Debt & Collections

According to a recent Ponemon Institute study, 65 percent of study participants say their organization has had a data breach in the past two years involving consumer data outsourced to a third party. Most of these are preventable, as employee negligence accounts for 45 percent of data breaches and lost or stolen devices account for 40 percent.

All skip tracing data is the same, right? Not exactly. While there are many sources of consumer contact data available to debt collectors, the quality, freshness, depth and breadth can vary significantly. Just as importantly, what you ultimately do or don't do with the data depends on several factors such as: Whether or not the debt is worth your while to pursue How deep and fresh the data is What if no skip data is available, and, What happens if there is no new information available when you go to your skip-tracing vendor requesting new leads? So what's the best way for your company to locate debtors? What data sources are right for you? Check out my recent article in Collections and Credit Risk for some helpful advice, and be sure to check out our other debt collection industry blog posts for best practices, tips and tricks on ways to recover more debt, faster. What data sources do you find most beneficial to your business and why? Let us know by commenting below.

Six states are the top producers of turkeys: Minnesota at 46 million, North Carolina at 36 million, Arkansas at 29 million, Missouri at 17.5 million, Virginia at 17 million and Indiana at 16.5 million. This accounts for nearly two-thirds of turkeys produced in the United States as of September 2012. The average wholesale price for frozen whole turkey during fourth-quarter 2012 is projected to range from $1.10 to $1.14 per pound -- similar to the 2011 fourth-quarter average price of $1.11 per pound. The average retail price for whole frozen turkeys in September 2012 was $1.62, about 6 cents lower than the average retail price for whole frozen turkeys in September 2011. Source: National Agricultural Statistics Service (NASS), Agricultural Statistics Board and United States Department of Agriculture (USDA).

According to a recent Ponemon Institute study, 44 percent of consumers who were notified about a data breach believed the breached company was hiding something. When data breaches occur, it is extremely important to be there for customers and to address their concerns. When companies hide a data breach, impacted consumers begin to suspect the breach is actually much worse than the company claims, and trust in the organization begins to wane. Find out more by downloading the data breach case study of lessons learned from the field.

It comes as no surprise to anyone that cell phone usage continues to rise, while at the same time the usage of wire lines, or what used to be affectionately known as POTS (Plain Old Telephone Service), continues to decline. Some recent statistics, supplied by the CDC show that: 34% of all households are now wireless only 25 states have rates of primary wireless exceeding 50% Landline only households is now down to only 10.2% When you couple that with churn rates for cell phones that can exceed 40% a year, it becomes paramount to find a good source for cell numbers if you are trying to contact an existing customer or collect on an overdue bill. But where can debt collectors go to find reliable cell phone numbers? The cell phone providers won’t sell you a database, there is no such thing as 411 for cell phones, nor is it likely there will be one in the near future with the aforementioned 40%+ churn rates. Each cell phone service provider will continue to protect their customer base. There are a few large compilers of cell phone numbers; they mostly harvest these numbers from surveys and sources that capture the numbers as a part of an online service—think ringtones here! These numbers can be good, at least initially, if they came with an address which enables you to search for them. The challenge is that these numbers can grow stale relatively quickly. Companies that maintain recurring transactions with consumers have a better shot at having current cell numbers. Utilities and credit bureaus offer an opportunity to capture these self-reported numbers. At our company, over 40% of self-reported phones are cell phones. However, in most cases, you must have a defined purpose as governed by Gramm Leach Bliley (GLB) in order to access them. Of course, the defined purpose also goes hand in hand with the Telephone Consumer Protection Act (TCPA), which restricts use of automatic dialers and prohibits unsolicited calls via a cell phone. Conclusion? If you are trying to find someone’s cell number for debt collection purposes, I recommend using a resource more likely to receive updates on the owner of a cell over that of compilers who are working with one time event data. In today’s world, obtaining an accurate good cell number is a challenge and will continue to be. What cell phone number resources have been most effective for you?

By: Kyle Aiman Let’s face it, debt collectors often get a bad rap.  Sure, some of it is deserved, but the majority of the nation’s estimated 157,000 collectors strive to do their job in a way that will satisfy both their employer and the debtor.  One way to improve collector/debtor interaction is for the collector to be trained in consumer credit and counseling. In a recent article published on Collectionsandcreditrisk.com, Trevor Carone, Vice President of Portfolio and Collection Solutions at Experian, explored the concept of using credit education to help debt collectors function more like advisors instead of accusers.  If collectors gain a better understanding of consumer credit – how to read a credit report, how items may affect a credit score, how a credit score is compiled and what factors influence the score – perhaps they can offer suggestions for improvement. Will providing past-due consumers with a plan to help improve their credit increase payments?  Read the article and let us know what you think!

By: Mike Horrocks It has been over a year that in Zuccotti Park the Occupy Wall Street crowd made their voices heard.  At the anniversary point of that movement, there has been a lot of debate on if the protest has fizzled away or is still alive and planning its next step.  Either way, it cannot be ignored that it did raise a voice in how consumers view their financial institutions and what actions they are willing to take i.e. “Bank Transfer Day”. In today’s market customer risk management must be balanced with retention strategies.  For example, here at Experian we value the voice of our clients and prospects and I personally lead our win/loss analysis efforts.  The feedback we get from our customers is priceless.   In a recent American Banker article, some great examples were given on how tuning into the voice of the consumer can turn into new business and an expanded market footprint. Some consumers however will do their talking by looking at other financial institutions or by slowly (or maybe rapidly) using your institution’s services less and less.   Technology Credit Union saw great results when they utilized retention triggers off of the credit data to get back out in front of their members with meaningful offers.     Maximizing the impact of internal data and spotting the customer-focused trends that can help with retention is even a better approach, since that data is taken at the “account on-us” level and can help stop risks before the customer starts to walk out the door. Phillip Knight, the founder of Nike once said, “My job is to listen to ideas”.  Your customers have some of the best ideas on how they can be retained and not lost to the competitors.  So, think how you can listen to the voice and the actions of your customers better, before they leave and take a walk in the park.

By: Maria Moynihan State and local governments responsible for growth may be missing out on an immediate and sizeable revenue opportunity if their data and processes for collections are not up to par. The Experian Public Sector team recently partnered with Governing Magazine to conduct a nationwide survey with state and local government professionals to better understand how their debt collections efforts are helping to address current revenue gaps. Interestingly enough, 81% stated that the economic climate has negatively impacted their collections efforts, either through reduced staff or reduced budgets, while 30% of respondents are actively looking for new technologies to aid in their debt collections processes. New technologies are always a worthwhile investment. Operational efficiencies will ultimately ensue, but those government organizations who are coupling this investment with improved data and analytics are even better positioned to optimize collections processes and benefit from growth in revenue streams. No longer does the public sector need to lag behind the private sector in debt recovery. With the total outstanding debt among the 50 states reaching an astounding size of approximately $631 billion dollars, why delay? Check out Experian's guide to improving debt collections efforts in the public sector. What is your agency doing to capitalize on revenue from overdue obligations?

By: Kyle Aiman For more than 20 years, creditors have been using scores in their lending operations.  They use risk models such as the VantageScore® credit score, FICO or others to predict what kind of risk to expect before making credit-granting decisions. Risk models like these do a great job of separating the “goods” from the “bads.” Debt recovery models are built differently-their job is to predict who is likely to pay once they have already become delinquent. While recovery models have not been around as long as risk models, recent improvements in analytics are producing great results.  In fact, the latest generation of recovery models can even predict who will pay the most. Hopefully, you are not using a risk model in your debt collection operations.  If you are, or if you are not using a model at all, here are five reasons to start using a recovery model: Increase debt recovery rates – Segmenting and prioritizing your portfolios will help increase recovery rates by allowing you to place emphasis on those accounts most likely to pay. Manage and reduce debt recovery costs – Develop treatment strategies of varying costs and apply appropriately. Do not waste time and money on uncollectible accounts. Outsource accounts to third party collection agencies – If you use outside agencies, use recovery scoring to identify accounts best suited for assignment; take the cream off the top to keep in house. Send accounts to legal – Identify accounts that would be better served using a legal strategy versus spending time and money using traditional treatments. Price accounts appropriately for sale – If you are in a position to sell accounts, recovery scoring can help you develop a pricing strategy based on expected collectibility. What recovery scoring tools are you using to optimize your company's debt collection efforts? Feel free to ask questions or share your thoughts below.   VantageScore® is a registered trademark of VantageScore Solutions, LLC.

Consumers want to hear about data breaches - Eighty five percent of respondents in a recent study say learning about the loss of their data is pertinent to them. However, when they do, 72 percent indicated that they are dissatisfied with the notification letters they receive. Companies need to take note of these findings because more than one-third of consumers who receive a notification letter contemplate ending their relationship with the company. Providing affected individuals with a membership in an identity protection product is extremely important since 58 percent of consumers consider identity protection to be favorable compensation after a breach. Learn five pitfalls to avoid in your notification letters and how Experian Data Breach Resolution can help. Source: Download the complete 2012 consumer study on data breach notification.

2011 was the 12th consecutive year that identity theft topped the list of FTC consumer complaints. Florida had the highest rate of complaints, followed by Georgia and California. Rank State Complaints per 100,000 population 1 Florida 179 2 Georgia 120 3 California 104 Learn how to detect and manage fraud activity while meeting regulatory requirements. Source: Consumer info.com infographic and FTC's Consumer Sentinel Network Data Book for January-December 2011.

The CFPB, the FTC and other regulatory authorities have been building up their presence in debt collections. Are you in the line of fire, or are you already prepared to effectively manage your riskiest accounts?  This year’s collections headlines show an increased need to manage account risk. Consumers have been filing suits for improper collections under the Fair Debt Collection Practices Act (FDCPA), the Servicemembers Civil Relief Act (SCRA), and the Telephone Consumer Protection Act (TCPA), to name a few. Agencies have already paid millions in fines due to increased agency scrutiny.   One collections mistake could cost thousands or even millions to your business—a cost any collector would hate to face. So, what can you do about better managing your regulatory risk?  1.       First of all, it is always important to understand and follow the collection regulations associated with your accounts. 2.       Secondly, follow the headlines and pay close attention to your regulatory authorities.  3.       Lastly, leverage data filtering tools to identify accounts in a protected status. The best solution to help you is a streamlined tool that includes filters to identify multiple types of regulatory risk in one place. At minimum, you should be able to identify the following types of risk associated with your accounts: Bankruptcy status and details Deceased indicator and dates Military indicator Cell phone type indicator Fraud indicators Litigious consumers Why wait? Start identifying and mitigating your risk as early in your collections efforts as possible. 

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  

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 Consumer Financial Protection Bureau (CFPB) now has the ability to write and enforce 18 consumer protection laws that guide financial products and services. The new regulator has signaled the following issues as priorities: Clarity on how credit scores affect lender decisions: Beginning July 21, 2011, lenders were required to disclose the credit score that they used in all risk-based pricing notices and adverse action notices Shorter and simpler consumer disclosure forms: One of the first priorities is to make the terms and conditions associated with purchasing a mortgage or applying for a credit card shorter and clearer Enforcing the Fair Debt Collection Practices Act: The CFPB will enforce the Fair Debt Collection Practices Act and review current debt collector practices Learn more about the CFPB