Data analytics offer new and more powerful ways to segment receivables, measure outcomes, and take cost-effective actions. Research shows that data analytics can improve the collection process, but firms need to implement that research. Working with a fintech partner is often the best solution for financial institutions and other organizations.
Collectors have always segmented cases, but data analytics are enabling new and more detailed micro-market segmentation. Rather than merely aging receivables, firms can now easily separate them into multiple categories right from the beginning. Businesses can save time and money by identifying “self-cures” and directing them to chatbots, apps, and other low-cost automated solutions. Data analytics can also be used to flag high-risk cases and pair them with experienced collectors. Increased market segmentation is intuitively appealing, but it is essential to measure the results.
Several studies demonstrate the effectiveness of data analytics in improving collection outcomes. The Oliver Wyman Group found that data analytics helped a bank reduce the number of calls by 35% while maintaining the same level of collections. In another study, McKinsey & Company measured the effects of adding assertive script elements to targeted cases for a credit card issuer. Without the script elements, the promise kept rate was only 44% for the high-risk segment. They trained collectors to use levers, such as anchoring the negotiation amount and mentioning emotionally relevant consequences. After the training, the promise kept rate improved to 95%.
Collectors often focus on improving collections percentages, but data analytics enable a more advanced approach based on expected values. Wang, Geer, and Bhulai developed a model for predicting collection call values using data analytics and applied it. The new collection policy produced only a mild increase in the percentage of cases that were fully collected. The new data analytics group completely collected 62.6% of cases, while the control group fully collected 59%. However, the amount collected per call was 46.30 euros for the data analytics group compared to 31.80 euros for the control group. It is true that top collectors with years of experience are good at picking the right cases. Data analytics can give any group of collectors that same advantage.
The core of the data analytics collection process is identifying and implementing cost-effective actions. Traditional collection efforts are increasingly thwarted by debtors who never answer the phone and do not respond to mailed notices. Simply adding call-out boxes and boldface type can increase responses to notices in some cases. In other cases, more resources should be deployed to resolve billing issues before they become long-term problems. Data analytics are the key to choosing the most cost-effective collection strategy for each case.
There is considerable evidence supporting increased use of data analytics in collections, but many firms still have difficulty actually making the necessary changes. Some businesses are held back by the belief that an advanced data infrastructure must be in place before data analytics can be used for collections. In fact, data analytics is the best place to start building the data infrastructure that firms will need for future advances in AI. The fear that data analytics take too much time to develop is another barrier. The notion that regulatory and compliance issues are insurmountable also keeps some financial institutions using older systems. At Relational, we have the experience that firms need to create customized data analytics collection solutions that are compliant with local regulations in the EU and elsewhere.
