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

June 24, 2021 · Data Analysis,Machine Learning,AI,Start Here

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.