Collections & Debt Recovery

With cell phones overtaking landlines as the new “home phone” for many consumers, things could get tricky for credit card holders and other debtors as well as the creditors who need to reach them. The Federal Communications Commission wants to limit the ability of collectors to use autodialers to call cell phones. But the unintended consequences could make credit more costly as well as harder to get for younger customers. The FCC’s proposed revision At issue is a proposed FCC action to revise the Telephone Consumer Protection Act (TCPA) of 1991 in an effort to align its regulations with Federal Trade Commission rules. The do-not-call rules already restrict telemarketers from calling cell phones. But the new FCC revisions would cover any call to a cell phone, including legitimate calls to collect a debt, notify a customer of a payment due, or request additional information to complete an application. Confusion about consent Businesses are puzzled at how compliance might work under the new rule. If approved, the proposed rule would no longer permit creditors to call a customer’s cell phone when the cell number was filled in on an application. The proposed rule changes the definition of what constitutes prior consent. Just having a phone number on an application wouldn’t be sufficient. Companies would be required to have written permission, such as “I consent to calling my cell phone when there’s a problem…” When a cell phone is the only phone This raises new issues. For instance, if a consumer needs to be contacted, but the company doesn’t know the cell phone is the only line, the company could still be liable for calling it. What now? The good news is that this issue hasn’t moved anywhere over the last year. The rule was proposed in March of 2010 and comments were accepted up to last May, but nothing has happened since. From a regulatory perspective, the level of industry concern over the FCC’s proposed rule warrants some caution. While some form of revision could still go forward, the modification may not be in line with FTC rules. Are you concerned about the FCC’s proposed cell phone rule? Let us know if you’ve developed contingencies in case it’s approved. We’ll be sure to keep you up to date on any new developments, so watch this space for updates. For further reading on this issue: FCC Cell Phone Rule Would Raise Risk Debt Collectors Seek Right to 'Robocall' Cell Phones

There’s no question times have been tough for consumers in the last few years due to the higher incidence of unemployment, bankruptcies, home foreclosures and increased credit balances. Unfortunately, these issues have a way of trickling down to communication companies’ collection departments, many of which are scrambling with heavier workloads and fewer resources. The key for cable, wireless, and telecom companies like yours is to prioritize your collection portfolio by first contacting the people most likely to pay. Once you’ve identified these people, your next task is to access and record any changes to their accounts, such as a new phone number or any improvements to their credit profile. But how can you get these updates without having to check their credit reports on a regular basis? Trigger program to the rescue By scrapping the usual manual skip tracing activities and using a “trigger” program, telecom industry collection staff can proactively obtain information as fresh as 24 hours old. Most trigger programs allow you to monitor any type of data, such as phone numbers, addresses, or places of employment. You can even use events, such as a change in the debtor’s financial status, to trigger an alert. This is especially helpful for cases in which your collection team has the right contact information, but the customer does not have the ability to pay. Being the first to contact the debtor when he or she again has money is crucial, because many collectors are likely competing for these funds to pay off debt. Save time, save money Most trigger program providers will monitor your portfolio for free, only charging on a per-trigger basis. Not only does this save valuable collector time, it also avoids the expense of pulling a full credit report on the consumer (and hoping that the information was recently updated). As more and more of your collection accounts become active again, and your customers’ credit improves, a trigger program helps your company be first in line to contact them for repayment. To learn more about how collection account monitoring tools can benefit your company, read our case study about how accounts receivable management firm First Financial Asset Management, Inc. was able to increase its collections by $3.5 million—a return of $72 for every $1 spent on trigger data.

This paraphrased lament from Coleridge’s Rime of the Ancient Mariner may loosely reflect the predicament facing many communications companies today: afloat on vast sea of customer information, yet, lacking resources or expertise, unable to draw from it much new or actionable intelligence. Not that data mining is ever a small or insignificant task. It isn’t. Even when resources are plentiful, obstacles can loom large—especially across numerous lines of business, where risk can multiply exponentially. Siloed data, disparate customer records and other challenges also make the work difficult, as do: The dynamic nature of consumer information Inconsistent data quality and match logic throughout the enterprise The inability to reliably link active and inactive accounts failing to identify existing customer relationships at the point of application The missing link Experian has seen many communications companies overcome these issues through database linking—that is, connecting, integrating and packaging customer information from several sources into a more cohesive and accessible structure. Linking reduces risk by identifying overlap of consumers with multiple accounts across several lines of business. It also reveals duplicate records, as well as active accounts that may be current in one line of business, but delinquent or inactive in another. The benefits The broader perspective gained through database linking can drive new efficiencies and profitability in many vital areas of your business, from fraud prevention to skip tracing and collections. Should the need arise, newly linked information can also be used to locate elusive customers or former employees for legal purposes. What you can do right now Even if resources are currently limited you can still begin discovery—the process of identifying precisely what data you have, where it resides within the enterprise, how it’s being used, and by whom. This information, perhaps combined with guidance from an experienced external service, can provide a solid foundation from which to begin leveraging (and if indicated, supplementing) existing customer data. We know communications clients who have identified millions of dollars in uncollected bad debt that was linked directly to current, active customers, using a couple of “next generation” data tools. Like the old Mariner, your in-house data has a big story to tell. Question is, are you equipped to hear it? If you like this topic, click here to read the post entitled “Leveraging Internal Data to Create a Holistic View of Your Customers.

We’ve written a number of posts suggesting how telecom and cable providers can use reliable consumer credit data to improve acquisition, prospecting, retention and risk mitigation, but we haven’t yet covered collections.  So here is the first in what will probably be several collections-related entries. Please let us know what you think. What’s in your data mix? As you know, the ranks of “skips” or delinquent accounts have grown considerably over the past few years. This phenomenon has compelled many telecom and cable companies to reevaluate the quality and use of their skip tracing data. What they’ve discovered is that contact data alone—current address, previous addresses, land line and cell phone numbers—may not be enough to recover lost payments. Which brings us to the missing ingredient. Reliable consumer credit data Combining fresh contact data AND reliable consumer credit data enables you to recapture more funds from more delinquent accounts. Adding credit history to the mix broadens your view of consumers’ overall financial health, allowing you easily distinguish between those who can pay and those who can’t. What the data can reveal Assuming your consumer credit data comes from a reputable, knowledgeable source, you’ll be able to immediately learn a lot about your customers’ behaviors and circumstances, including: Attempts to open a new account while they’re still overdue with you Recently declared bankruptcies Once delinquent customers who now have the ability to pay Tying in “triggers” Some communications providers use collection tools called “triggers.” When tied to a consumer credit report, triggers alert you to new information, including cell or landline number, address, employer, or changes in financial status, such as when bankcard funds become newly available. Quality credit data + tools like triggers + a reliable data partner = a surefire recipe for collections success. Collections not your focus? Check out the post on “Using Data Intelligence to Reduce Churn, Build Loyalty and Keep the Right Customers.”

By: Kari Michel As consumers and businesses continue to experience financial hardship, the likelihood of continued bankruptcy filings is fairly strong. Data from the Administrative Office of the U.S. Courts show there were 1,222,589 filings through September, versus 1,100,035 in the first nine months of 2009. According to American Bankruptcy Institute executive director Samuel J. Gerdano, "As the economy looks to climb out of the recent recession, businesses and consumers continue to file for bankruptcy to regain their financial footing. With unemployment hovering near 10% and access to credit remaining tight, total filings in 2010 will likely exceed 1.6 million." Given the bankruptcy trends, what can lenders do to protect themselves from acquiring consumers that are at risk for filing for bankruptcy? Bankruptcy scores are available, such as Bankruptcy PLUS, and are developed to accurately identify characteristics specific to a consumer filing for bankruptcy. Bankruptcy scores are typically used in conjunction with risk scores to set effective acquisition strategies. _________________ Source:  http://www.collectionscreditrisk.com/news/bankruptcy-filings-up-3003998-1.html  

As the economic environment changes on what feels like a daily basis, the importance of having information about consumer credit trends and the future direction of credit becomes invaluable for planning and achieving strategic goals. I recently had the opportunity to speak with members of the collections industry about collections strategy and collections change management -- and discussed the use of business intelligence data in their industry. I was surprised at how little analysis was conducted in terms of anticipating strategic changes in economic and credit factors that impact the collections business. Mostly, it seems like anecdotal information and media coverage is used to get ‘a feeling’ for the direction of the economy and thus the collections industry. Clearly, there are opportunities to understand these high-level changes in more detail and as a result, I wanted to review some business intelligence capabilities that Experian offers – and to expand on the opportunities I think exist to for collections firms to leverage data and better inform their decisions: * Experian possesses the ability to capture the entire consumer credit perspective, allowing collections firms to understand trends that consider all consumer relationships. * Within each loan type, insights are available by analyzing loan characteristics such as, number of trades, balances, revolving credit limits, trade ages, and delinquency trends. These metrics can help define market sizes, relative delinquency levels and identify segments where accounts are curing faster or more slowly, impacting collectability. * Layering in geographic detail can reveal more granular segment trends, creating segments for both macro and regional-level credit characteristics. * Experian Business Intelligence has visibility to the type of financial institution, allowing for a market by market view of credit patterns and trends. * Risk profiling by VantageScore can shed light on credit score trends, breaking down larger segments into smaller score-based segments and identifying pockets of opportunity and risk. I’ll continue to consider the opportunities for collections firms to leverage business intelligence data in subsequent blogs, where I’ll also discuss the value of credit forecasting to the collections industry.  

By: Wendy Greenawalt Given the current volatile market conditions and rising unemployment rates, no industry is immune from delinquent accounts. However, recent reports have shown a shift in consumer trends and attitudes related to cellular phones. For many consumers, a cell phone is an essential tool for business and personal use, and staying connected is a very high priority. Given this, many consumers pay their cellular bill before other obligations, even if facing a poor bank credit risk. Even with this trend, cellular providers are not immune from delinquent accounts and determining the right course of action to take to improve collection rates. By applying optimization, technology for account collection decisions, cellular providers can ensure that all variables are considered given the multiple contact options available. Unlike other types of services, cellular providers have numerous options available in an attempt to collect on outstanding accounts.  This, however, poses other challenges because collectors must determine the ideal method and timing to attempt to collect while retaining the consumers that will be profitable in the long term.  Optimizing decisions can consider all contact methods such as text, inbound/outbound calls, disconnect, service limitation, timing and diversion of calls.  At the same time, providers are considering constraints such as likelihood of curing, historical consumer behavior, such as credit score trends, and resource costs/limitations.  Since the cellular industry is one of the most competitive businesses, it is imperative that it takes advantage of every tool that can improve optimizing decisions to drive revenue and retention.  An optimized strategy tree can be easily implemented into current collection processes and provide significant improvement over current processes.

By: Kari Michel   Lenders are looking for ways to improve their collections strategy as they continue to deal with unprecedented consumer debt, significant increases in delinquency, charge-off rates and unemployment and, declining collectability on accounts. Improve collections To maximize recovered dollars while minimizing collections costs and resources, new collections strategies are a must. The standard assembly line “bucket” approach to collection treatment no longer works because lenders can not afford the inefficiencies and costs of working each account equally without any intelligence around likelihood of recovery. Using a segmentation approach helps control spend and reduces labor costs to maximize the dollars collected. Credit based data can be utilized in decision trees to create segments that can be used with or without collection models. For example, below is a portion of a full decision tree that shows the separation in the liquidation rates by applying an attribute to a recovery score This entire segment has an average of 21.91 percent liquidation rate. The attribute applied to this score segment is the aggregated available credit on open bank card trades updated within 12 months. By using just this one attribute for this score band, we can see that the liquidation rates range from 11 to 35 percent. Additional attributes can be applied to grow the tree to isolate additional pockets of customers that are more recoverable, and identify segments that are not likely to be recovered. From a fully-developed segmentation analysis, appropriate collections strategies can be determined to prioritize those accounts that are most likely to pay, creating new efficiencies within existing collection strategies to help improve collections.

In my last blog, I discussed the basic concept of a maturation curve, as illustrated below: Exhibit 1 In Exhibit 1, we examine different vintages beginning with those loans originated by year during Q2 2002 through Q2 2008. The purpose of the vintage analysis is to identify those vintages that have a steeper slope towards delinquency, which is also known as delinquency maturation curve. The X-axis represents a timeline in months, from month of origination.  Furthermore, the Y-axis represents the 90+ delinquency rate expressed as a percentage of balances in the portfolio.  Those vintage analyses that have a steeper slope have reached a normalized level of delinquency sooner, and could in fact, have a trend line suggesting that they overshoot the expected delinquency rate for the portfolio based upon credit quality standards. So how can you use a maturation curve as a useful portfolio management tool? As a consultant, I spend a lot of time with clients trying to understand issues, such as why their charge-offs are higher than plan (budget).  I also investigate whether the reason for the excess credit costs are related to collections effectiveness, collections strategy, collections efficiency, credit quality or a poorly conceived budget. I recall one such engagement, where different functional teams within the client’s organization were pointing fingers at each other because their budget evaporated. One look at their maturation curves and I had the answers I needed. I noticed that two vintages per year had maturation curves that were pointed due north, with a much steeper curve than all other months of the year. Why would only two months or vintages of originations each year be so different than all other vintage analyses in terms of performance? I went back to my career experiences in banking, where I worked for a large regional bank that ran marketing solicitations several times yearly. Each of these programs was targeted to prospects that, in most instances, were out-of-market, or in other words, outside of the bank’s branch footprint. Bingo! I got it! The client was soliciting new customers out of his market, and was likely getting adverse selection. While he targeted the “right” customers – those with credit scores and credit attributes within an acceptable range, the best of that targeted group was not interested in accepting their offer, because they did not do business with my client, and would prefer to do business with an in-market player. Meanwhile, the lower grade prospects were accepting the offers, because it was a better deal than they could get in-market. The result was adverse selection...and what I was staring at was the "smoking gun" I’d been looking for with these two-a-year vintages (vintage analysis) that reached the moon in terms of delinquency. That’s the value of building a maturation curve analysis – to identify specific vintages that have characteristics that are more adverse than others.  I also use the information to target those adverse populations and track the performance of specific treatment strategies aimed at containing losses on those segments. You might use this to identify which originations vintages of your home equity portfolio are most likely to migrate to higher levels of delinquency; then use credit bureau attributes to identify specific borrowers for an early lifecycle treatment strategy. As that beer commercial says – “brilliant!”  

--by Jeff Bernstein In the current economic environment, many lenders and issuers across the globe are struggling to manage the volume of caseloads coming into collections. The challenge is that as these new collection cases come into collections in early phases of delinquency, the borrower is already in distress, and the opportunity to have a good outcome is diminished. One of the real “hot” items on the list of emerging best practices and innovating changes in collections is the concept of early lifecycle treatment strategy. Essentially, what we are referring to is the treatment of current and non-delinquent borrowers who are exhibiting higher risk characteristics.  There are also those who are at-risk of future default at higher levels than average. The challenge is how to identify these customers for early intervention and triage in the collections strategy process. One often-overlooked tool is the use of maturation curves to identify vintages within a portfolio that is performing worse than average. A maturation curve identifies how long from origination until a vintage or segment of the portfolio reaches a normalized rate of delinquency. Let’s assume that you are launching a new credit product into the marketplace. You begin to book new loans under the program in the current month. Beyond that month, you monitor all new loans that were originated/booked during that initial time frame which we can identify as a “vintage” of the portfolio. Each month’s originations are a separate vintage or vintage analysis, and we can track the performance of each vintage over time. How many months will it take before the “portfolio” of loans booked in that initial month reach a normal level of delinquency based on these criteria: the credit quality of the portfolio and its borrowers, typical collections servicing, delinquency reporting standards, and factor of time?  The answer would certainly depend upon the aforementioned factors, and could be graphed as follows:   Exhibit 1        In Exhibit 1, we examine different vintages beginning with those loans originated during Q2 2002, and by year Q2 2008. The purpose of the analysis is to identify those vintages that have a steeper slope towards delinquency, which is also known as a delinquency maturation curve.  The X-axis represents a timeline in months, from month of origination.  Furthermore,, the Y-axis represents the 90+ delinquency rate expressed as a percentage of balances in the portfolio. Those vintages that have a steeper slope have reached a normalized level of delinquency sooner, and could in fact, have a trend line suggesting that they overshoot the expected delinquency rate for the portfolio based upon credit quality standards. So how do we use the maturation curve as a tool? In my next blog, I will discuss how to use maturation curves to identify trends across various portfolios.  I will also examine differentiate collections issues from originations or lifecycle risk management opportunities.    

-- by Dan Buell Towards the end of 2007, the management of Bay Area Credit Service embarked on an agressive strategy to dramatically enhance the company's market position and increase its collection revenues.  These goals could be achieved only through superior performance at competitive rates.  At the same time, though, the company needed to drastically reduce internal operating expenses while facing significant competition.  The company's major goals for 208 included: *  Earn a much larger share of business from one of the nation's top five cellular phone service providers; *  Become a major collections partner for one of the nation's largest banking institutions; *  Earn more than 50 percent of the market in the pre-charge-off, early-out segment for the nation's largest landline communications provider; *  Enhance the company's position in the secondary collections tier. It's an interesting case study.  Navigate to the link to learn more: https://www.experian.com/whitepapers/index.html

--by Mike Sutton In today’s collections environment, the challenges of meeting an organization’s financial objectives are more difficult than ever.  Case volumes are higher, accounts are more difficult to collect and changing customer behaviors are rendering existing business models less effective. When responding to recent events, it is not uncommon for organizations to take what may seem to be the easiest path to success — simply hiring more staff. Perhaps in the short-term there may appear to be cash flow improvements, but in most cases, this is not the most effective way to cope with long-term business needs. As incremental staff is added to compensate for additional workloads, there is a point of diminishing return on investment and that can be difficult to define until after the expenditures have been made. Additionally, there are almost always significant operational improvements that can be realized by introducing new technology.  Furthermore, the relevant return on investment models often forecast very accurately. So, where should a collections department consider investing to improve financial results? The best option may not be the obvious choice, and the mere thought can make the most seasoned collections professionals shutter at the thought of replacing the core collections system with modern technology. That said, let’s consider what has changed in recent years and explore why the replacement proposition is not nearly as difficult or costly as in the past. Collection Management Software The collections system software industry is on the brink of a technology evolution to modern and next-generation offerings. Legacy systems are typically inflexible and do not allow for an effective change management program. This handicap leaves collections departments unable to keep up with rapidly changing business objectives that are a critical requirement in surviving these tough economic times. Today’s collections managers need to reduce operational costs while improving these objectives: reducing losses, improving cash flow and promoting customer satisfaction (particularly with those who pose a greater lifetime profit opportunity).  The next generation collections software squarely addresses these business problems and provides significant improvement over legacy systems. Not only is this modern technology now available, but the return on investment models are extremely compelling and have been proven in markets where successful implementations have already occurred. As an example of modern collections technologies that can help streamline operations, check out the overview and brief demonstration that is on this link: www.experian.com/decision-analytics/tallyman-demo.html.  

--by Mike Sutton I recently interviewed a number of Experian clients to determine how they believe their organizations and industry peers will prioritize collections process improvement over the next 24 months. Additional contributions were collected by written surveys. Here are several interesting observations:   Improve Collections survey results: Financial services professionals, in general, ranked “loss mitigation / risk management improvement” as the most critical area of focus. Credit unions were the financial services group’s exception and placed” customer relationship management / attrition control” at the top of their priority list. Healthcare providers ranked both “general delinquency management” and “improving cash flow / receivables” as their primary area of focus for the foreseeable future. Almost all of the first-party contributors, across all industries polled, ranked “operational expense management / cost reductions” as being very important or at least a high priority. This category was also rated the most critical by utilities. “External partner management (agencies, repo vendors and debt buyers)” also ranked high, but did not stand out on its own, as a top priority for any particular group.     All of the categories mentioned above were considered important by every respondent, but the most urgent priorities were not consistent across industries.        

By: Kari Michel In August, consumer bankruptcy filings were up by 24 percent over the past year and are expected to increase to 1.4 million this year.  “Consumers continue to turn to bankruptcy as a shield from the sustained financial pressures of today’s economy,” said American Bankruptcy Institute’s Executive Director Samuel J. Gerdano. What are lenders doing to protect themselves from bankruptcy losses? In my last blog, I talked about the differences and advantage of using both risk and bankruptcy scores. Many lenders are mitigating and managing bankruptcy losses by including bankruptcy scores into their standard account management programs. Here are some ways lenders are using bankruptcy scores: • Incorporating them into existing internal segmentation schemes for enhanced separation and treatment assessment of high risk accounts; • Developing improved strategies to act on high-bankruptcy-risk accounts • In order to manage at-risk consumers proactively and • Assessing low-risk customers for up-sell opportunities. Implementation of a bankruptcy score is recommended given the economic conditions and expected rise in consumer bankruptcy. When conducting model validations/assessments, we recommend that you use the model that best rank orders bankruptcy or pushes more bankruptcies into the lowest scoring ranges.  In validating our Experian/Visa BankruptcyPredict score, results showed BankruptcyPredict was able to identify 18 to 30 percent more bankruptcy compared to other bankruptcy models.  It also identified 12 to 33 percent more bankruptcy compared to risk scores in the lowest five percent of the score range.  This supports the need to have distinct bankruptcy scores in addition to risk scores.  

By: Kari Michel Bankruptcies continue to rise and are expected to exceed 1.4 million by the end of this year, according to American Bankruptcy Institute Executive Director, Samuel J. Gerdano.  Although, the overall bankruptcy rates for a lender’s portfolio is small (about 1 percent), bankruptcies result in high dollar losses for lenders.  Bankruptcy losses as a percentage of total dollar losses are estimated to range from 45 percent for bankcard portfolios to 82 percent for credit unions.  Additionally, collection activity is restricted because of legislation around bankruptcy.  As a result, many lenders are using a bankruptcy score in conjunction with their new applicant risk score to make better acquisition decisions. This concept is a dual score strategy.  It is key in management of risk, to minimize fraud, and in managing the cost of credit. Traditional risk scores are designed to predict risk (typically predicting 90 days past due or greater).  Although bankruptcies are included within this category, the actual count is relatively small.   For this reason the ability to distinguish characteristics typical of a “bankruptcy” are more difficult.  In addition, often times a consumer who filed bankruptcy was in “good standings” and not necessarily reflective of a typical risky consumer.   By separating out bankrupt consumers, you can more accurately identify characteristics specific to bankruptcy.  As mentioned previously, this is important because they account for a significant portion of the losses. Bankruptcy scores provide added value when used with a risk score. A matrix approach is used to evaluate both scores to determine effective cutoff strategies.   Evaluating applicants with both a risk score and a bankruptcy score can identify more potentially profitable applicants and more high- risk accounts.