Predictive modeling is used in many aspects of life today. For example, a fisherman can use predictive forecasts to determine the patterns of fish to find the best times and locations to get the most action. Similarly, in the world of collections, there are many fish in the sea with agencies challenged to handle massive amounts of accounts and debt.
As a result, these companies need to come up with ways to analyze the consumers they are trying to collect and predict if and when they can pay in a timely manner. This can mean the prioritization of certain accounts over others to maintain a constant flow of collections or even a more personalized approach tailored to collecting on a particular individual. This is the essence of predictive modeling in accounts receivable.
What is Predictive Modeling in Collections?
Put simply, predictive modeling in the world of collections may be described as the use of machine learning and personal data to construct models that pave the way for more accurate and effective collection strategies. Additionally, this allows for the segmentation of accounts to prioritize collection efforts where needed. Predictive models work to guide the focus of an agency’s representatives to increase their overall collection rates.
Many factors fall under predictive modeling. Each has its own value when trying to create custom strategies for accounts — whether it helps with call scheduling, improves relations between the collector and consumer, or determines whether or not the account should be a priority at all.
Assessing Risk and Consumer Scoring
One old-fashioned way to assess a consumer is by looking at their FICO credit scores. Credit scores have long been a standard indicator of how efficient someone is at paying off their debts. However, collection scores are now used instead to assess consumers. These scores are an assessment of a consumer’s 
propensity to pay (or the likelihood that they will fall further into delinquency). The consumer may be someone who consistently pays on time and may have just fallen through the cracks just this once. Thus, efforts to collect on this individual have a better chance of success. On the other hand, the consumer may have a poor repayment record with a long history of avoiding their debts. These scores are a good indicator of collection difficulty.
Learning the Consumer
Collection agencies are able to log conversation data into their collection programs where machine learning is able to use that information to track the optimum times to get in contact with consumers for the best results. For example, it may be wiser to contact them when they aren’t working so calls won’t be missed or perceived as disruptive. Showing consumers that contact efforts have their best interests in mind can work to improve the chances that they will listen to a call agent.
Additionally, machine learning can monitor a consumer’s spending patterns to determine the times of the month when they have more spending money. Consumers may be more likely to pay off their debt when they have money in their pockets.
Segmentation
With all the information garnered through predictive modeling, collection directors will be able to segment accounts into groups to address them differently and create a higher probability of success. The primary goal of segmentation is to find efficient ways of allocating an agency’s workforce to enable the recovery of the greatest amount of debt.
One primary method is segmenting consumers who are receptive to automated messaging. Some consumers like the independence of being able to choose their own repayment options while others may simply find it to be a valid form of contact. These consumers require no additional human contact. Naturally this works in favor of an agency because with fewer accounts routed to agents, the agency can use their collectors to focus on other delinquent accounts where needed.
Another method is segmenting by propensity to pay. Predictive modeling can help an agency focus on accounts that are likely to pay rather than those that may be more difficult to collect from. Why spend countless hours contacting consumers who are stone walling collectors when you can give more attention to those willing to pay their debts? Being able to identify “easy wins” to pursue and negotiate with enables collection agencies to increase cash flow for their clients.
Additionally, consumer accounts can be segmented by their value. For example, high value accounts may be segmented and reserved for an agency’s most skilled collection teams. This gives the account the highest probability of success. Low value accounts may be relegated differently to save time.
Segmentation is an invaluable tool for any agency. Being able to identify where and how a collector can be most effective will ultimately optimize collection efforts to provide the best possible results.
Summary
Predictive modeling is a thorough system that can help agencies learn more about the consumers they are trying to collect from. With the information garnered through this process, agencies will be able to formulate optimal strategies for scheduling contact with consumers. Being thorough and utilizing predictive modeling are some of the best traits that make up a best-in-class agency.
Optio’s head of operations creates effective strategies to maximize collection efforts with the help of predictive modeling and analytics. These advantages — in conjunction with a compliance management system, data security, collections technology, and considerable experience in financial services — provide a favorable return on investment, brand protection, and customer retention for clients. Contact us today to learn about an individualized collections strategy for your organization.
