When Generative Artificial Intelligence (Gen AI) went mainstream in 2023, with tools such as ChatGPT by OpenAI leading the way, it sparked a fair amount of bothpanic and excitement about how it would transform business operations across different industries. This period represented a tipping point as businesses and individuals figured out what it was, how it could be used, and what its implications – both positive and negative – would be.

By 2024, however, organizations appear to have gained clarity and begun to use generative AI to improve their business operations, as a McKinsey Global Survey from March 2024 shows that 65% of organizations are now using generative AI on a regular basis – almost double the percentage just ten months earlier.

In this blog, we discuss the current state of Gen AI, key areas of deployment, challenges, and trends, with a focus on how organizations, particularly in the insurance sector, can integrate AI into their operations for business success.

The Current State of Generative AI in the Insurance Sector

The insurance sector has traditionally been risk-averse and slow to embrace change, much like other financial services institutions. However, as a highly data-intensive industry with multitude of processes that heavily rely on data – but have historically been done manually – Generative AI has opened up a wide range of opportunities for insurance companies to streamline their operations.

No wonder the industry is on an upward growth trajectory. Globally, research shows that generative AI in the insurance industry is expected to exceed USD 8,099.97 million by 2032, up from USD 462.11 million in 2022, growing at a CAGR of 33.11% over the decade.

However, further research shows that insurers are increasingly being strategic in their deployment of generative AI. A report published by Ernst and Young (EY) in May 2024 highlights that 69% of insurers are specifically investing in use cases that improve a key area of their value chain. In addition, 83% are focusing more on use cases that provide quick wins – or a combination of short-term and long-term wins – rather than long-term benefits.

This calculated approach allows them to progressively demonstrate tangible results while effectively mitigating risk, building confidence, and maintaining stakeholder buy-in. As a result, the majority of them (65%) of them are expiring revenue growth of more than 10%, accompanied by cost savings, increased productivity, and improved customer efficiency.

Key Areas for Deployment of Gen AI in Insurance

As just highlighted, insurance companies are being highly targeted with their investments in Gen AI. Some of the high-value segments that are attracting attention include:

  • Underwriting For Risk Assessment: Generative AI uses advanced predictive analytics to assess risk and determine policy terms for customers. Insurers are able to evaluate vast amounts of data such as historical claims, weather patterns, traffic data, market trends, etc., to create fairer pricing models for everyone including previously uninsurable demographics.
  • Personalized Policies: Using Gen AI, insurers are able to process information such as lifestyle choices, health records and previous insurance history to tailor policies to better meet the needs of each individual customers. They’re also able to offer more personalized product recommendations to customers.
  • Automation of Claims Processing: The ability to automate tasks involved in claims processing tasks such as analyzing claim forms, verifying details against policy terms, and assessing damage or loss using image recognition and natural language processing. Using Gen AI in this process speeds up claims processing and reduces operational costs as well as human error, resulting in faster settlements and better customer experience.
  • Proactive Risk Management: Achievedby continuously monitoring data and identifying emerging risks before they can materialize. For example, AI systems can analyze real-time data from various sources, such as IoT sensors and social media, to detect potential hazards or trends that could impact insurance risks. This allows insurers to take preventative measures, adjust policies, and provide timely advice to policyholders.


Obstacles in the Adoption of Generative AI

Similar to other industries, the adoption of generative AI in insurance is being hampered by a number of challenges, such as:

  • Risk of Inaccuracy and Bias: According to McKinsey, inaccuracy in AI output is the most concerning issue in the adoption of Gen AI, along with data privacy, bias, and Intellectual Property (IP) infringement. A PwC survey reveals that 74% of respondents are concerned about the potential for generative AI to increase misinformation and introduce bias, which could undermine trust and accuracy in insurance operations.
  • Governance and Trust Issues: Proper data governance is essential for institutions to gain customer trust, yet it remains a challenge in most regions. For instance, a study of Irish businesses shows that only 7% have established proper AI governance structures.
  • Skill Gaps: While businesses are positive about adopting Gen AI, a study by Boston Consulting Group reveals that only 14% of the employees are being trained, out of the 86% who actually need to effectively integrate AI into their operations.


Trends and Emerging Practices

  • Growth of Decentralized Insurance – Gen AI is expected to enhance blockchain’s capabilities in the development of peer-to-peer insurance models. It’s contribution will be useful in segments such as risk assessment, claims processing, and execution of the smart contracts.
  • Creation of Hyper-customized Insurance Products – The technology has been a game changer in the creation of hyper-personalized products in other industries, and this is expected to be projected in the insurance industry with features such as real-time premium adjustments, and daily adjustment of health insurance premiums based on the customer’s daily activities.
  • Deeper Adoption in Sales and Marketing – AI-driven insights allow insurers to more effectively segment their customer base, develop targeted marketing campaigns, and optimize sales strategies. Generative AI can also generate personalized marketing content, such as emails and social media posts, that resonate with specific audiences.
  • Advancement in Risk Modelling – AI allows insurers to create highly realistic simulations that can predict and assess the impact of various risks, such as natural disasters and cyber-attacks. This will enable insurers to better understand potential threats and develop more accurate risk assessments, leading to more effective risk management strategies.
  • From human to AI Claim Adjusters – AI has the potential to take over many of the tasks currently handled by human claim adjusters, such as assessing damage, reviewing claim forms, and determining policy coverage – much more quickly, efficiently and cost-effectively. While human intellect is definitely needed for more complex situations, AI processes can significantly reduce the manual processes currently involved.


Looking Ahead – The Way Forward for Insurance Companies

As can be seen, generative AI has already had a significant impact on the insurance industry, and this is only set to increase in the future – and for good reason. It’s set to deliver delightful experiences for customers, streamline internal processes, reduce operating costs and increase revenue for insurance companies, among other benefits. As such, it’s a wake-up call for decision makers to rise the challenge and take proactive steps to integrate Gen AI into their business processes in order to stay ahead of the market and fully realize the range of benefits.