Common Predictive Analytics Process Areas and Use Cases in Insurance

CIO Review APAC | Monday, July 19, 2021

Predictive analytics uses historical data to make predictions.

FREMONT, CA: The technological landscape evolves, and as a result, industries do as well. While businesses are hesitant to invest in costly automation tools, some enhancements are universally welcomed.

The insurance industry's abundance of data makes it an ideal environment for implementing predictive analytics. This tool is handy for developing fundamental patterns, gaining essential insights into the insurance business, and managing complex relationships between agents and clients.

In practice, predictive analytics benefits the insurance industry primarily through improved claims management and more transparent underwriting services. It transforms the sphere into a data-driven, predictive, and efficient system through artificial intelligence, machine learning, and behavioural intelligence.

Common Predictive Analytics Process Areas

Life Insurance Predictive Analytics

Key industry players rely heavily on predictive analytics to mine Big Data and identify potentially valuable connections in life insurance. In this context, the insights generated by this processing include a more effective response to common concerns. They incorporate complex bureaucracy associated with underwriting and additional time spent on registration.

Simultaneously, purchases represent a blind spot in predictive analytics for life insurance. Their modelling presents some challenges due to a shortage of data for low-frequency periods. That is why, in life insurance analysis, the primary focus is on bringing the underwriting process up to date.

Health Insurance Predictive Analytics

Predictive analytics in health insurance gains various stakeholders, including health agencies, hospitals, and medical providers. Its primary enhancements focus on business operations, operational fidelity, and treatment adjustments.

Specifically, the use of real-time reporting ensures that adjustments to the changing environment are made promptly and precisely, including rapid adaptation to the changes. The use of predictive analytics in health care also raises concerns about the potential moral hazard associated with reasonable risk control. That is why the application of this analytical tool should be approached with extreme caution.

Predictive Analytics in Insurance: Use Cases

Calculation of Prices and Risk Mitigation

Data insights derived from first-hand information enable more effective decision-making. This is because the availability of numerous sophisticated data sources allows the conclusion of good customer behaviour. Social media posts, smart technologies, and claims are all highly reliable sources of information in and of themselves. And the ability to aggregate and analyse them in one location offers numerous benefits to insurers.

Client Support

The significant advantage of predictive analytics is its ability to rescue clients on the verge of losing them. The data insights can identify unhappy visitors or those who do not fully utilise the insurance service and address their concerns long before they cause an insurance breakup.

Preventing Fraud

Predictive analytics has already begun to manage fraud risk effectively. In this context, social media is an ideal platform for gathering intelligence and addressing threats.

Tracking Trends

Predictive analytics can help maintain a competitive edge by monitoring and alerting to emerging trends. In practice, insurers can develop new products, create customised customer experiences, and incorporate necessary technological solutions.

See Also: Top 10 Retail Banking Solution Companies

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