Retooling for Collections Management
In our last post, we discussed how creating infrastructure that supports orchestration and real-time decisioning is a crucial step towards applying AI to credit decisioning. The same can be said about collections management. Last fall, we conducted a survey with The Financial Brand to examine how financial institutions have been navigating COVID-19. In regards to collections, many businesses planned to focus on automation, more regular testing, and utilizing machine learning to prioritize collection activities in 2021. However, many of the most tech savvy organizations still rely on spreadsheets to populate their collections calling lists. Therefore, investing in modernizing backend processes will be necessary to achieving those objectives. The benefit of leveraging a decision engine like Enova Decisions Cloud™ is that you can manage your fraud, credit, and collections decisions all on one platform. Once you have that infrastructure in place, you will have the ability to test your way into a more holistic approach to debt valuation and collections management. Since collections is an ongoing activity, incremental improvements will yield high returns. If your starting point is prioritizing calls based on amount past due, here are three ways to augment your current program.
- Predict that amount that is likely to be collected by an account and prioritize calls accordingly.
- Predict the future value of a cured account which includes the likelihood a customer would be a returning customer once in a good standing and create a communications strategy for high-value customers.
- Leverage 3rd-party data to beef up your predictive models.
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