Many different industries are becoming more reliant on machine learning. The insurance industry is among those that has found new opportunities to take advantage of machine learning technology. Life insurance companies in particular are discovering the wondrous opportunities that AI provides, since this sector faces some unique challenges relative to other insurance offerings.
The market for data analytics in the insurance sector is projected to be worth nearly $22.5 billion within the next six years. Many of the applications of big data for insurance companies will be realized with machine learning technology. A growing number of AI features are proving to be tremendously beneficial for life insurance companies.
The most obvious benefit of AI in the life insurance sector pertains to actuarial analysis. However, there are multiple other reasons that life insurance companies use machine learning as well.
One way that it can help is when customers need a lump-sum cash payment. AI can help companies determine the fair market value of their policy when they want to liquidate it for cash.
Huge databases and standardized processes are widely used in the insurance sector. This definitely makes the insurance industry one of the biggest consumers of machine learning technology. According to a study by Capgemini (2019), 34% of respondents from insurance companies confirm the use of machine learning (AI) in operations. Comprehensive digital visions and AI strategies, on the other hand, are still a rarity in this sector.
According to the Capgemini study mentioned above, the possibilities of AI in the insurance industry are being explored primarily by means of pilots and proofs of concepts. One of the biggest examples is in the area of automated claims assessment. In particular, AI analysis techniques are already being used for targeted, personalized marketing. However, if insurers want to achieve measurable success in customer experience management, they need a cross-departmental data flow that allows highly detailed knowledge about customers to be generated quickly and automatically. These insights can then be used for even more targeted marketing actions.
Capgemini states that many insurance companies recognize the need to invest more in AI. “Banks and insurance firms are deriving high benefits from AI in customer engagements, yet the initiatives are not scaled,” the report states.
With machine learning platforms, which have the advantage of learning from the processing and analysis of large volumes of historical and real-time data, even more use cases are conceivable. In interaction with CRM and with marketing automation, insurers can personalize their customer communications based on the life events of their customers. In this way, insurers benefit from highly personalized and automated customer communication, optimize their processes, and open up access to new business models.
5 Ways AI Can Be Used By Life Insurers
Machine learning is changing the life insurance sector in very significant ways. The benefits go far beyond using AI to improve on the actuarial process, although that is clearly one of the most important benefits. Here is a succinct overview of the most important benefits of AI for life insurance companies:
- Up- and cross-selling: By analyzing customer data and interaction histories, the insurance situation of customers can be analyzed carefully and additional needs can be derived. This enables the insurer to propose new products to the customer.
- Reducing the churn rate: The algorithms are trained with huge amounts of data that the insurer already has on hand. It can learn to recognize patterns over time. If a customer has already filed multiple complaints with the insurer, it could mean they are at risk of churn. Proactive outreach by the insurer or agent strengthens customer loyalty.
- Claims management: Data analytics and machine learning are particularly helpful with insurance claims management. Pattern recognition in current and historical data makes insurance fraud more quickly identifiable. By analyzing historical data in combination with image recognition, insurers can automatically determine the severity of damage – for example, after storms – and repair cost estimates more quickly.
- Individual insurance solutions and ecosystem building: Individual insurance policies with flexible terms can benefit from AI tools. It is also good to develop ecosystems in cooperation with companies from other industries. These partnerships are becoming increasingly important, as shared data helps all parties improve their own business models. For example, Basler Versicherungen, together with the Swiss furniture store “Möbel Pfister,” offers customers insurance coverage for all furniture purchased from Pfister.
- Underwriting and risk assessment: Up to now, assessment has been a relatively complex process, depending on the insured event. Risks can be assessed faster and more solidly on the basis of historical transaction data than with manual processing.
All of these benefits are helping drive the remarkable growth of AI in the life insurance sector.
AI is a game-changer for Life insurance companies
Life insurance companies are continuously looking for new solutions to expand their market and improve their ROI. Machine learning is demonstrating remarkable potential
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