Debt & Collections
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 previous two blogs, I introduced the definition of strategic default and compared and contrasted the population to other types of consumers with mortgage delinquency. I also reviewed a few key characteristics that distinguish strategic defaulters as a distinct population. Although I’ve mentioned that segmenting this group is important, I would like to specifically discuss the value of segmentation as it applies to loan modification programs and the selection of candidates for modification. How should loan modification strategies be differentiated based on this population? By definition, strategic defaulters are more likely to take advantage of loan modification programs. They are committed to making the most personally-lucrative financial decisions, so the opportunity to have their loan modified - extending their ‘free’ occupancy – can be highly appealing. Given the adverse selection issue at play with these consumers, lenders need to design loan modification programs that limit abuse and essentially screen-out strategic defaulters from the population. The objective of lenders when creating loan modification programs should be to identify consumers who show the characteristics of cash-flow managers within our study. These consumers often show similar signs of distress as the strategic defaulters, but differentiate themselves by exhibiting a willingness to pay that the strategic defaulter, by definition, does not. So, how can a lender make this identification? Although these groups share similar characteristics at times, it is recommended that lenders reconsider their loan modification decisioning algorithms, and modify their loan modification offers to screen out strategic defaulters. In fact, they could even develop programs such as equity-sharing arrangements whereby the strategic defaulter could be persuaded to remain committed to the mortgage. In the end, strategic defaulters will not self-identify by showing lower credit score trends, by being a bank credit risk, or having previous bankruptcy scores, so lenders must create processes to identify them among their peers. For more detailed analyses, lenders could also extend the Experian-Oliver Wyman study further, and integrate additional attributes such as current LTV, product type, etc. to expand their segment and identify strategic defaulters within their individual portfolios.
In my last blog, I discussed the presence of strategic defaulters and outlined the definitions used to identify these consumers, as well as other pools of consumers within the mortgage population that are currently showing some measure of mortgage repayment distress. In this section, I will focus on the characteristics of strategic defaulters, drilling deeper into the details behind the population and learning how one might begin to recognize them within that population. What characteristics differentiate strategic defaulters? Early in the mortgage delinquency stage, mortgage defaulters and cash flow managers look quite similar – both are delinquent on their mortgage, but are not going bad on any other trades. Despite their similarities, it is important to segment these groups, since mortgage defaulters are far more likely to charge-off and far less likely to cure than cash flow managers. So, given the need to distinguish between these two segments, here are a few key measures that can be used to define each population. Origination VantageScore® • Despite lower overall default rates, prime and super-prime consumers are more likely to be strategic defaulters Origination Mortgage Balance • Consumers with higher mortgage balances at origination are more likely to be strategic defaulters, we conclude this is a result of being further underwater on their real estate property than lower-balance consumers Number of Mortgages • Consumers with multiple first mortgages show higher incidence of strategic default. This trend represents consumers with investment properties making strategic repayment decisions on investments (although the majority of defaults still occur on first mortgages where the consumer has only one first mortgage) Home Equity Line Performance • Strategic defaulters are more likely to remain current on Home Equity Lines until mortgage delinquency occurs, potentially a result of drawing down the HELOC line as much as possible before becoming delinquent on the mortgage Clearly, there are several attributes that identify strategic defaulters and can assist in differentiating them from cash flow managers. The ability to distinguish between these two populations is extremely valuable when considering its usefulness in the application of account management and collections management, improving collections, and loan modification, which is my next topic. Source: Experian-Oliver Wyman Market Intelligence Reports; Understanding strategic default in mortgage topical study/webinar, August 2009.
By: Wendy Greenawalt In my last blog on optimization we discussed how optimized strategies can improve collection strategies. In this blog, I would like to discuss how optimization can bring value to decisions related to mortgage delinquency/modification. Over the last few years mortgage lenders have seen a sharp increase in the number of mortgage account delinquencies and a dramatic change in consumer mortgage payment trends. Specifically, lenders have seen a shift in consumer willingness from paying their mortgage obligation first, while allowing other debts to go delinquent. This shift in borrower behavior appears unlikely to change anytime soon, and therefore lenders must make smarter account management decisions for mortgage accounts. Adding to this issue, property values continue to decline in many areas and lenders must now identify if a consumer is a strategic defaulter, a candidate for loan modification, or a consumer affected by the economic downturn. Many loans that were modified at the beginning of the mortgage crisis have since become delinquent and have ultimately been foreclosed upon by the lender. Making optimizing decisions related to collection action for mortgage accounts is increasingly complex, but optimization can assist lenders in identifying the ideal consumer collection treatment. This is taking place while lenders considering organizational goals, such as minimizing losses and maximizing internal resources, are retaining the most valuable consumers. Optimizing decisions can assist with these difficult decisions by utilizing a mathematical algorithm that can assess all possible options available and select the ideal consumer decision based on organizational goals and constraints. This technology can be implemented into current optimizing decisioning processes, whether it is in real time or batch processing, and can provide substantial lift in prediction over business as usual techniques.
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: Wendy Greenawalt In the second installment of my three part series, dispelling credit attribute myths, we will discuss why attributes with similar descriptions are not always the same. The U.S. credit reporting bureaus are the most comprehensive in the world. Creating meaningful attributes requires extensive knowledge of the three credit bureaus’ data. Ensuring credit attributes are up-to-date and created by informed data experts. Leveraging complete bureau data is also essential to obtaining long-term strategic success. To illustrate why attributes with similar names may not be the same let’s discuss a basic attribute, such as “number of accounts paid satisfactory.” While the definition, may at first seem straight forward, once the analysis begins there are many variables that must be considered before finalizing the definition, including: Should the credit attributes include trades currently satisfactory or ever satisfactory? Do we include paid charge-offs, paid collections, etc.? Are there any date parameters for credit attributes? Are there any trades that should be excluded? Should accounts that have a final status of \"paid” be included? These types of questions and many others must be carefully identified and assessed to ensure the desired behavior is captured when creating credit attributes. Without careful attention to detail, a simple attribute definition could include behavior that was not intended. This could negatively impact the risk level associated with an organization’s portfolio. Our recommendation is to complete a detailed analysis up-front and always validate the results to ensure the desired outcome is achieved. Incorporating this best practice will guarantee that credit attributes created are capturing the behavior intended.
--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.
By: Tracy Bremmer Preheat the oven to 350 degrees. Grease the bottom of your pan. Mix all of your ingredients until combined. Pour mixture into pan and bake for 35 minutes. Cool before serving. Model development, whether it is a custom or generic model, is much like baking. You need to conduct your preparatory stages (project design), collect all of your ingredients (data), mix appropriately (analysis), bake (development), prepare for consumption (implementation and documentation) and enjoy (monitor)! This blog will cover the first three steps in creating your model! Project design involves meetings with the business users and model developers to thoroughly investigate what kind of scoring system is needed for enhanced decision strategies. Is it a credit risk score, bankruptcy score, response score, etc.? Will the model be used for front-end acquisition, account management, collections or fraud? Data collection and preparation evaluates what data sources are available and how best to incorporate these data elements within the model build process. Dependent variables (what you are trying to predict) and the type of independent variables (predictive attributes) to incorporate must be defined. Attribute standardization (leveling) and attribute auditing occur at this point. The final step before a model can be built is to define your sample selection. Segmentation analysis provides the analytical basis to determine the optimal population splits for a suite of models to maximize the predictive power of the overall scoring system. Segmentation helps determine the degree to which multiple scores built on an individual population can provide lift over building just one single score. Join us for our next blog where we will cover the next three stages of model development: scorecard development; implementation/documentation; and scorecard monitoring.
Back during World War I, the concept of “triage” was first introduced to the battlefield. Faced with massive casualties and limited medical resources, a system was developed to identify and select those who most needed treatment and who would best respond to treatment. Some casualties were tagged as terminal and received no aid; others with minimal injuries were also passed over. Instead, medical staff focused their attentions on those who required their services in order to be saved. These were the ones who needed and would respond to appropriate treatment. Our clients realize that the collections battlefield of today requires a similar approach. They have limited resources to face this mounting wave of delinquencies and charge offs. They also realize that they can’t throw bodies at this problem. They need to work smarter and use data and decisioning more effectively to help them survive this collections efficiency battle. Some accounts will never “cure” no matter what you do. Others will self-cure with minimal or no active effort. Taking the right actions on the right accounts, with the right resources, at the right time is best accomplished with advanced segmentation that employs behavioral scoring, bureau-based scores and other relevant account data. The actual data and scores that should be used depend on the situation and account status, and there is no one-size-fits-all approach.
In addition to behavioral models, collections and account management groups need the ability to implement collections workflow strategies in order to effectively handle and process accounts, particularly when the optimization of resources is a priority. While the behavioral models will effectively evaluate and measure the likelihood that an account will become delinquent or result in a loss, strategies are the specific actions taken, based on the score prediction, as well as other key information that is available when those actions are appropriate. Identifying high-risk accounts, for example, may result in strategies designed to accelerate collections management activity and execute more aggressive actions. On the other hand, identifying low-risk accounts can help determine when to take advantage of cost-saving actions and focus on customer retention programs. Effective strategies also address how to handle accounts that fall between the high- and low-risk extremes, as well as accounts that fall into special categories such as first payment defaults, recently delinquent accounts and unique customer or product segments. To accommodate lenders with systems that cannot support either behavioral scorecards or strategies, Experian developed the powerful service bureau solution, Portfolio Management Package, which is also referred to as PMP. To use this service, lenders send Experian customer master file data on a daily basis. Experian processes the data through the Portfolio Management Package system which includes calculating Fast Start behavior scores and identifying special handling accounts and electronically delivers the recommended strategies and actions codes within hours. Scoring and strategy parameters can be easily changed, as well as portfolio segmentation, special handling options and scorecard selections. PMP also supports Champion Challenger testing to enable users to learn which strategies are most effective. Comprehensive reports suites provide the critical information needed for lenders to design strategies and evaluate and compare the performance of those strategies.
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