\n For generations, the value proposition in insurance has been defined by risk transfer: When losses occur, insurers help policyholders recover financially. That role remains essential. But, major long-term shifts across the global insurance landscape are now forcing a reimagining of customer value, profitability, and growth.\n<\/p>\n\n\n\n
Property and casualty (P&C) insurers face growing challenges, including macro-economic factors and cyber-attacks, but none is bigger than climate risk. Catastrophic events are nothing new, of course. What has changed is the scale and frequency of weather-related losses and the operational strain that follows. Swiss Re estimates global insured losses from weather\u2011related natural catastrophes have exceeded $135 billion in 2024, marking the fifth consecutive year insured losses topped $100 billion, and underscoring a structural escalation in climate\u2011related risk.1<\/sup><\/p>\n\n\n\n \n In response, many insurers are rethinking how to best deliver customer value, profitability, and growth. Mutual and cooperative insurers are under sustained pressure to balance financial strength with their purpose of providing protection in an environment marked by increasingly severe risks and closer regulatory scrutiny. It is a challenge that AI is well suited to answer, helping to expand the role of insurers from risk transfer providers to proactive risk partners.\n<\/p>\n\n\n\n A 2024 survey by the International Cooperative and Mutual Insurance Federation (ICMIF<\/a>) found that 62% of respondents were already using AI, with a further 19% planning adoption within the next year. In practice, however, most deployments were commonly concentrated in specific functional areas, such as supporting underwriting, claims processing, and customer interactions. About 67% of insurers expect AI to become more central to their operations, even as many cite data quality and talent gaps as key challenges.2<\/sup><\/p>\n\n\n\n According to a recent BCG study, only about 7% of insurers have successfully scaled initiatives, with 67% engaged in pilots, fragmented across functions. The opportunity now is to move from isolated use cases to AI embedded across end\u2011to\u2011end processes, extending to more automated, interconnected workflows and setting the stage for a shift toward risk prevention.3<\/sup><\/p>\n\n\n\n \n Prevention does not replace excellence in risk transfer. Forward-looking organizations pursue both. They modernize service and core operations across the customer engagement cycle, while investing in prediction and prevention-oriented capabilities that help reduce future risk and strengthen long-term resilience. \n<\/p>\n\n\n\n One area where AI delivers important benefits is in enabling faster, more consistent client service by helping representatives locate and validate policy information faster. At Unum Group<\/a>, for example, a new AI-powered application lets representatives search across 1.3 terabytes of policy and related documents and receive highly relevant answers in four to five seconds, with reported accuracy of up to 95%. This reduces time spent on manual lookup and frees representatives to focus on higher-value client interactions.<\/p>\n\n\n\n Likewise, NFU Mutual<\/a> uses Copilot for Sales with Microsoft Dynamics 365 to establish a centralized \u201csingle source of truth\u201d for customer data and interactions. By capturing and summarizing communications in real time, employees can quickly understand customer needs and respond with greater precision, helping to reduce response times and deliver more informed, personalized engagement.<\/p>\n\n\n\n \n AI can also streamline First Notice of Loss by ingesting call transcriptions, images, and videos, and guiding representatives to capture the right information in the first conversation, helping accelerate remediation. \n<\/p>\n\n\n\n In claims review, AI can turn static documentation into insights that inform action. Gallagher<\/a>, for example, built an internal AI platform that summarizes complex claims files in minutes rather than hours, helping adjusters move faster and apply those insights more effectively across claims and client workflows.<\/p>\n\n\n\n \n In cases of widespread impact, such as a storm that causes power outages that result in many food spoilage claims, AI can route low-complexity claims through specialized AI agents that can help validate coverage, correlate weather data, detect fraud, calculate payouts, and generate audit trails. This increases service representative capacity for higher-impact cases by addressing low-risk claims with autonomous AI.\n<\/p>\n\n\n\n \n These innovations use document processing, contextual summarization, natural language interface and workflow automation, all of which can be used to help improve other processes across core insurance capabilities, customer service, and relationship management.\n<\/p>\n\n\n\n \n The impact of prevention\u2011led approaches, whether applied to customer risk or enterprise risk, is twofold: financial resilience and stronger trust. This positions insurers as partners that mitigate, not just transfer risk for their customers.\n<\/p>\n\n\n\n \n Prevention\u2011led use cases extend well beyond field\u2011level interventions, such as property risk scoring or event\u2011readiness outreach. Increasingly, they focus on identifying and reducing risks earlier, before disruptions, security incidents, or service failures occur.\n<\/p>\n\n\n\n This shift is visible in how organizations are applying AI to support faster, more informed decisions. At Aon, which has an enterprise grade platform that can operate across solution lines, teams use AI-enabled tools to better assess and respond to risk. To enhance decision quality while maintaining strong governance, they built an Azure-based AI platform called AonGPT<\/a> that securely connects data and supports consistent, governed analysis, especially in fast-moving situations. During recent California wildfires, Aon\u2019s teams combined near real-time satellite imagery with proprietary data to generate timely insights that helped clients assess damage and plan their response.<\/p>\n\n\n\n AI also enables a shift from paying claims to helping customers reduce exposure before losses occur. Zurich Insurance Group<\/a> deployed more than 200 AI tools to interpret unstructured inputs in the form of images, reports, and emails in multiple languages, and translate them into clear, consistent risk signals for underwriters. This improves the accuracy and timeliness of risk assessments, helping customers anticipate and reduce potential exposures before losses occur, and supports better informed underwriting decisions.<\/p>\n\n\n\n \n Prevention can also take the form of making dormant risk visible early enough to act. For example, AI can analyze large volumes of historical risk engineering reports to identify patterns, such as construction materials or design features that are associated with higher structural risk. This can distinguish specific higher-risk properties for expert review\u2014in weeks rather than months in some cases\u2014letting insurers engage earlier, prioritize inspections, and reduce the likelihood of disruption. \n<\/p>\n\n\n\n \n Many prevention types depend on spotting and interpreting early signals, often from outside of core insurance systems. Using generative AI and machine learning, insurers can integrate third-party signals with internal data to help create new ways to refine risk selection, pricing, event readiness, customer outreach, and more. Sources such as external research, disclosures, regulatory filings, sensor data, and geospatial imagery can have immense impact, provided they are reliably accessible.\n<\/p>\n\n\n\n \n Initiatives from Microsoft Research and AI for Good highlight advances in third-party data that can significantly enrich the power of predictive solutions:\n<\/p>\n\n\n\n \n Earlier, more precise forecasting can inform proactive risk alerts, giving customers and commercial clients time to take preventive actions (for example, securing property or adjusting operations) and support coordination among insurers, risk engineers, brokers, and public authorities. The objective is straightforward: Improve analysis, lead <\/a>time, and decision quality to mitigate large losses.\n<\/p>\n\n\n\n \n For leaders, realizing measurable value from AI across the business, including enhancing prevention, can happen in a matter of months or quarters. Microsoft\u2019s view of industry patterns indicates that successful approaches often prioritize the following:\n<\/p>\n\n\n\n Insurers who employ this comprehensive approach and tailor AI to their unique business requirements can improve the most critical aspects of their operations. Critically, they can enhance prevention as an important part of their future growth strategies. <\/p>\n\n\n\n 1<\/sup> Swiss Re, \u201cHurricanes, severe thunderstorms and floods drive insured losses above USD 100 billion for 5th consecutive year, says Swiss Re Institute<\/a>,\u201d December 2024\n<\/p>\n\n\n\n 2<\/sup> International Cooperative and Mutual Insurance Federation, \u201cBalancing AI innovation with member-driven values at mutual and cooperative insurers<\/a>,\u201d February 26, 2025\n<\/p>\n\n\n\nInsurers and AI: early adoption and opportunity<\/strong>\n<\/h2>\n\n\n\n
How AI helps improve efficiency, service, and relationship management<\/strong>\n<\/h2>\n\n\n\n
How AI helps with prevention and protection<\/strong>\n<\/h2>\n\n\n\n
Emerging external data sources help improve risk prevention<\/strong>\n<\/h2>\n\n\n\n
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