{"id":555546,"date":"2024-02-07T12:05:07","date_gmt":"2024-02-07T11:05:07","guid":{"rendered":"https:\/\/ibsintelligence.com\/?post_type=blogs&p=555546"},"modified":"2024-02-07T12:06:55","modified_gmt":"2024-02-07T11:06:55","slug":"how-legacy-players-can-reinvent-the-insurance-value-chain","status":"publish","type":"blogs","link":"https:\/\/ibsintelligence.com\/blogs\/how-legacy-players-can-reinvent-the-insurance-value-chain\/","title":{"rendered":"How legacy players can reinvent the Insurance value chain"},"content":{"rendered":"

By Leon Gauhman, Co-Founder & CPO\/CSO, Elsewhen<\/a><\/em><\/strong><\/p>\n

Generative AI is poised to disrupt every sector \u2013 and the insurance industry is not immune.
\nThe good news is that Large Language Models (LLMs) are tailor-made for the fast-
\nChanging insurance sector.<\/p>\n

Language forms the basis of many facets of risk management, from modelling to quotes and underwriting. So, if incumbents can use LLMs to leverage their high-quality, first-party data, they will have a ready-made defence against BigTech contenders at the gate. With Amazon targeting home insurance, Apple eyeing the health insurance market, and Lemonade, Zego and Clearcover also making waves, LLMs and AI could be legacy insurers\u2019 game-changing opportunities. Here\u2019s how to embrace the shift:<\/p>\n

\"Leon
Leon Gauhman, Co-Founder & CPO\/CSO, Elsewhen<\/figcaption><\/figure>\n

Personalised product development:<\/strong> Using their rich reserves of proprietary data, incumbents like Zurich can focus on breaking down the rigidity of underwriting, which currently confines them to offering consumers commoditised, one-size-fits-all products in areas such as health, home, car, and travel insurance. Cut loose; insurers can deploy LLMs to create valuable IP at speed. This could be a personalised AI broker to deliver nuanced recommendations that fit precisely with a customer\u2019s circumstances and risk profile.<\/p>\n

Accelerated distribution:<\/strong> Once insurers have deployed LLMs to create new products, AI can also help with their rapid roll-out to market by generating product descriptions and marketing collateral at scale. Using an LLM-enabled drill down of multiple data sets around individual customers\u2019 risk profiles and requirements, insurers can use AI to generate personalised marketing campaigns across various channels and formats, including bespoke offers and incentives. At the other end of the campaign, LLMs constantly learn from data about the success or failure of new product launches and related marketing campaigns. Consequently, these services accelerate the feedback loop, adjusting and improving insurance products and campaigns in near real-time.<\/p>\n

Next-generation underwriting:<\/strong> Because LLMs can understand and process unstructured and natural language, adopting them will also speed up the automation of underwriting \u2013 prompting more accurate pricing and reduced risk. This year, for example, insurance platform Cytora introduced new capabilities to its digital risk assessment and underwriting processes, which it claims will enable customers to reduce the time to onboard new businesses. By providing risk-specific LLMs that build upon Cytora\u2019s proprietary AI, underwriters can \u2018cut through the noise and reach decisions more efficiently and effectively\u2019, said CEO Richard Hartley.<\/p>\n

Rethinking the business model:<\/strong> Perhaps the most fundamental way LLMs will impact the insurance industry value chain lies in the technology\u2019s ability to reinvent the entire operational model. Insurers\u2019
\ncurrent models follow a linear, step-by-step framework covering product development, underwriting, distribution, policy issuance, admin, accounting, and claims. Using LLMs presents an opportunity to rewrite and transform this time-consuming approach from the ground up.<\/p>\n

In this scenario, AI would draw on an insurance company\u2019s structured and unstructured data to plan and generate risk and underwriting models for product details. There would still need to be humans.
\nOversight to set parameters and decide strategically significant outputs. However, this calibre of AI-led operating model would allow insurers to create particular variants of their products tailored to
\nCustomers\u2019 circumstances with previously unimaginable scale and speed.<\/p>\n

LLMs offer a bold leap forward for legacy operators, especially since they have largely failed to take advantage of the web and mobile revolutions. There\u2019s no question that the transformation required for this AI-driven shift is significant, particularly for analogue underwriters that \u2013 like Lloyds of London \u2013 are still weaning themselves off paper-based processes. The payoff is worth it. LLMs could become a transformational tool, allowing them to reinvent the insurance value chain in a previously impossible way.<\/p>\n","protected":false},"excerpt":{"rendered":"

By Leon Gauhman, Co-Founder & CPO\/CSO, Elsewhen Generative AI is poised to disrupt every sector \u2013 and the insurance industry is not immune. The good news is that Large Language Models (LLMs) are tailor-made for the fast- Changing insurance sector. Language forms the basis of many facets of risk management, from modelling to quotes and […]<\/p>\n","protected":false},"author":35761,"featured_media":555548,"template":"","categories":[11395,19264,11386],"tags":[12663,12598,13852,20298,11891,43287,14786,43288,11757,12019],"class_list":["post-555546","blogs","type-blogs","status-publish","has-post-thumbnail","hentry","category-analytics","category-ibsi-flagship-offerings","category-insurtech","tag-ai","tag-big-data","tag-bigtechs","tag-elsewhen","tag-insurance","tag-insurance-brokers","tag-insurtech","tag-policy-insurance","tag-risk-management","tag-underwriting"],"acf":[],"yoast_head":"\nHow legacy players can reinvent the Insurance value chain - IBS Intelligence<\/title>\n<meta name=\"description\" content=\"Language forms the basis of many facets of risk management, from modelling to quotes and underwriting. 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