“It’s about being able to map inputs to outputs in a way that we as humans can’t comprehend,” says Justin Bass, chief data science officer at the accounting, consulting and technology firm Crowe. In other words, ML takes a mass of data and determines which rules are needed to arrive at a desired outcome. Over time, it can even track patterns and “learn” from its data environment, suggesting solutions to problems that programmers had not necessarily flagged.
Crowe, however, focuses on a third category that lies somewhere between the two, which Bass calls “product machine learning” — ML that’s integrated into existing software solutions to offer expanded functionality at scale. The value of developing software that allows the computer to define its own rules becomes clear when you consider its practical uses — like the following solutions ML provided for three common industry pitfalls.Crowe’s first foray into product machine learning came in the cyclical area of medical billing.
“Historically, it would take 15 minutes per account to resolve, and medical practices would have a whole staff of people who are dealing with this,” says Bass. The benefit of ML-equipped software for balance resolutions is not just automation but also pinpointing what types of inconsistencies to look for in the first place. The need for ML-equipped solutions to help bail out accounts receivable departments became increasingly apparent — which led to the development of. Using ML, the software looks at how past accounts were resolved and seamlessly decides on the course for outstanding balances.