a traditional risk score, allows better visibility into consumers who may be “balance loading”, but not necessarily going delinquent, on their accounts. By looking at both aspects of risk, layering scores can identify consumers who may look good from a traditional credit score, but are poised to file bankruptcy. This way, a lender can keep their approval rates up and lower risk of overall dollar losses. Layering scores can be used in other areas of the customer life cycle as well. For example, as new lending starts to heat up in markets like Auto and Bankcard, adding a next generation response score to a risk score in your prospecting campaigns, can translate into a very clear definition of the population you want to target. By combining a prospecting score with a risk score to find credit worthy consumers who are most likely to open, you help mitigate the traditional inverse relationship between open rates and credit worthiness. Target the population that is worth your precious prospecting resources. Next time, we’ll look at other analytics that help complete our view of consumer risk. In the meantime, let me know what scoring topics are on your mind.

The strongest growth in new bankcard accounts is occurring in the near-prime and subprime segments of VantageScore® credit score C, D and F. Year-over-year (Q1 2011 over Q1 2010) growth rates of 20 percent, 46 percent and 53 percent were observed for each of the respective tiers. Listen to our recent webinar featuring bankcard credit trends Source: Experian-Oliver Wyman Market Intelligence Reports