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Insurance CIO Outlook | Friday, December 04, 2020
The advancement of data science technology has helped identify fraudulent activities, suspicious claims, and behavioral patterns using predictive analytics that uses statistical models for improved fraud detection.
FREMONT, CA: Navigating through complex claims procedures, pricing, and promotion, mitigate risks, cash repression, and more are some of the issues in the insurance industry. Advanced analytics uses big data for actionable insights, which is used for various business use cases. Insurers are gradually adopting advanced analytics to protect their businesses from risk and find new growth opportunities using customer data.
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Here are five use cases of advanced analytics in the insurance industry:
Lifetime Value Prediction
Customer Lifetime Value (CLV) is estimated using customer behavior data to decide the customer’s profitability for the company. Behavior-based predictive models are used to evaluate all the data on customers and predict customer buying and retention.
The models offer insights on the possibilities of customers’ behavior in handling or surrendering a policy. It can also be facilitated for creating market strategies as it indicates important customer traits.
See Also: Top Insurance Technology Companies
Detecting And Mitigating Risk In Real-Time
Advanced analytics is used to carry out real-time analysis that allows organizations to be swift and active in an unstable risk environment. In the case of vehicle insurance, advanced analytical modeling can help insurers precisely analyze the possibility of the driver in an accident by matching a driver’s behavioral data with the vast database on the behavior of other drivers.
Identifying Fraudulent Claims
The advancement of data science technology has helped identify fraudulent activities, suspicious claims, and behavioral patterns using predictive analytics that uses statistical models for improved fraud detection. The models use historical data on fraudulent activities to get to certain conditions that estimate the chances of fake claims.
A claim made by a person with a history of false claims is identified, the system stops the claim process and suggests an investigation on the case. Predictive modeling techniques are also used to evaluate patterns in fraud and the screening of false claims.
Impacting Customer Behavior
Insurance companies use advanced analytics to assess telematics data and impact customer behavior. In health insurance, companies can provide a holistic assessment of customers’ health by monitoring behavior and habits and help customers take care of their health, thus reducing the risks involved.
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