When you think about the intersection of artificial intelligence and debt collection, do you have a clear picture of what that means? Maybe your initial response is, yes, of course. But if you try to explain it, the vision may get a bit blurry. This is what happened to me. So I set aside the technology for a moment to try to illustrate what it means to interact with artificial intelligence in the collections process. I thought that if the concept were viewed from the perspective of a consumer, it would be easier to visualize how AI affects the experience. So, I started where the experience is most recognizable -- the plain old process of getting a credit card and making purchases.

This led me to create an illustrated brief titled, “The Consumer’s Credit-Collections Journey, Powered by Artificial Intelligence.” Designed for legislators, regulators, industry participants, and anyone seeking to get their arms around how the latest technology affects the consumer experience, the brief provides a plain-English overview of what the consumer sees, what’s going on behind the scenes, and examples of the companies that provide the technology.

The illustrated journey begins with a consumer’s application for credit and continues through purchases, payments, and dealing with possible life curves such as fraud alerts, identity breaches, late payments or collection notices.

What became clear is that artificial intelligence -- especially in the form of machine learning -- offers a far more sophisticated approach to segmenting and servicing consumers in the manner best suited to the individual. Which means greater efficiency. And greater satisfaction on both sides of the communication.

What I also find exciting is the idea that machine learning can make it possible for call centers to benefit from the unstructured data they hold, such as call recordings or agent notes. If used correctly, this should be a benefit to consumers too. How often have you called customer service -- for the 3rd time -- only to have to repeat previously provided information because it didn’t fit neatly into a dropdown box or predetermined database field?

The next step, of course, must be a more effective transfer of information between clients (like lenders, healthcare providers, telecom companies, utilities, and others) and their service providers (like outsourced call centers) so that data is not lost or buried. But I digress.

This brief is intended to give regulator and industry stakeholders a place to start asking the right questions about the risks, the opportunities, and where they should focus their efforts as it relates to this fast-moving train of technology. I’d like to thank the members of the iA Innovation Council for their input.

You can download the brief here.

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Stephanie Eidelman is CEO of the iA Institute and Executive Director of the iA Innovation Council.