The State of AI Transformation in Insurance

Fueled by the power of generative AI (GenAI), traditional forms of artificial intelligence are also advancing. Recent innovations in machine learning (ML), robotic process automation (RPA), natural language processing (NLP), voice technologies, and computer vision/image recognition are opening up a myriad of exciting applications for the insurance industry.  McKinsey estimates that advanced AI technologies could reach $1.1 trillion in annual value for the global insurance industry.¹

Applications for AI are found throughout the insurance sector, reshaping underwriting and risk assessment, claims processing, customer service and personalization, and fraud detection – marking a new era of innovation.

Underwriting and Risk Assessment

Underwriting has traditionally been a labor-intensive process, relying on historical data and manual analysis. But AI algorithms can analyze vast amounts of data at unprecedented speeds, incorporating real-time information from diverse sources such as social media, weather forecasts, and IoT devices. This not only enhances the accuracy of risk assessment, but also allows insurers to tailor policies more precisely to individual needs.

Claims Processing

Due to its complexity, criticality, and potential for disputes and litigation, claims processing has always been a challenging area for both insurers and policyholders. AI is revolutionizing this area by automating many aspects of the claims process. Machine learning algorithms can analyze claims data to detect anomalies and predict outcomes based on historical patterns, streamlining the process and reducing the time taken to settle claims, while also mitigating risk for legal action and lessening overall severity.

AI-powered chatbots and virtual assistants are capable of handling initial claim inquiries, guiding policyholders through the First Notice of Loss (FNOL) process, and even performing preliminary assessments of damage using image recognition technology. These advancements not only speed up the claims process but also improve the accuracy and fairness of settlements, enhancing customer satisfaction

Customer Service and Personalization

Policyholders want interactions that are relevant to them. The demand for highly personalized service has caused insurers to develop more data-driven and customer-centric approaches that focus on optimizing the customer experience. AI-driven customer insights are enabling insurers to better understand individual customer preferences and behaviors, allowing them to offer tailored products and engagement strategies.

Natural language processing (NLP) technologies are being employed to enhance customer interactions. More sophisticated AI chatbots are able to understand and respond to complex queries in real time, providing 24/7 support, addressing common issues without the need for human intervention, and freeing up agents to handle more complex cases.

Fraud Detection

According to the Coalition Against Insurance Fraud (CAIF), insurance fraud in the U.S. costs the industry an estimated $308.6 billion annually.² AI is playing a crucial role in combating fraud by identifying suspicious patterns and behaviors. ML models can analyze large datasets to detect anomalies, flagging potentially fraudulent claims for further investigation.

Traditional AI in Action: Impressive Insurer Examples

UNUM

Winner of Celent’s 2024 Model Insurer Award for Innovation Execution, employee benefit provider UNUM significantly improved customer retention through the power of predictive AI/ML. A cross-functional team spanning 10 business areas worked together to create, test, and implement AI models to better understand challenges within the customer journey that led to higher customer attrition within their small business segment. Using a data-driven approach, they identified clients with a higher probability of leaving and then proactively engaged them, offering timely solutions to address each client’s needs. They not only reduced attrition by 5%, but also improved employee satisfaction.³

CURE

Innovation is nothing new for not-for-profit auto insurance provider Citizens United Reciprocal Exchange (CURE). In an Ethical AI in Insurance Consortium (EAIC) webinar, CURE Chief Information Officer, Douglas Benalan, noted the insurer’s success with their AI model, citing a 60% to 70% productivity increase for their claims management team. He discussed CURE’s decision to implement an AI model that informs the claims team and enables business decisions over one that allows AI to make decisions that directly impact end customers. Benalan stated: “Before we run, we want to make some baby steps to build the AI framework and the necessary ecosystems so that our team can learn and get mature before driving toward the complex solutions.”⁴

Challenges, Risks, and Regulation

AI advancements do come with their challenges and risks. The National Association of Insurance Commissioners (NAIC) took an important initial step toward adapting regulatory frameworks to respond to the continually evolving AI landscape. In December 2023, the members adopted the ‘Model Bulletin on the Use of Artificial Intelligence Systems by Insurers’ to provide a foundation to protect consumers and promote fairness. The bulletin addresses key areas of AI accountability: responsible governance, risk management policies, and procedures to ensure fair outcomes for consumers.⁵

Additionally, 17 states have proposed AI-focused legislation, with many of them enacting bills with special emphasis on specific areas that include: interdisciplinary collaboration, protection from unsafe or ineffective systems, transparency, and protection from discrimination, accountability, and abusive data practices.⁶

Ethical Challenges and the Journey Ahead

Working to further drive accountability, the EAIC promotes ethical adoption of AI in the industry. Currently at 17 members, the EAIC brings together insurers, insurtechs and other stakeholders to establish industry-wide standards, foster transparency, and ensure fair and accountable use of AI technologies. Concentrating on critical challenges and opportunities arising from AI, the EAIC is working to shape a future in which artificial intelligence drives positive outcomes.  

To gain insight into the current state of AI in insurance, as well as learn more about insurers’ priorities and challenges, the EAIC surveyed 250 property and casualty insurance professionals involved in actuarial, underwriting, claims, and data science functions. The 2024 EAIC survey demonstrated that despite challenges related to cost, data quality, and bias, 80% of insurers are either already using AI or planning to do so within the next year.⁷ What’s more, 68% of insurers already using AI for business decisioning are satisfied with the return on investment.⁸

CURE Insurance’s Benalan, also an EAIC member, explains the apparent dichotomy of AI: “The implementation of AI in our organization has transformed the way we approach claims. We’re already observing substantial gains in operational efficiency and accuracy. However, the journey is not without its ethical challenges, making the need for industry-wide collaboration and proper frameworks paramount.”⁷  

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Sources:

1. McKinsey – https://www.mckinsey.com/~/media/mckinsey/industries/financial%20services/our%20insights/insurer%20of%20the%20future%20are%20asian%20insurers%20keeping%20up%20with%20ai%20advances/insurer-of-the-future-are-asian-insurers-keeping-up-with-ai-advances.pdf
2. Forbes – https://www.forbes.com/advisor/insurance/fraud-statistics/#Sources
3. Celent – https://www.celent.com/insights/867361813
4. Carrier Management – https://www.carriermanagement.com/features/2024/03/23/260365.htm
5. NAIC – https://content.naic.org/article/naic-members-approve-model-bulletin-use-ai-insurers
6. Insurance Journal – https://www.insurancejournal.com/news/national/2024/05/22/775285.htm
7. EAIC – https://ethicalinsuranceai.org/2024/03/12/press-release-2024-survey-reveals-crucial-ethical-ai-adoption-challenges-in-insurance-industry/ 
8. Carrier Management – https://www.carriermanagement.com/features/2024/03/23/260365.htm