Machine learning in insurance
Insurance companies are using machine learning 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.

Use cases in Insurance

Claim Development Modeling

Out with the old, in with the new....newer machine learning algorithms are allowing insurance companies to build more robust mechanisms for predicting, once a claim occurs, how much it will ultimately cost.

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Claim Payment Automation Modeling

Time is money, for your business and for your customers. Use DataRobot to model when autopaying claims is the best option. Shortening the claim cycle drives costs down and customer satisfaction up.

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Conversion Modeling

The ability to predict which segments are most likely to convert from a quote to a policy allows insurance companies to optimize their pricing algorithm and their marketing spending, leading to data-driven objective business decisions.

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Fraudulent Claim Modeling

The cost of fraudulent insurance claims is in the billions. Accurately predicting claims legitimacy significantly reduces fraudulent payouts and leaves the insured with a positive customer experience.

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Insurance Pricing

To be profitable in the insurance industry, you must avoid being adversely selected against. To avoid this and maintain your underwriting margins requires highly accurate predictive models.

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Life Insurance Underwriting for Impaired Life Customers

Typically, unless a reinsurance company covers the risk, direct insurance companies do not underwrite life insurance for individuals who have suffered a serious disease and are in a situation of “impaired life." A reinsurance company wants to predict which customers have positive health prospects and are insurable.

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