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Predictive Analytics in Insurance
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Predictive Analytics in Insurance

Predictive Analytics in Insurance
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In Insurance industry the insurer, sells the insurance to the insured for a premium, the premium being the  amount of money charged for the insurance coverage. Predictive analytics is used in appraising and controlling risk in underwriting, pricing, rating, claims, marketing and reserving in Insurance sector. According to the published marketing studies, predictive analytics is used in many of the large insurance companies in the areas of underwriting, claims and marketing.

Underwriting: In underwriting the business model is to collect more in premium and investment income than is paid out in losses. This need to be done by offering a competitive price which customers will buy insurance. Modeling with the data using predictive analytics is used in pricing analytics.

Pricing/ Rating : Rating for different risk characteristics involves comparing the losses with loss relativities using multivariate analyses. Other statistical methods used are univariate analysis and probability of future losses. Predictive Analytics is used in the ratemaking, the price setting of policies in the actuarial science, where the statistics and probability to approximate the rate of future claims based on a given risk.

Claims: Claims may be filed by insureds directly with the insurer or through brokers or agents. Modeling with the data using predictive analytics is used in claim fraud analytics and claim provider fraud analytics.

Marketing: Insurers will often use insurance agents to initially market or underwrite their customers especially on segmentation, channel analytics and customer retention.

Reserving: Loss reserving or Outstanding claims reserves is the calculation of the required reserves for a tranche of general insurance business. The reserves are calculated by forecasting future losses from past losses and the statistical methods commonly used are the Chain Ladder Method and the Bornhuetter Ferguson Method.

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