How Machine Learning and AI Reduce Risk

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 the 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 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 help with risk management. New Data, New Insights Much of the information that risk managers value in making assessments is not readily accessible to them today. Data in claim notes, documents, images, even injured worker sentiment requires someone manually poring through files because this type of information can’t be entered or sorted in conventional systems easily. But new, AI-based systems can incorporate and analyze 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. See also: How Machine Learning Transforms Insurance   Additionally, the more that 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 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 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 can 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. See also: Key Challenges on AI, Machine Learning   Armed with an abundance of data that is simple to access and interpret, claims managers can do their jobs faster and more easily than ever. 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 and are more widely adopted, the industry will become more exact. Costs will drop, and efficiency will improve, ultimately helping to transform the insurance industry. As first published in WorkCompWire.