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How Machine Learning and AI Can Help Reduce Risk

This article was first published in WorkCompWire

Risk management is integral to insurance, but it’s traditionally been an inexact science. Thanks to recent technological advances, however, risk management is about to get a long-overdue upgrade.

If an eyebrow is raised, it is likely because the insurance industry has been slow to adopt technology, but artificial intelligence (AI) and machine learning are making headway. The appeal in using data to predict outcomes, drive efficiency, and reduce costs has sparked intrigue and curiosity. Tack on its ability to make jobs easier and facilitate claims faster, and even the biggest skeptics, those most resistant to change, are curious about how AI can be applied.

Despite the relative aversion to tech or potentially costly, time-consuming operational overhauls, AI systems already have been put to work in some of the world’s largest insurance organizations where they are used to address highly specific issues that have plagued different sectors for years. Now, the time has come to consider how AI can make a positive impact on risk management.

New Data, New Insights
Much of the information risk managers find valuable in making assessments is not readily accessible to them today. Data in claim notes, documents, images, even injured worker sentiment requires someone manually pouring through files because this type of information can’t be entered or sorted in conventional systems very easily. But, new AI-based systems are capable of incorporating and analyzing these forms of unstructured data. They make it much simpler for employees — even the least tech-savvy employees — to find and interpret the elements that will be the most crucial to their decisions.

Additionally, the more AI-based systems “read,” the faster and better they learn and understand. Models that leverage unstructured data yield more accurate and detailed analysis, and by enabling adjusters to make more informed decisions based on data, organizations can reduce the severity and frequency of claims. This makes everyone happy. The industry can move light years forward by delivering this kind of data and analysis to risk managers’ fingertips whenever they need it.

Group Analysis
Another way in which new AI-based systems can help risk managers is by analyzing data across groups. It’s far more efficient to grasp what is happening across a portfolio or set of claims when a machine generates a report vs. reading file after file to formulate an opinion. With new tools, risk managers easily can look across very large datasets to see what’s happening collectively. They can determine the macro impact instead of relying on an isolated view of a single claim. In addition to the time and resource advantages, AI-based software spots trends and outliers that cost money unnecessarily.

Collective View vs. Limited Project Basis
AI models also are able to draw on a wealth of historical information — information that is constantly updated. This stands in contrast to the way the world of risk works today, where most analysis is conducted on a project basis. The project ends, so does ongoing data collection. Important information is often lost in the lapse between projects. Modern AI systems solve the issue by persistently refreshing to ensure updated reports can be ready on demand. The result is a much richer and more realistic picture of what is happening in an organization’s claims.

Power of Prediction
The gold for risk management, however, lies in AI-based solutions’ ability to predict outcomes. AI applies science to risk management based on an incredible number of data points that should be considered in helping teams prepare for the future. Modern systems show risk managers the behaviors that need to change, assumptions that are incorrect, and what things will look like if they continue to follow the present course. This information is so important because every customer or risk manager has observed different behaviors, which shape their views and how they conduct their jobs. AI systems weigh and parse all of this behavior to give a far more comprehensive view. Systems then can alert users to adverse trends that are developing so that teams are able to adjust accordingly. This not only decreases the lifespan of claims but potentially can save millions of dollars.

To gain the best predictions, however, it is necessary to use a platform solution that lets users easily gather insights and create models that learn from the entire industry, not just their own data. They then apply that information to a specific customer’s data. The more data a system can analyze, the more patterns come up, yielding more precise and valuable predictions.

Armed with an abundance of data that is simple to access and interpret, claims managers can do their jobs faster and easier than ever before. This can make a potentially huge positive impact, not only on their own organization but also on the larger sector. As machine learning and AI-based technologies mature further and are more widely adopted, the industry will become more exact in its nature. By doing so, it will drive costs down and efficiency up, ultimately helping to transform the insurance industry for the better.

About Pramod Akkarachittor
Pramod Akkarachittor, vice president of products at CLARA Analytics, has more than 20 years of enterprise product management and development experience. He is charged with overseeing products across the CLARA platform, ensuring an optimal user experience and aligning features with market demand. Mr. Akkarachittor received a B.E., Computer Engineering, from Pune Institute of Computer Technology and an MBA, Marketing, from the University of California, Berkeley – Walter A. Haas School of Business. For more information, visit

Team CLARA Analytics

CLARA Analytics is the leading AI as a service (AIaaS) provider that improves casualty claims outcomes for commercial insurance carriers and self-insured organizations.

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