{"id":16676,"date":"2025-01-28T10:14:45","date_gmt":"2025-01-28T07:14:45","guid":{"rendered":"https:\/\/insly.com\/?p=16676"},"modified":"2025-01-28T13:18:29","modified_gmt":"2025-01-28T10:18:29","slug":"artificial-intelligence-in-insurance-underwriting-a-revolution-is-underway","status":"publish","type":"post","link":"https:\/\/insly.com\/en\/blog\/artificial-intelligence-in-insurance-underwriting-a-revolution-is-underway\/","title":{"rendered":"Artificial intelligence in insurance underwriting \u2013 a revolution is underway"},"content":{"rendered":"
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Underwriters are at the heart of any insurance business. Their ability to assess risk accurately based on a wide variety of information is critical to product innovation, effective pricing, and ultimately whether a business can make a profit in an increasingly competitive market. Consequently, insurance companies are always striving to optimise their underwriting processes, in a bid to get ahead. In today\u2019s market, that increasingly means leveraging artificial intelligence (AI).<\/p>\n

Underwriting has already advanced enormously in recent years; as technology and modelling have developed, the process has become increasingly sophisticated and digitised, with underwriters now sifting through rafts of proprietary and third-party information. But as data sources have increased, underwriters face the challenge of how to process and analyse this \u2018big data\u2019 as accurately and quickly as possible, to keep up with customer expectations regarding service and convenience. This is where AI comes in.\u00a0<\/span><\/p>\n

AI technologies including natural language processing (NLP), large language models (LLMs), generative AI, and machine learning, promise to solve this problem, with the ability to ingest, process, and analyse huge volumes of unstructured data significantly faster and more accurately than humans. With <\/span>Hiscox recently claiming<\/span><\/a> AI has enabled it to cut underwriting times from three days to three minutes, it is no surprise <\/span>77% of insurance decision-makers<\/span><\/a> are at some stage of adopting AI in their value chain.\u00a0<\/span><\/p>\n

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How is artificial intelligence being used in insurance underwriting?\u00a0<\/b><\/p>\n

It\u2019s now more than two years since ChatGPT burst onto the scene, and since then, it has been joined by numerous competitors, including Claude, Google Gemini, and Microsoft Copilot. In the insurance sector, these and other foundation models are being harnessed to create AI-powered <\/span>underwriting systems<\/span>, <\/span><\/a>solving challenges such as manual data entry, fragmented data sources, and incomplete or unstandardised risk assessments. AI tools can even help evaluate a person’s risk profile by scanning huge amounts of structured and unstructured data sources, such as social media, <\/span>financial<\/span><\/a>, and public records, with the ability to make decisions in a matter of minutes in some cases.\u00a0<\/span><\/p>\n

London-based broker, Hiscox, <\/span>recently announced<\/span><\/a> it has collaborated with Google Cloud\u2019s Gemini large language model to assess risks, generate pricing, and draft emails to brokers, accelerating the underwriting process significantly. AIG is also <\/span>harnessing generative AI <\/span><\/a>to improve data quality in underwriting, with the Chairman and CEO Peter Zaffino claiming it has improved data collection and accuracy rates from around 75% to more than 90%<\/span>. <\/span>And QBE <\/span>recently launched<\/span><\/a> a Cyber Underwriting AI Assistant, which has driven a 65% reduction in review times, by performing initial submission reviews.\u00a0<\/span><\/p>\n

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What are the benefits of artificial intelligence in insurance underwriting?\u00a0<\/b><\/p>\n

It\u2019s no exaggeration to say AI is revolutionising underwriting efficiency. According to a <\/span>McKinsey report<\/span><\/a> of more than 50 insurance leaders, more than half of the respondents say gen AI could lead to productivity gains of 10 to 20 percent, premium growth of 1.5 to 3.0 percent, and improvement in technical results by 1.5 to 3.0.<\/span> Here are a few reasons why:<\/span><\/p>\n

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