Using AI to Understand Workplace Risk

US companies pay an estimated 62 Billion dollars per year on workplace injuries, according to the U.S. Bureau of Labor Statistics (BLS) and the National Academy of Social Insurance.  The risk for each company is different and unique but the problem is always the same: how do companies understand where they are most at risk?

Managing workplace risk properly begins with understanding your claims.  The claims process is an arduous exercise with many of the dollars spent not only on paying out claims, but in the due diligence of each and every incident.  However, this critical (and expensive) information is discarded once the claim has been closed.

Claims have an abundance of information included in each and every report.  Whether it is a slip and fall, car collision, or muscular injury, claims data includes a massive amount of text and specificity for each incident.  The amount and specificity of the data is so large that it cannot be fully examined and aggregated.  Instead, simply “slip and fall” or “muscle strain” may be the only pieces of data recorded after the claim process has been closed.

Working with an insurance broker, Signafire applied its Hailstorm and Aperture platforms to fuse and enrich thousands of workers compensation claims, identifying not only common types of injuries, but more importantly, common causes of injuries unique to each industry and company.   Applying artificial intelligence and data mining to uncover the cause instead of the outcome allows companies to identify where, when, and how accidents occur, enabling them to be proactive in risk mitigation and cutting overall claims.

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