5 Ways AI is Shaping the Future of Debt Collection - ClearDu

So when it comes to deciding the best way to engage consumers, has your strategy evolved to keep up? Machine learning, Artificial Intelligence for Loan Recovery, data science—these terms are thrown around a lot, and for good reason.

We’re here to cut through the jargon and help you understand machine learning and its application in the collection and recovery so you can make an informed decision before taking a big technology leap.

Machine learning is the capability of a computer to make decisions using thousands of inputs faster than the human brain can process, while continuously improving the quality of those decisions as it learns more over time.

Machine learning is used in numerous ways across industries and organizations—but what about in collection? There are many ways it enhances a debt collection and recovery strategy, but here is a quick overview of common machine learning components used for collections with the benefits each provides.

What message will appeal to and compel a customer to read and respond? Treating content as one-size-fits-all can fall flat when trying to engage an individual, especially when it comes to sensitive or delinquent accounts.

Send a message more likely to resonate based on data points that align with an individual customer. Through the learning feedback loop, a wider library of content can be created to offer more personalized messages tailored to a variety of customers.

Which is the best channel to reach out to a customer: phone, email, SMS, or something different? Trying to figure out the best way to contact a customer is the key to any successful collection operation—avoiding wasting valuable time, energy and cost on the wrong channel with no engagement.