Eight use cases for machine learning in insurance

Insurance companies that sell life, health, and property and casualty insurance are using machine learning (ML) to drive improvements in customer service, fraud detection, and operational efficiency. For example, the Azure cloud is helping insurance brands save time and effort using machine learning to assess damage in accidents, identify anomalies in billing, and more. Here are some common use cases for ML in insurance, along with resources for getting started with ML in Azure. Eight ML use cases to improve service, optimization, automation, and scale Lapse management: Identifies policies that are likely to lapse, and how to approach the insured about maintaining the policy. Recommendation engine: Given similar customers, discovers where individual insureds may have too much, or too little, insurance. Then, proactively help them get the right insurance for their current situation. Assessor assistant: Once a car has been towed to a body shop, use computer vision to help the assessor identify issues which need to be fixed.

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