FIS Blog

The Rise of Predictive Analytics Within Credit and Collections

Mike Kresse | Thursday, February 11, 2016

Big data has been an area of significant focus over the last three or four years, and the conversation continues to evolve. In particular, there is a growing realization that big data generates even bigger questions – such as what companies should do with the information they are collecting in order to benefit further.

As a result, companies are using increasingly sophisticated analytics to gain greater insights from their data. Different types of analytics can be used to obtain different types of information, which tends to progress through the following stages:

  • Descriptive data: What has happened?
  • Diagnostic data: Why did this happen?
  • Predictive data: Is this going to happen again?
  • Prescriptive data: What should I do now?

Consumers already accept and acknowledge the use of predictive analytics in areas such as the presentation of online advertisements based on their purchase history. Where credit and collections is concerned, however, predictive analytics are at an earlier stage of development and acceptance. People might understand what predictive analytics are, but they want to know what they should do with the information they gather.

Predictive analytics can offer clear benefits in the area of credit and collections. Companies tend to have a mix of customers, ranging from those who always pay on time to those who are at high risk of going past due. A company may be owed $100 million in accounts receivable, but the individual accounts within the portfolio can behave very differently from each other. For the credit manager, the question is where the team’s efforts should be directed in order to achieve the greatest impact.

By using predictive analytics in this area, companies can gain a greater understanding of their portfolio risk and thereby improve the effectiveness of the collections process. For example, descriptive data can tell the company why a particular account has gone from low risk to high risk and whether this is likely to happen for other accounts. More importantly, predictive analytics can help identify which customers will be at risk in the future – and what action the company should take in order to achieve a positive outcome.


Tagged in: Credit and Collections, Receivables, Predictive Analytics, Corporate Solutions

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