Artificial Intelligence for Insurers: Examining the Benefits and Risks

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SCOR’s AI and the Future of Insurance publication discusses several critical points to consider in adopting artificial intelligence (AI) to insurance businesses. In this article, we’ll examine SCOR’s insights into the benefits and risks that insurers must consider before investing in AI.

Benefits of AI adoption

AI adoption comes with many benefits and risks that affect both consumers and insurers.

For consumers, the key benefits of using Large Language Models (LLMs) and AI include:

  • Automating the underwriting and claims processes leading to shorter turnaround times and cost reduction
  • Generating generic financial advice automatically and, in the future, tailored financial advice as the solution advances
  • Assisting policyholders in determining if they can make a claim
  • Helping applicants understand terms and conditions
  • Automating changes to policies
  • Dynamic engagement tools

For insurers, AI and LLM bring many benefits such as:

  • Reading medical evidence in underwriting and claims process for life and health insurance applications
  • Writing administrative or claims letters to policyholders
  • Querying contracts with third parties (e.g., with reinsurers or distributors)
  • Summarizing data (e.g., portfolio performance)
  • Improving risk modeling and setting actuarial assumptions
  • Automating tasks

AI’s ability to benefit consumers and insurers makes adoption a valuable opportunity. Insurers must avoid over-expectations that may lead to disappointment, but a significant interest in the technology’s potential is certainly warranted.

Read: AI and the Future of Insurance

Risks of AI adoption

AI adoption also comes with a considerable number of potential risks, pitfalls, and challenges that require insurers’ attention. These include:

  • Privacy and data security issues: As the usage of AI-based tools rose, so did concerns around privacy and data security. Insurers need to prioritize, invest in, and adopt strong data protection policies, always using the most updated technology.
  • Data accuracy: We need human intervention to review anything intended directly for customers to ensure no incorrect information is sent. We also need to ensure we are not making wrong decisions based on incorrect information. AI is not able to generate perfect responses 100% of the time. We need to be careful with ensuring the right information is given to customers, particularly at sensitive times like the point of claim.
  • Public and policyholders’ perception: Even if AI can be right 100% of the time, we still need to be mindful of the public perception of having AI answer their queries. Having AI assess insurance claims, particularly in declined cases, could increase insurer reputational risk. Insurers must be conscious of this risk and take a sensible approach to avoid their AI deployment to cause public and our policyholders’ mistrust.
  • Regulatory scrutiny: Insurers need to be mindful of adhering to relevant regulations. Many countries have strong regulatory controls around who (or what) can give financial advice. In the short term, AI is probably best placed to assist financial advisers or just offer generic information rather than tailored personal recommendations to people.

Generative AI and LLM offer vast opportunities in the insurance industry. Considering the benefits and mitigating the risks of the technology can help insurers achieve optimum efficiency and return on the AI investment.

Read more about the opportunities, challenges, adoptions, and ethics of AI in SCOR’s AI and the Future of Insurance publication. To find out more about how our digital solutions can help you to support your policyholders and grow your business, head here.