Powering Predictive Analytics – Supporting Collections Superpowers While many superheroes gain their powers through freak accidents or genetic quirks, your collections and risk teams don’t need seek out radioactive spiders or have been born on a distant planet to implement collections analytics. In fact, building the ecosystem to support predictive analytics is achievable in just a few months.
The teams supporting your collections strategies will need to focus on three key areas:
Data Integration
Data holds the answers to your collections questions, such as is this account high risk? Is this account likely to self-cure? How is this strategy performing? Can we make changes to reduce recidivism? What’s the best way to engage with a specific customer? What’s the best resource to maximize payment on a particular account? What strategies build the most customer loyalty? Data provides both historical and real-time insights that improve the accuracy of your predictive models and in turn the effectiveness of your collections team. Connecting disparate systems and integrating data into your analytics tools is an essential step in implementing collections analytics. This poses a challenge for many organizations, where integrating data sources can often take weeks or months. Choosing an analytics tool that simplifies integration and creates easy access to data rapidly reduces deployment time and empowers a faster return on investment.
Automation
Automating the manual processes that burden collections teams, such as outreach to those likely to self-cure, allows you to focus valuable resources on the tasks that computers can’t handle, such as engaging with and supporting customers. Analytics tools can determine which accounts can be handled through automation and which need human intervention. The same tools can automate the decisioning which places accounts into specific segments, determines next best steps, and initiates contact strategies through dialers, text, and email systems.
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