Create use_cases_insurnace.md

Author: Amogh Singhal

use_cases_insurnace.md

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+###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. This helps accuracy, speeds an assessment, and keeps the customer informed with any repairs.
+Property analysis: Given images of a property, identifies structures on the property and any condition issues. Insurers can proactively help customers schedule repairs by identifying issues in their roofs, or suggest other coverage when new structures, like a swimming pool, are installed.
+Fraud detection: Identifies claims which are potentially fraudulent.
+Personalized offers: Improves the customer experience by offering relevant information about the coverage the insured may need based on life events, such as the birth of a child, purchase of a home or car.
+Experience studies: Uses unsupervised machine learning to discover predictors in claims activity. This information can help set assumptions and feed into activities such as pricing models, risk analyses, and other actuarial analyses.