Thinking Differently: The 4 Most Common Mistakes Plaguing Data Science and AI for Debt Collection

Published on June 30, 2021 at 3:00 pm

This article is part of the iA Think Differently series. Written by or recorded with members of the iA Innovation Council, the series of articles and videos showcases thought leadership in analytics, communications, payments, and compliance technology for the accounts receivable management industry.

WHAT YOU'LL LEARN: How to fix the four most common mistakes behind failed AI / machine learning implementation for debt collection, including: having a company vision misaligned with its tech strategy, a failure to look outside the industry for models and solutions, poor data hygiene, and building models in a vacuum.

There has been an explosion of data science* and machine learning** tech designed for ARM / collections companies and those companies are definitely paying attention. According to a 2020 trends survey from Interactions, over half of ARM participants think of themselves as “innovative” or “disruptive.” What most companies mean by this is that they have invested (or would like to invest) in the tools that define "innovative" and "disruptive" in 2021: data science, machine learning, and AI.

But here's the thing many of these organizations may not understand: no company can realize the promise in those tools unless they have a solid foundation in basic data hygiene

The Path to Innovative Starts with Basic

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