How Machine Learning is Transforming the Life Insurance Industry, ET BFSI

In the era of Industry 4.0, companies are building their competitive edge by combining data analytics and machine learning algorithms. The value of data is now widely recognized, and rapid technological advances and increasing data literacy are redefining the concept of “data-driven.”

The emergence of data-driven approaches allows companies across industries to gain valuable insight into customer behavior. Retailers can now personalize the shopping experience based on customer data, and food delivery services can now use order history to suggest menu items. Streaming platforms may also use user behavior analytics to recommend new shows and movies. These approaches enable businesses to predict behavior, identify problems and opportunities in real time, and connect more closely with their customers.

For example, according to a 2021 McKinsey article, “Predict the future of CX, a major airline deployed a machine learning system based on 1,500 customer operations and financial variables to measure the satisfaction and predictive revenue of over 100 million customers daily. The system identified and prioritized customers at risk of losing relationships due to delays or cancellations, and provided individualized compensation to maintain relationships and reduce customer dropouts on high-priority routes. . The possibilities for data analysis are endless, and with the right analytical tools, businesses can unlock unprecedented opportunities.

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How Machine Learning Models Work in Life Insurance

The life insurance industry is no exception, with insurers leveraging advanced machine learning solutions to help customers retain their policies and maximize product benefits. Insurers know that when customers churn, they not only lose the peace of mind that insurance provides, they also miss long-term financial goals.

Customer loyalty, also called persistence, is very important for life insurers. This is mainly because acquiring new customers is costly. According to Propeller, a leading global market research firm, the average customer acquisition cost for the retail industry is just $10, while the average customer acquisition cost for the global insurance industry is a staggering $303. Given the high cost of customer acquisition, it is imperative that insurers focus on retaining existing customers and ensuring that they continue to pay renewal premiums.

Higher sustainability leads to higher inflows of renewal premiums, which in turn increases assets under management. In addition, higher persistence reduces the amount at risk, thereby reducing the burden of paying claims. It also helps insurers improve profitability, reduce costs, and overall growth and development.

Briefly describe how a life insurance company implemented machine learning models to predict customer behavior and identify financial needs across three stages of the policy lifecycle.

Machine learning implementation

During the onboarding phase itself, machine learning models can help identify customers who are more likely to make fraudulent claims, who exhibit undesirable characteristics, such as a history of non-payment of insurance premiums. Early detection of these traits enables sales teams to take rapid action, provide appropriate interventions, and address customer concerns.

Then, during the renewal collection phase, the model analyzes the past behavior of existing customers to identify customers at risk of delinquent premium payments. Insurers can reach out to these customers and offer customized solutions for renewing policies.

Finally, machine learning models can help insurers identify customers whose policies have expired but who may be reinstated. This will enable insurers to offer personalized solutions, motivating customers to reinstate policies and continue investing over the long term.

Using these advanced machine learning models, life insurers can predict future sustainability trends and optimize efforts to improve overall sustainability. For example, ICICI Prudential Life Insurance has effectively incorporated advanced machine learning models into its operational framework to enable targeted interventions for different customer segments. As a result, we were able to effectively respond to customer inquiries and improve retention. In the 2023 financial year, the company achieved his staggering retail renewal premium of Rs 13 million. 52.6 billion.

As the world becomes more digital, businesses are turning to data-driven approaches to gain insight into customer behavior. By harnessing the power of technology, businesses can collect and analyze vast amounts of information to predict future trends. This ushers in a new era of customer centricity where businesses can customize their services to meet the needs of different customer segments. With the right safeguards in place, businesses can use data to create more personalized and enriched customer experiences.

This article was authored by Dhiren Sarian, CFO, ICICI Prudential Life Insurance Company. All views expressed are personal.

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