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The Cutting Edge: AI's Expanding Role in Life Insurance

By Brian Barnes

NewsDirect, August 2024

In an era marked by technological innovation, Artificial Intelligence (AI) is starting to have a meaningful impact in the life insurance industry, transforming traditional practices and introducing new methodologies in risk assessment, policy personalization, fraud detection, and customer service. Currently, AI algorithms enhance accuracy in risk assessment by analyzing comprehensive datasets that include medical records and lifestyle information, enabling more personalized policy pricing. Additionally, AI-driven chatbots and virtual assistants are improving customer engagement by providing round-the-clock service and efficiently handling queries and claims. AI also plays a pivotal role in automating routine, heavily manual, processes such as data entry, commission reconciliation and claims processing, significantly speeding up these operations. Sophisticated AI tools are increasingly utilized to detect fraudulent activities, safeguarding companies against potential financial losses.

Future State of AI

Over the next three to five years, the impact of AI in life insurance is poised to deepen further. Insurers are expected to leverage AI to offer highly customized policies that adapt in real-time to the changing needs of policyholders. Startups like Leopard and Atidot are beginning to show green shoots by providing best-fit coverage recommendations for agents, brokers and consumers. Predictive analytics will advance, enabling insurers to forecast long-term health trends based on genetic information and lifestyle choices, which will aid in crafting precisely targeted insurance products. Moreover, the use of telematics is anticipated to expand, allowing insurers to monitor health parameters continuously, thus enabling dynamic pricing models. According to a recent study by Bain & Company the application of generative AI in insurance distribution could yield over $50 billion in annual economic benefits. These benefits would come through increased productivity, more effective sales and advice, and reduced commissions as direct digital channels gain share. For individual insurers, the technology could boost revenues by 15% to 20% and cut costs by 5% to 15%.[1]

Looking beyond the next five years, the prospective applications of AI in life insurance venture into even more speculative territories. The integration of AI with the Internet of Things (IoT) could provide real-time health data, leading to innovative "pay-as-you-live" insurance models that adjust premiums based on daily activities and health conditions. Furthermore, AI may become instrumental in underwriting policies related to anti-aging therapies and longevity, with algorithms tailored to predict life expectancy improvements driven by scientific advancements. As AI continues to embed itself deeply within the life insurance sector, the importance of addressing ethical concerns grows, highlighting the need for transparency, privacy, and the elimination of algorithmic bias, ensuring AI's role enhances rather than diminishes the human aspect of insurance. These advancements promise not just enhanced efficiency but also greater adaptability to meet the evolving demands of consumers.

The Challenges in Leveraging AI

Despite the promising benefits of AI, areas of consideration carriers and distributors face remain:

  1. Data Integration: Integrating data from various sources, such as CRM, AMS, policy admin systems, and paper, into a coherent structure is complex. Inconsistent data formats and siloed data systems can hinder the seamless application of AI.
  2. Data Quality: AI algorithms are only as good as the data they analyze. Poor data quality, including incomplete or outdated information, can lead to inaccurate risk assessments and policy recommendations.                                   
  3. Data Privacy and Security: Handling sensitive data such as genetic information and personal health records necessitates robust security measures. Ensuring data privacy while integrating and analyzing large datasets is a significant challenge.
  4. Legacy Systems: Many insurers operate on outdated technology platforms that are not compatible with modern AI tools. Transitioning to newer systems that support AI applications requires substantial investment and time.
  5. Regulatory Compliance: Navigating the regulatory landscape to ensure compliance with data protection laws adds another layer of complexity. Insurers must balance the innovative use of AI with adherence to stringent regulations.
  6. Bias Risk in AI Systems: AI tools can inadvertently perpetuate existing biases present in the data, leading to discriminatory pricing or coverage offers. Developing AI systems that identify and mitigate such biases is critical for fair and equitable service delivery.
  7. Nascency Hurdle for Specialty Brokers: Specialty brokers, who rely heavily on localized relationships and specialized market knowledge, may find it challenging to integrate AI systems designed for broader applications. Customizing AI tools to fit niche markets is necessary to support the unique needs of these brokers.   
  8. Integration Challenges Post-Consolidation: The consolidation of smaller brokerages into larger entities presents additional challenges in AI integration. Establishing a strong technological infrastructure capable of supporting AI across different systems, data structures, and locations is crucial for seamless operations post-merger.

To effectively leverage AI, carriers and distributors need to address these challenges. This might involve investing in modern data infrastructure, adopting industry standards addressing bias, bespoke integration plans for smaller firms, and implementing stringent data governance practices.

Five Steps to Get Started with AI

  1. Assess Data Readiness: Evaluate the current state of your data. Ensure it is clean, organized, and in a consistent format. Address any data quality issues and integrate data from various sources into a unified system.
  2. Invest in Technology: Upgrade legacy systems to modern platforms that support AI applications. Invest in AI tools and technologies that can analyze large datasets and generate actionable insights.
  3. Start Small: Begin with pilot projects that have clear objectives and measurable outcomes. This could be an AI-driven chatbot for customer service or an AI tool for automating underwriting processes like APS summarization. Potential partners like Fintary who offer low lift AI implementation in commission processing and reconciliation are great places to get started.
  4. Build Expertise: Develop in-house AI expertise by hiring data scientists and AI specialists. Provide training for existing staff to understand and work with AI technologies.
  5. Partner with Experts: Collaborate with AI startups and technology providers who have the expertise and tools to implement AI solutions. Leverage their knowledge to accelerate your AI adoption process.

Statements of fact and opinions expressed herein are those of the individual authors and are not necessarily those of the Society of Actuaries, the newsletter editors, or the respective authors’ employers.

Brian Barnes is VP partnerships with SCOR and incoming president of the Life Insurance Direct Marketers Association. Brian can be contacted at bbarnes@scor.com.

Endnote

[1] Sean O'Neill, Rebecca Stephens-Wells, Bhavi Mehta, and Harshveer Singh, “It’s for Real: Generative AI Takes Hold in Insurance Distribution.” April 1,2024. https://www.bain.com/insights/its-for-real-generative-ai-takes-hold-in-insurance-distribution/.