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While most insurance claims are processed without a hitch, there are cases where an insured individual enters into litigation with their insurance carrier on the financial terms of a claim. In this case, the insurance carrier will attempt to offer a settlement outside of court where they can come to a mutual financial agreement with the insured. For the insurance carrier, engaging in litigation translates into years long disputes that cost significant amounts of money and time, and they will make their best efforts to prevent this from happening. Unfortunately, the insured are not always able to come to an agreement with their insurance carriers when cases have escalated for too long and become difficult to diffuse.
AI helps you predict from first notice of loss which claims have a high risk of going to litigation. By learning the patterns in your data that reveal characteristics of past claims that went to litigation, AI will be able to infer which new claims will go to litigation based on similarly concerning characteristics. Unlike manual reviews where cases can only be assessed throughout limited intervals of time, AI automatically reruns its predictions as new information about a claim comes in, meaning that a claim’s risk of litigation will continuously be updated throughout the lifecycle of a claim until it is fully processed. Claims that have a high risk of litigation can be assigned to a claims specialist who will avert the claim from escalating in tension. AI informs your claims specialists of the top reasons why the claim has high risk, giving them actionable intervention steps they can take personalized to every claim and insured individual. This helps your insurance carrier reduce attorney related costs and avoid other risks of litigation such as loss of goodwill from the public.
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Insurance companies are using machine learning and AI to increase top and bottom line through gaining competitive advantages, reducing expenses, and improving efficiencies. They are optimizing all areas of their business from underwriting to marketing in order to make data-driven decisions to lead to increased profitability.