Collections Strategy

-- by Wendy Greenawalt

In my last blog post I discussed the value of leveraging optimization within your collections strategy. Next, I would like to discuss in detail the use of optimizing decisions within the account management of an existing portfolio. Account Management decisions vary from determining which consumers to target with cross-sell or up-sell campaigns to line management decisions where an organization is considering line increases or decreases.  Using optimization in your collections work stream is key.

Let’s first look at lines of credit and decisions related to credit line management. Uncollectible debt, delinquencies and charge-offs continue to rise across all line of credit products. In response, credit card and home equity lenders have begun aggressively reducing outstanding lines of credit.    One analyst predicts that the credit card industry will reduce credit limits by $2 trillion by 2010. If materialized, that would represent a 45 percent reduction in credit currently available to consumers. This estimate illustrates the immediate reaction many lenders have taken to minimize loss exposure. However, lenders should also consider the long-term impacts to customer retention, brand-loyalty and portfolio profitability before making any account management decision.

Optimization is a fundamental tool that can help lenders easily identify accounts that are high risk versus those that are profit drivers. In addition, optimization provides precise action that should be taken at the individual consumer level.

For example, optimization (and optimizing decisions) can provide recommendations for:

• when to contact a consumer;
• how to contact a consumer; and
• to what level a credit line could be reduced or increased...

…while considering organizational/business objectives such as:

• profits/revenue/bad debt;
• retention of desirable consumers; and
• product limitations (volume/regional).

In my next few blogs I will discuss each of these variables in detail and the complexities that optimization can consider.

-- By Wendy Greenawalt

The combined impact of rising unemployment, increasing consumer debt burdens and decreasing home values have caused lenders to shift resources away from prospecting and acquisitions to collection and recovery activities. As delinquencies and charge-off rates continue to increase, the likelihood of collecting on delinquent accounts decreases -- because outstanding debts mount for consumers and their ability to pay declines. Integrating optimized decisions into a collection strategy enables a lenders to assign appropriate collection treatments by assessing the level of risk associated with a consumer while considering a customer’s responsiveness to particular treatment options.  

Specifically, collections optimization uses mathematical algorithms to maximize organizational goals while applying constraints such as budget and call center capacity  -- providing explicit treatment strategies at the consumer level -- while producing the highest probability of collecting outstanding dollars. Optimization can be integrated into a real-time call center environment by targeting the right consumers for outbound calls and assigning resources to consumers most likely to pay.  It can also be integrated into traditional lettering campaigns to determine the number and frequency of letters, and the tone of each correspondence. The options for account treatment are virtually limitless and, unlike other techniques, optimization will determine the most profitable strategy while meeting operational and business constraints without simplification of the problem.

By incorporating optimization into a collection strategy that includes a predictive model or score and advanced segmentation, an organization can maximize collected dollars, minimize the costs of collection efforts, improve collections efficiency, and determine which accounts to sell off – all while maximizing organizational profits.

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?"

 

The way in which you communicate with your customers really does impact the effectiveness of your collections operation.

At the heart of any collections management operation is the quality of the correspondence and, in particular, the tone of voice adopted with the debtor. In short, what you say is important, but how you say it has a critical impact on its effectiveness.

To help guide best practice in this area and provide areas for consideration when designing and implementing customer letters within a collections strategy, Experian commissioned a study to explore how consumers react to the words used to communicate with them about their debt.

Key findings:

• An appropriate tone, clear detail of the consequences and a conciliatory approach are effective in the early phases of collection 
• Fees and charges and negative impacts on credit ratings were key motivators to pay
•  Charges applied to an account for issuing a letter is disliked and likely to encourage many to contact the organisation to express their frustration 
• After 3 months a strong emphasis on serious action is appropriate, including reference to legal action or debt collection agency involvement 
• Support should be offered, wherever possible, to aid those in difficulty 
• Letters should avoid an informal and patronising tone 
• Lengthy letters have a low impact and are often not fully read, resulting in important messages being missed 
• Use of red to highlight and focus on a specific point is effective
• Use of red to highlight more than one point is counter-effective 

To download the entire paper* and view other best practice briefings, follow the link below to the global Experian Decision Analytics collections briefing papers page:

http://www.experian-da.com/resources/briefingpapers.html

* Secure download account required. You can sign up for one today - FREE.

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.
 

Behavioral scoring is one of the most important tools that allow collections management and account management groups to evaluate accounts in an efficient and cost-effective manner. Although behavioral models are developed in a similar manner as new applicant models, there are several key differences that make behavioral models a better choice for many account management applications and collections workflow systems:

By using only internal master file data as opposed to external credit bureau data, for example, accounts can be regularly evaluated without incremental cost. The most common practices are to score accounts on a weekly or monthly basis, which allows for quick strategic responses to a customer’s change in behavior. Frequent evaluations can result in automated or manual actions such as the acceleration or deceleration of collections efforts, adjusting credit limits and changing terms and conditions.

The performance definitions of behavioral scores are very specific to each strategy and task, and it is typically not advised to use models in applications for which they were not designed. For example, a new applicant model definition of “bad” may be a high probability of charge off during the initial term of a line of credit. For collections strategy, a more appropriate bad definition might be the likelihood of an account rolling to the next delinquency bucket, regardless of the age of the account. 

Behavioral models also have a much shorter outcome period of three to four months versus new applicant models that forecast over one to two years. Since behaviors with one creditor can typically be recognized more quickly than with all lending institutions associated with a particular debtor, behavioral models provide a unique and timely evaluation of the ongoing risk once the account is already on the books.

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.