7 Reasons to Embrace Machine Learning to Detect Worker’s Compensation Fraud

Workers’ compensation fraud is more common than you may imagine. It may perpetrated by any stakeholder in the insurance ecosystem. Detecting fraud manually is an impossibly time-consuming task. Equally inefficient is meeting information requests from the District Attorney’s office or an external agency – if you’re still not using a new technology solution.

Specifically, solutions powered by machine learning can help insurers identify and pursue fraud proactively. Machine learning leverages analytics techniques that learn patterns in datasets without requiring human intervention. Analytic models built using machine learning evolve and adapt over time. That means they can discover previously unseen fraud methods, making fraud investigation and management more robust.

The question is, what are the ways in which machine learning can make workers’ compensation fraud detection vastly more efficient? Here are seven indications of how advanced analytics solutions are faster and better than traditional fraud identification techniques.

1. Makes fraud detection proactive rather than reactive

Advanced analytics models present critical information necessary to fraud detection from vast data sets. They represent the data in the form of graphs, charts and tables for easy human consumption. You can pin-point suspicious activity from these representations in an intuitive way.

Instead of burying into data once an instance of fraud has been reported, you can view suspicious patterns easily and in advance. In other words, you can actually investigate a potential fraud and prevent it from causing further damage.

2. Speeds up data gathering

The District Attorney’s Office of some counties have prosecutors who vertically prosecute all worker’s compensation fraud cases and have dedicated investigators. They usually lead aggressive campaigns to investigate and prosecute fraud. In the event of fraud or a tip-off, they may instruct you to provide fraud-related data.

In the absence of an advanced analytics software, data collection can take a couple of weeks. A machine learning-powered solution can create custom reports aligned to the requested information within a matter of minutes.

3. Offers instant access to provider profiles

Maintaining up-to-date provider profiles is a part-time job. Although it is essential to fraud management, it is not the high-value activity that fraud investigators should spend too much time on. An advanced analytics software can take over this administrative task of gathering data and updating profiles to support informed decision-making.

4. Shows links in an intuitive way

Medical provider fraud is a common type of worker’s compensation fraud. Kickback schemes with medical specialists and unnecessary services to collect insurance payment are typical instances. Software offering link analytics can show connections between providers, facilities, addresses as well as bring up details on each individual claimant or bill. A visual representation of this data allows you to draw inferences easily and accurately, helping you prove or disprove fraud.

5. Simplifies provider investigation

Following from the two points above, a machine learning fraud detection software plots double billing, denied claims, unbundling and other warning signs on a graph to provide a 360 degree view of providers. This is useful when fraud is suspected and you need more evidence to suggest that a particular provider may have perpetuated the fraud.

6. Performs big data analysis for claims research

Advanced analytics models may pull data from social media and the web in researching claims. As disparate sources of data are useful in this regard, machine learning can be employed to effectively collect anything on a browser.

7. Makes fraud detection teams self-sufficient

Back and forth between IT and fraud departments of insurance companies can take up time and seem burdensome. A machine learning-powered software saves many hours of fraud detection for both the investigation and IT units.

Proactive fraud detection minimizes losses and damages arising from worker’s compensation fraud. The right advanced analytics solution will go a long way in combatting fraudulent practices.

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The Aquilla platform leverages Artificial Intelligence and Machine Learning to assess healthcare data for suspicious patterns and proactively detects workers compensation fraud, Saving Insurance Companies Millions of dollars and weeks or work.

To make worker’s compensation fraud detection – Simple & Fast, request a demo HERE

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