For life insurance providers, AI offers the chance to increase revenue, improve efficiency, and reduce risk. Among the many benefits of AI are the ability to improve mortality and lapse predictions and optimize decision-making to help build and sustain profitable customer relationships. 

Hand coding models cannot possibly meet the demand or realize the incredible opportunities that AI can provide. Even though insurtech companies are innovating by leaps and bounds, established insurers actually have more expertise and data, which is critical for the success of AI in insurance applications. They can harness this data to become AI-driven, propelling their business forward. 

Specifically, AI can help life insurers: 

1. Identify and retain the right customers, and keep them happy and engaged.

2. Offer the most appealing products to each customer profile.

3. Improve mortality and other actuarial assumptions, even undiagnosed risks.

4. Accelerate the underwriting process.

5. Improve distribution effectiveness.

There are opportunities to optimize every department of a life insurance company:

Marketing and Sales. An AI solution can help with customer segmentation, opportunity targeting, prospect pre-qualification, product recommendation and personalization, and calculating customer LifetimeValue (LTV). AI-powered decision-making can be applied to lead scoring, cross-selling and upselling, lapse prediction, and agency performance optimization. 

Product and Underwriting. AI can help with individualized pricing, disease severity prediction, submission prioritization, and rapid product development. Your solution can work with your team to determine underwriter assignment, prioritization of questions, and underwriting automation. 

Engagement. Non-renewal costs insurers money, and AI can predict the likelihood of churn. These predictions can be used to determine the appropriate renewal price change (RPC).

Reserving. Your AI solution can help you determine individual mortality reserving, as well as individual persistence estimating and overall profitability and cash flow. 

The Proof Is in the Profit

As previously noted, non-renewals cost insurers money. Among our clients, we saw one insurer who tackled this problem to reduce churn by 1% and variable costs by 24%. This resulted in an estimated savings of over $400,000 per year. 

In order to tackle their marketing issues, another large insurer looked at their current customer base and determined that in the 50-85 age range, there are 100 million people — far too many to target with a direct mail campaign. Instead, they identified a smaller group of people who were more likely to respond to marketing campaigns, greatly reducing their marketing costs through greater personalized messaging.

Another large insurer was looking to avoid underwriting unprofitable policies that are likely to lapse within a year. By predicting and avoiding the policies most likely to lapse, this company was able to save $15 million with their AI solution. 

Across the board, life insurers are discovering that when they combine their data with an AI solution, they can reap enormous benefits, such as increasing revenues, reducing costs, and growing their businesses.