Debt & Collections

--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.  

Optimization is a very broad and commonly used term today and the exact interpretation is typically driven by one's industry experience and exposure to modern analytical tools. Webster defines optimize as: "to make as perfect, effective or functional as possible". In the risk/collections world, when we want to optimize our strategies as perfect as technology will allow us, we need to turn to advanced mathematical engineering. More than just scoring and behavioral trending, the most powerful optimization tools leverage all available data and consider business constraints in addition to behavioral propensities for collections efficiency and collections management. A good example of how this can be leveraged in collections is with letter strategies. The cost of mailing letters is often a significant portion of the collections operational budget. After the initial letter required by the Fair Debt Collection Practice Act (FDCPA) has been sent, the question immediately becomes: “What is the best use of lettering dollars to maximize return?” With optimization technology we can leverage historical response data while also considering factors such as the cost of each letter, performance of each letter variation and departmental budget constraints, while weighing the alternatives to determine the best possible action to take for each individual customer. n short, cutting edge mathematical optimization technology answers the question: "Where is the point of diminishing return between collections treatment effectiveness and efficiency / cost?"  

Currently, financial institutions focus on the existing customer base and prioritize collections to recover more cash, and do it faster. There is also a need to invest in strategic projects with limited budgets in order to generate benefits in a very short term, to rationalize existing strategies and processes while ensuring that optimal decisions are made at each client contact point. To meet the present challenging conditions, financial institutions increasingly are performing business reviews with the goal of evaluating needs and opportunities to maximize the value created in their portfolios.  Business reviews assess an organization’s capacity to leverage on existing opportunities as well as identifying any additional capability that might be necessary to realize the increased benefits. An effective business review covers the following four phases: Problem definition: Establish and qualify what the key objectives of the organization are, the most relevant issues to address, the constraints of the solution, the criteria for success and to summarize how value management fits into the company’s corporate and business unit strategies. Benchmark against leading practice: Strategies, processes, tools, knowledge, and people have to be measured using a review toolset tailored to the organization’s strategic objectives. Define the opportunities and create the roadmap: The elements required to implement the opportunities and migrating to the best practice should be scheduled in a phased strategic roadmap that includes the implementation plan of the proposed actions. Achieve the benefits: An ROI-focused approach, founded on experience in peer organizations, will allow analysis of the cost-benefits of the recommended investments and quantify the potential savings and additional revenue generated. A continuous fine-tuning (i.e. impact of market changes, looking for the next competitive edge and proactively challenge solution boundaries) will ensure the benefits are fully achieved. Today’s blog is an extract of an article written by Burak Kilicoglu, an Experian Global Consultant To read the entire article in the April edition of Experian Decision Analytics’ global newsletter e-news, please follow the link below: http://www.experian-da.com/news/enews_0903/Story2.html  

2007 and 2008 saw a rapid change of consumer behaviors and it is no surprise to most collections professionals that the existing collections scoring models and strategies are not working as well as they used to. These tools and collections workflow practices were mostly built from historical behavioral and credit data and assume that consumers will continue to behave as they had in the past. We all know that this is not the case, with an example being prioritization of debt and repayment patterns. Its been assumed and validated for decades that consumers will let their credit card lines go before an auto loan and that the mortgage obligations would be the last trade to remain standing before bankruptcy. Today, that is certainly not the case and there are other significant behavior shifts that are contributing to today's weak business models.   There are at least three compelling reasons to believe now is the right time for updates: It appears that most of the consumer behavioral shift is over for collections. While economic recovery will take many years, more radical changes in the economy are unlikely. Most experts are calling for a housing bottom sometime in 2009 and there are already signs of hope on Wall Street.   What is built now shouldn't be obsolete next year. A slow economic recovery probably means that the life of new models will be fairly long and most consumers won't be able to improve their credit and collections scores anytime soon. Even after financial recovery (which at this point is not likely over the short term for many that are already in trouble), it can take two to seven years of responsible payment history before a risk assessment is improved.   We now have the data with which to make the updates. It takes six to12 months of stability to accumulate sufficient data for proper analysis and so far 2009 hasn't seen much behavioral volatility. Whether you build or buy, the process takes awhile, so if you still need a few more months of history in will be in hand when needed if the projects are kicked off soon.

Our current collections management landscape is seeing unprecedented consumer debt burdens: Total consumer debt o/s is at $14 trillion as of Jan ’09 Revolving debt o/s has reached $1 trillion The unemployment rate is at 7.6% and is expected to continue to rise Credit card and Home Equity Line Of Credit issuers reduced available credit by approximately $2 Trillion last year and more reductions are expected in 2009 There is a continuing rise in delinquencies and chargeoffs.  Here are some examples from our recent research: 8.5% of Prime Adjustable Rate Mortgages are now delinquent which shows an increase of 491% over this time last year 25% of all sub prime mortgages are now 60+ days delinquent Delinquencies for prime bankcard customers have increased 286% over the last 2 years 34% of all scoreable consumers (those who have sufficient trade information to calculate a score) now have a collection account. Compound these by a decline in the relative collectability of these accounts and you see: 9 million households now have negative equity 20% of 401(k) accounts have been tapped for loans (usually at a cost of 45% in penalties and fees to the account holder) According to the Federal Reserve, in late 2006 – at the height of the sub prime mortgage boom - the U.S. experienced a negative savings rate for the first time since the Great Depression.  

Understanding the Champion/Challenger testing strategy As the economic world continues to change, collection strategy testing becomes increasingly important. Champion/Challenger strategy testing is performed using a sample segment and the results provide a learning tool for determining which collections strategies are most effective. This allows strategies to be tested before rolling them out across the entire portfolio. The purpose of this experimental element to collections strategy management is to observe the effectiveness of new strategies, support continuous improvement of collection approaches and facilitate adaptability to changes in consumer behavior. The methodology behind testing is simple. First, the current environment should be assessed to identify specific areas for potential improvement. Then, a test plan is designed. The test plan should, at a minimum, include well-defined objectives and goals, proposed strategy design, determination of sample size, operational considerations, execution approach, success criteria, and evaluation timetable. After the framework for the test plan has been outlined, running “what if” scenarios will improve refinement of the collections strategy. In the next phase, implementation occurs following the directives of the test plan. Evaluating strategies commences after implementation and continues throughout the duration of the test. This includes analyzing metrics established during the test plan phase to identify trends and changes as a result of the new challenger strategy. The challenger strategy is declared the new champion if the test achieves or exceeds expectations. However, before proceeding with the new champion strategy over the entire portfolio, carefully consider any operational constraints that might hinder the success of the strategy on a grand scale. Once these operational constraints have been identified and their impact assessed, the new champion strategy should be executed.

In addition to behavioral models, collections management and account management groups need the ability to implement 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 collections strategies designed to accelerate collections activity and execute more aggressive actions and increase collections efficiency. 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 automated strategy assignments a hosted collections software decisioning system can close the gap. To use these services master file data needs to be transmitted (securely) on a regular basis. The remote decision engine then calculates behavioral scores, identifies special handling accounts and electronically delivers the recommended strategy code or string of actions to drive treatments.  

Have you ever wondered how your current collections workflow process evolved to its current state?  To start at the beginning, let’s rewind to medieval England … The Tallyman The earliest known collections system was essentially a door-to-door program, as there were no modern day devices to make the process more efficient. The system of record at that time was typically a hardwood stick with carved notches representing loans and payments between a lender and borrower. This door-to-door collector was known as the Tallyman, which referred to the collection of tally sticks he carried to document financial transactions. The beginning of modern times As technology evolved, telephones and letters became the collections management tools of choice, with a personal visit being a last resort action. The process where a collector managed the repayment strategy and relationships for his assigned customers was still in practice. Collections operations were typically in decentralized branches and small teams of skilled collectors were able to effectively manage this “cradle-to-grave” approach. Yesterday When expense management became a priority, the migration to larger, centralized operations became an industry trend.  Many companies found it difficult to hire large teams of highly-skilled collectors in their geographic regions and the bucket system was born. The concept was simple and effective -- let the less experienced staff work the accounts that are the easiest to collect and focus the experienced collectors on the more difficult cases.  Advanced collections tools such as automatic dialers arrived on the market to increase efficiency and were shortly followed by decision engines used to support behavioral scoring and segmentation strategies. Today Current trends in collections include the migration towards a risk-based segmentation and strategy approach. Cutting edge tools and collection management software, designed to address today’s collections business objectives, are hitting the market and challenging the traditional bucket approach most of us are used to. As the economic conditions of the past few years deteriorated, many organizations began shifting their spending focus towards the collections department and this, in turn, has inspired investment and innovation from software, analytics and data vendors. New collections scores were recently unveiled that yield predictiveness that has never been seen and collections data products have become significantly more sophisticated. Modern technology is also empowering collections managers to control the destiny of their business units by freeing them from the constraints of over-burdened IT departments and inflexible systems. There is also an emerging trend to consider the collective power of multiple products working in tandem. Collections experts are finding that the benefit of the complete solution equals much more than just the sum of the parts. Tomorrow Once we all migrate to the next level and employ today’s modern marvels to make our businesses more productive and efficient, what’s next?  It’s highly probable that tomorrow’s collections workflow will consider the entire relationship and profit potential of a customer before a collections action is executed. Additionally, the value in considering the entire credit and risk picture associated with a customer will be better understood and we will learn when each of the holistic view options is most appropriate. There are a number of roadblocks in the way today, including disparate systems and databases and siloed business units with goals and objectives that are not aligned. Will we eventually get there? The business leaders with long-range vision certainly will … just as some unknown visionary had the initiative to embrace emerging technology and abandon his tally sticks. For more information and to read the Decision Analytics newsletter that features one of my previous blogs, "Next generation collections systems", click here.   

They have started to shift away from time-based collections management activities (the 30-, 60-, 90-day bucket approach).  Instead, the focus is migrating towards the development of collections strategy that is based on the underlying risk of the individual – to look at how he is performing on all of the obligations in the total relationship to determine the likelihood of repayment and the associated activities that can facilitate that repayment.  They’ve found they can’t rely purely on traditional models anymore because consumer behavior has dramatically changed and an account only approach doesn’t reflect the true risk and value of the individual’s relationship.