INCREASING ACCURACY OF INSURANCE
PROJECTIONS

Challenges

Millions of Americans will lose their jobs but changes to people’s health insurance coverage might not be as widespread as anticipated. Health insurers oftentimes struggle with accurate forecasts, thus throwing off the accuracy of their underwriting models.

Solution

Our system automatically mines public data from multiple websites that contain employee hire and layoff data, insolvency and bankruptcy reports, etc to provide live estimates using several models. All gathered company information is aggregated in one database, and later cross referenced with our client’s enterprise data. Our system increases model accuracy by feeding better data.

The solution results in more accurate projections by correcting the underwriting estimates on how many people will need to be insured in the specified timeframes.

Results

⦁ Significant increase the accuracy of underwriting projections
⦁ Defined list of companies which are are currently hiring new employees and are expanding the payroll capacity
⦁ Constantly self-updating list of potential commercial clients that our client should target
⦁ Predictive models allowing to increase our client’s profitability and reliability

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