OPERA SOLUTIONS CAPABILITIES. Collections: reducing charge-offs with advanced analytics

Transcription

1 OPERA SOLUTIONS CAPABILITIES Collections: reducing charge-offs with advanced analytics

2 2 Enhancing collections performance through next generation analytics 1. The delinquency tsunami and the new collections paradigm The financial industry has been overwhelmed by a massive wave of increasing chargeoff losses that is progressively affecting all portfolios. Two concurrent factors will continue to drive this process. The scale and magnitude of the growth in charge-off losses provides clear evidence of the shortcomings of traditional collections methods. To cope with the growing flood of delinquent and charged-off accounts, a new collections paradigm is emerging, shifting focus from the mass execution of standardized payment requests to delivery of customized and proactive treatments on a large scale. Four key principles characterize this new approach: Take action before things sour. Stop customers before they go bad and enter collections, by identifying behavioral patterns that predict delinquency and taking proactive actions such as credit limit reduction, renegotiation of loan terms, or debt restructuring. Negotiate rather than intimidate. Bruteforce treatments such as contacting newly laid-off customers with repeated, frequent demands for full payment do not work. Customized negotiation tools and strategies, including refinancing or partial settlement, will maximize the recovered amount. Be the first in line with likely-to-pay customers. Even customers who want to discharge their debts have only so much in their payback wallet. It is critical to reach customers who are willing to pay while they still have the resources to do so. Recognize the long-term value of some delinquent customers. High-value customers experiencing temporary financial troubles will benefit from dedicated hardship programs. Deliver customized treatments based on profile and lifetime value. Most existing collections operating models and infrastructure are not adequately prepared to support this new paradigm and successfully cope with the challenges of today s environment. Adoption of these new principles requires radical changes in both the actions taken and the ways they are designed and executed. Implementation of the new paradigm requires enhancing collections capabilities in three critical areas: Customer profile enrichment. Expanding the information available on delinquent customers Next-generation analytics. Adopting advanced analytics methods to determine optimal treatment strategies for each delinquency Platform and skills enhancement. Upgrading collections platforms and skills to ensure effective execution.

3 3 Below, we provide more detail on each of these critical components. Customer profile enrichment Collections operations traditionally rely on essential, but very basic, customer profile information (for example, application data, outstanding amount, and tenure in collections). In today s rapidly changing environment, with its increasingly more highly segmented delinquent population, only well-informed collections operations can deliver better results. It is imperative to enhance customer profiles to include the most recent behavioral and market trends from both internal sources (such as the customers response to past collections treatments) and external ones (such as unemployment rate trends and real estate market trends in the customers areas). Platforms and skills enhancement Most existing collections platforms are not ready to support the execution of customized and differentiated treatments. Data on delinquent customers is often exchanged through batch files, and platforms offer limited real-time tracking and reporting capabilities. The lack of integration between transactional and reporting systems does not allow for the effective design, piloting, and implementation of treatment strategies based on analytic models outputs. In addition, existing training programs and incentives may require adjustments in order to increase the motivation and professionalism of the collections staff. Next generation analytics Historical experience is not enough to define successful collections strategies and treatments. Instead, collections needs to utilize the same analytical rigor used in other phases of the customer life cycle underwriting and relationships management. This approach allows collections to understand and proactively drive customers behavior to the desired outcome, improving performance. Adoption of integrated, adaptive, flexible learning models that grow stronger when confronted with complexity, rapid change, and new information (for example, Neural Networks, Singular Value Decomposition, and Restricted Boltzmann Machines), will enable companies to define optimal collections treatment methods.

4 4 Figure 2, below, summarizes the differences between the new paradigm and the old: FIGURE 1: key enablers of the new collections paradigm Focus TRADITIONAL APPROACH Massive execution of standardized payment requests NEW PARADIGM Large scale delivery of customized collections treatments Customer Profile Basic customer attributes from application form and payments history Large scale delivery of customized collections treatments Collections Treatment Prioritization based on outstanding balance, tenure, and risk score Differentiated treatment strategies and tools defined through next generation analytics Infrastructure Limited flexibility and integration with legacy systems Platform integrated with legacy systems, enabling execution of customized treatments Skill Inexperienced and low tenured collectors Trained collectors, retained by competitive compensation schemes

5 5 2. Enhancing collections through analytics General framework Collections departments can achieve near-term and sustainable performance improvement by adopting a comprehensive approach that addresses both the what and the how of the new collections paradigm challenge. FIGURE 2: collections enhancement framework Understand Book Business Devise Customized Treatment Strategies Levels What Profiling Modeling Segmentation Segments Customised Strategies Hardware Communication Systems Capability Incentives How Results Results Software Physical Infrastructure Capacity Training The core elements of the what are customized treatment strategies determined through advanced behavioral analytics, to maximize the amount recoverable from each customer. The how represents tools and procedures ensuring consistent execution of these treatment strategies. Implementation must focus on near-term results as well as on sustainable improvement. These two objectives can be achieved through development of transitional tools that work within the existing infrastructure, and alignment of infrastructure and skills with the requirements of the customized treatments.

6 6 3. The what : segmenting accounts and devising customized treatments Risk and marketing departments routinely use analytical and scientific approaches profiling, segmentation, and modeling to improve their understanding of their customer base and drive sustainable business growth. Similarly, collections departments can use advanced analytic methodologies to understand the key attributes, behavioral patterns, and needs of delinquent customers, and use this information to develop treatments customized to each customer s specific profile. Analytics-driven strategies and tools will help collections not only to better manage the current tsunami of delinquencies, but also to achieve sustainable performance improvement into the future. 3.1 Profiling and segmentation The first step in developing an analytics-driven approach to collections is to create robust profiles and segmentations of delinquent customers. This is a departure from current practice. Most existing collections segmentation schemes focus mainly on workload management and primarily consider balance and tenure. Their goal is usually to match volumes with collectors capacity and quality. Opera Solutions approach to segmentation is more sophisticated, and enables collections departments to define customized treatment strategies by understanding individual customer profiles and predicting their behavior and likely payment triggers. We develop segments by deconstructing the collections portfolio along a multi-dimensional set of independent criteria: balance/tenure, likelihood-to-pay, sensitivity to treatments, other customer attributes, and retention value. FIGURE 3: collections enhancement framework Attributes 1. Likelihood to Pay 1. Likelihood to Pay 1. Likelihood to Pay 2. Sensitivity to Treatment 2. Sensitivity to Treatment 3. Outstanding Amount 1. Likelihood to Pay 2. Sensitivity to Treatment 3. Outstanding Amount 4. Other Attributes Sensitivity L M H Sensitivity L M H L M H L M H L M H Likelihood to Pay Likelihood to Pay Likelihood to Pay L M H Balance Payment=DD Co-signer=Yes... Payment=CC

7 7 The segmentation process creates many granular clusters, which are then aggregated to define a manageable number of functional groups, usually less than 10. For each group, we can define an optimal treatment intensity across different channels and possibly develop specific scripts to target their individual profiles. Below, we focus on the specific inputs into the segmentation model, and how we use them. Balance/tenure Outstanding balance and tenure in collections are usually the main factors used to distribute workload among collectors, by matching the amounts at risk with the collectors capabilities and bandwidth. Segmentation criteria focused only on balance and tenure, however, do not take into account other factors affecting customer s recoverability. For customers who share a similar profile, the same effort might be required to collect $100 or $10,000. On the other hand, customers with a different profile may be likely to recover on their own, without any collections effort. This is where analytics that can define a customer s likelihood-to-pay are critical to achieving results. Likelihood to pay Even before entering collections, each customer has a specific likelihood-to-pay, which can be defined as the combination of his or her willingness (that is, system of preferences) and his or her ability (that is, financial situation) to pay. Likelihood-to-pay is assessed through a combination of demographic and behavioral attributes that are usually tracked by companies, but have not generally been used to define collections strategies. When collections departments know a customer s likelihood to pay, they are better able to decide the customers priority in collections and the best type of treatment to deliver. Sensitivity to treatments Some customers may have similar risk profiles and balance but still respond differently to the same collections treatments. For example, we have observed that some customer segments are sensitive to text messages (generating high volumes of inbound calls after receiving an SMS) while others are not responsive to this type of treatment. Based on these observations, we have found that the segmentation scheme can be further enhanced by adding a treatments sensitivity layer. Sensitivity to treatments is evaluated by considering the cure rates of segments with similar profiles and different treatment patterns. The possibility of adding a treatments sensitivity layer to segmentation, however, depends heavily on the availability of operational data. For example, in the above example, the analysis of sensitivity to SMS treatments would be significantly undermined if logs of text messages campaigns were either not stored or stored in a third-party system, such as a telecom services vendor.

8 8 New data sources Historical and conventional metrics are no longer sufficiently predictive in today s rapidly changing credit and collections environment. New data sources must be used in analysis, including relationship information, geodemographic trends, income and wealth information, and real estate data. These new data sources can be utilized to derive novel and insightful variables, such as geo-demographic combined loan-to-value ratio (CLTV) on all existing properties, and debt-to-income ratios. Taken together, the updated data and variables can provide a holistic view of a customer s financial situation and offer insight into his or her future behavior. Lifetime Value Risk and marketing departments tend to consider customer lifetime value as a priority, but this has generally not been the main focus of collections. However, collections departments can further improve their performance and deliver a substantial contribution to portfolio growth by also considering the long-term profitability of delinquent accounts. For example, collections may decide to deliver an incremental effort to retain a specific group of customers who have an overdraft account but are highly likely to acquire a loan or a mortgage in the next three years. Other clients who are facing temporary problems may benefit from an appropriate debt restructuring and may consequently become loyal customers, or even brand ambassadors. While a focus on outstanding balance yields an immediate impact, taking into account lifetime value can allow collections to drive long-term profitability for the company.

9 9 3.2 Enhanced treatment strategies Segmentation and behavioral analysis enable the definition of customized treatments along the entire life cycle of the customer: Before acquisitions: underwriting models Before collections: early intervention strategies In collections: pre-contact (customer information enhancements, prioritization) and postcontact (customized treatments, refinancing, debt settlement treatments) After collections: debt sale and outsourcing FIGURE 4: key enablers of the new collections paradigm Charge-off Delinquent Charge-off Early Intervention Pre-Contract Operational Enhancements Post-Contract Settlement Strategies Debt Sale LEVERS OF IMPACT Identify at-risk customers based on early trends Prevent delinquencies by timely and effective interventions Improve quality of contact information Differentiate treatment strategies Align capacity with workflow and select outsourcing schedule Design and optimize advanced collections tools (e.g. partial settlement, refinancing) Develop differentiated call/letter scripts Identify high-risk segments for inventory sale

10 10 Underwriting strategies Current underwriting models are dated as soon as they are deployed. In order to allow time to observe performance, traditional models use data that is no less than 18 months old as input. Assuming a realistic minimum 3-6 month lag from the time data is acquired until a model is deployed, even a fresh model scores accounts based on behavior that is, in general, 24 months old. And the situation worsens over the deployed life of a model. Within the current financial environment, we are facing an unprecedented rapid shift in macroeconomic conditions and consumer behavior. We are seeing high default rates for segments of traditionally good customers, coupled with steep decreases in recoveries. What is exceptional is that the profile characteristics of delinquents are in constant change, and now relate less to individual historical credit behavior than to external factors such as geography, housing status, and employment. New techniques that incorporate preprocessing, post-processing, and learning models that use dynamic feedback are essential to controlling risk in today s environment. Early intervention strategies One undeniable way to reduce chargeoff loss is to avoid having accounts enter collections in the first place. This goal can be achieved by implementing early intervention strategies that are triggered by signals predictive of delinquency and charge-off. Through our work with several clients, Opera Solutions has found certain behavioral patterns that are robust predictors of delinquency. For example, peaks in certain types of transactions, high credit line utilization, and balance build up a few months before entry into collections. FIGURE 5: cash advance spending trend before delinquency - example monthly cash advance per account Delinquent Charge-off Delinquent Non- Charge-off Non - Delinquent Months Before Delinquency

11 11 Once a current customer exhibits a behavior associated with high likelihood of delinquency, early intervention strategies can be activated. These may include educating the customer on proper utilization of financial products, reducing the credit limit to lessen the negative impact of a potential charge-off, or restructuring contractual terms before the situation becomes critical. In such early intervention actions, collectionslike treatments are delivered to a customer who is still under the responsibility of the risk department. For such initiatives to be effective, the risk and collections departments must interact closely. Enhancing contact information Only customers for whom you have accurate contact information will ever be successfully treated. Accounts with missing contact information will never receive proactive treatment. Those with incorrect information will divert time and resources from workable accounts. Early identification of bad numbers is critical, since it will reduce the lead time to begin the skip tracing process and increase the time available to reach the customer. Traditionally, phone numbers are recognized as bad numbers after several weeks of unsuccessful attempts. Analytics can dramatically reduce the lead time required to recognize a bad number by predicting the likelihood of a right-party contact based on a 5-10 day history of call attempts. Prioritization and customized treatments Most companies do not differentiate treatment for various accounts in collections, or have only rudimentary differentiation for example, calling high balance accounts more frequently, but using the same scripts. Prioritization is critical. Limited resources must be allocated to reach the maximum number of customers who would not have cured without treatment and who are sensitive to treatment. More importantly, certain accounts should be contacted as early as possible. In cases where customers are being pursued by several creditors at once, whichever one reaches them first is most likely to collect. Refinancing Most collections operations already have some refinancing programs in place. However, strategies and criteria for selecting those customers with the potential for refinancing are typically based on experience and intuition rather than in-depth analysis of account-level data. When accounts are not appropriately selected or when they receive an insufficient offer, they frequently return to collections in the near term, nullifying the effort. Detailed customer-level analytics can identify the accounts that are most likely to become charge-offs without refinancing, and pinpoint optimal terms that balance the benefits of keeping the account with the cost of additional financing. Settlement Settlement is an option that has not achieved much traction in the collections industry. Until recently, reaching a settlement agreement was not considered a viable option, due in large part to the lack of negotiation criteria and reliable information on a customer s likelihood to charge-off. Behavioral analytics provide an unprecedented level of insight into the economics of partial settlement by estimating client likelihood to charge-off, defining a target settlement price, and setting a floor price for the negotiation.

12 12 With behavioral analytics, analysis of historical data for each account determines a profitable target price that will provide a partial settlement higher than the expected collections amount, given charge-off probability. The ideal time window in which to offer settlement can also be calculated, optimizing the trade-off between settling too early when partial payments and fees may be lost, and too late when customer may no longer be able to pay even the partial amount. any recovered amount. Predictive debt models can provide a valuable contribution in selecting accounts for debt outsourcing. Being able to identify potential candidates for debt outsourcing or sale as soon as possible is critical, since the outsourcing fee or sale price becomes less favorable as more time is spent in collections. Outsourcing and debt sale When an account is impossible to recover or collection is not worthwhile for example when the outstanding amount is small businesses have an opportunity to recover a partial amount through outsourcing or debt sale. Debt sale causes an immediate loss on the business books; outsourcing an account makes it possible to extend the time in which it might be collected without wasting internal resources. However a predictive analysis of the future collected amount can be used to determine the optimal timing and price of selling certain groups of accounts. With debt outsourcing, the external vendor does not bear the credit risk, but pursues collection and agrees to pay back a fraction of

13 13 4. The how : implementing the change and realizing the impact While most companies have some sort of prioritization and basic segmentation of their accounts, extensive infrastructure and operational changes will be needed in order for them to implement treatment prioritization and customized strategies. Nevertheless, implementation of collections enhancement can be planned to achieve near-term impact with limited modifications to existing infrastructure, while the full-scale transformation is rolled out in the medium term. FIGURE 6: collections enhancement implementation roadmap Full-scale Collections Transformation Near-term Improvements & Skill-set Development Quick-hit Enhancements Enhanced Treatment Strategies Financial Impact Losses Reduction & Retention Value

14 14 In order to capture immediate impact, initiatives that can be kicked off with minimal impact on existing infrastructure should be a priority. Configurations of existing infrastructure can be fine tuned to support the new strategies, or temporary tools and manual processes can be put in place to avoid time-consuming infrastructure enhancements. For example, a high-level prioritization scheme can be created immediately by defining different dialer campaigns while waiting for a collections platform that enables a more granular prioritization. Other collections initiatives, such as debt sale and contact information enhancement, can be kicked off by establishing a what team that manually works a list of accounts created through an offline decision engine. Enhancements to collections strategies and operations should be the result of an incremental and iterative process focused on the following elements: Embedding the behavioral models and decision engines in the collections platform Developing the collection analytics datamart engine and real-time reporting capabilities Recalibrating the models to respond to changes in market conditions and portfolio profile Increasing the automation in the delivery of customized treatments Training and motivating the collections resources As these quick changes are put in place, it is also necessary to ensure that the organization and infrastructure is prepared to execute the customized treatment strategies. Formal training and professional development for collectors as well as collections analytics experts are key to enabling continuous update of decision models and consistent delivery of customized treatments.

15 15 Conclusion A tsunami of delinquent accounts is now flooding collections operations, overwhelming collections operations. Charge-off losses are growing at an even higher pace than the delinquent volumes. Not only is the sheer volume difficult to manage, but also the types of debtors are more varied. Traditional collections methods are not able to handle the increasing volumes and differentiation of clients facing financial problems. Keeping one s head above water indeed, thriving through this period requires a new collections paradigm that focuses on large scale delivery of treatments tailored to individual customer s profile. Transition to the new paradigm requires a unique combination of Analytical capabilities, to develop indepth understanding of customer behavior Technical skills, to manage the necessary enhancements to IT infrastructure Change management experience, to design new procedures and drive organizational change Opera Solutions has successfully worked with top financial institutions in US and Europe, helping them transition to the new collections paradigm. For more information, or to contact us, click here.

16 ABOUT OPERA SOLUTIONS, LLC: Opera Solutions drives productivity for enterprise and government through advanced Big Data predictive analytics that create machine-generated directed actions to significantly improve front-line decisions. Its Signal Hub technologies provide organizations an enterprise-wide capability to continually extract and put to use newly available predictive information from outside their firewalls, creating a revolutionary pathway to productivity, profit, and advantage. With 230+ data scientists among its 700 employees, the company is an internationally recognized leader in machine learning. It serves enterprises in Financial Services, Healthcare, Government, Supply Chain, Marketing, and other sectors, with offices in North America, Europe, India, and China. New York Jersey City Boston Washington, DC San Diego London Shanghai New Delhi