Predictive analytics is a term used to describe the use of statistics and modeling techniques to make predictions about future outcomes. An essential component of predictive analytics is machine learning. In healthcare, predictive analytics has been used to learn about the characteristics of diseases, how it affects individuals, and spread within communities. In workers’ comp, predictive analytics can provide valuable insight into various parts of the claim process, from measuring claim severities to measuring a TPA’s overall performance. Though machine learning in predictive analytics continues to evolve, it is impacting the way claims management professionals identify and leverage data trends today.
Recently, CorVel sponsored a survey of risk and claims management professionals to better understand their current use of data and their level of interest in advanced data analytics. 93% of respondents agreed dynamic claim practices developed via machine learning were the wave of the future; however, nearly half reported that they do not currently use predictive analytics. Moreover, survey results found that 66% of respondents receive limited to no strategic consulting.
Leveraging predictive analytics is an area of opportunity for the risk management industry. However, many risk management professionals lack the resources and capabilities essential to gather and analyze data effectively. In anticipation of these needs, CorVel has developed CogencyIQ, a complete data analytics solution now available to clients. We have proactively expanded our analytics team to include data scientists, machine learning and predictive analytics experts, and senior thought leaders with top-level solutions expertise and deep market knowledge. By advancing cutting-edge analytics in workers’ compensation and liability claims management, CorVel’s CogencyIQ offers claims professionals insight consulting and even greater data analysis, recommendations, and control of risk.