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How AI and Machine Learning Can Fight Health Care Fraud

Blog: Enterprise Decision Management Blog

Medical insurance claim form

Health care payers use a variety of tools and solutions to fight fraud, waste and abuse in their fee-for-service healthcare claims. Recently, artificial intelligence (AI) and machine learning have become popular concepts in payment integrity. But how can they help reduce your losses to health care fraud, waste and abuse?

First, let’s look at what is meant by AI and machine learning in this space.

One way to think about AI is that it emulates a human who is evaluating claim, provider and member/beneficiary risk for fraud, waste and abuse. Think of AI as an individual who can assess each of your claims, providers and members or beneficiaries for risk of health care fraud, waste and abuse with “always on” reliability, keen insight, and consistent results. And AI is programmed to look for suspicious behavior and anomalies – things that don’t look right.

Machine learning learns non-human programmed insights by analyzing data and discovering patterns that a typical human analyst would not.  These insights may be driven by changes in benefits or membership, or changes in claiming behavior in response to new payment integrity controls.

Although rules-based payment integrity solutions play an important role in payment integrity they are only simple AI, as they do not utilize machine learning. AI and machine learning in combination are found in the leading analytics-driven decision management systems for healthcare payment integrity, and are often used in conjunction with systems that use rules.

Putting AI and Machine Learning to Work

How do you use AI and machine learning to best effect in your payment integrity program? We recommend that you consider the following things:

We welcome your comments. For more information, see our white paper on Uncovering More Insurance Fraud with Analytics.

The post How AI and Machine Learning Can Fight Health Care Fraud appeared first on FICO.

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