The commercial insurance marketplace is a unique ecosystem where brokers, commercial insurers, specialty insurers, and reinsurers not only coexist but actively shape industry dynamics through their interdependencies.

In 2024, the commercial insurance market was valued at approximately $776.6 billion and is projected to grow to $845.3 billion in 2025, with a CAGR of 8.8%¹. Reinsurers provide critical stability to both primary and specialty carriers, offering treaty reinsurance for broad portfolio risk mitigation and facultative reinsurance for tailored, high-risk exposures such as cyber or marine insurance. Brokers enable clients to navigate complex risk landscapes by integrating standard and specialty solutions, driving resilience and adaptability in response to global risks.

This synergy between macro-level market forces and micro-level client customization underpins the industry’s adaptability. Stakeholders – reinsurers supplying capital, commercial & specialty insurers mitigating risk, and brokers tailoring solutions – create a balanced ecosystem capable of addressing both common and differentiated challenges. This collaboration fosters scalability, efficiency, and value creation while ensuring the market’s resilience amidst evolving threats.

In today's competitive landscape, success hinges on the ability to recognize and leverage these synergies while adeptly accommodating the variances among industry segments. This balanced approach enables organizations to accelerate ROI and optimize financial outcomes, driving significant improvements in efficiency, innovation, and profitability.

This article explores the strategic considerations that facilitate scalable value creation and how tailored approaches to distinct segment needs have allowed organizations to successfully achieve their financial goals.

Leading insurers are embracing a multi-faceted transformation to remain competitive in an increasingly complex market. Success for 2025 in commercial insurance across Brokers, Commercial Underwriters, Specialty and Reinsurance is centred around common themes. These themes address the industry's pressing need for operational efficiency, enhanced customer experiences, and resilience against evolving risks. They also enable firms to stay competitive amidst rapid technological advancements and shifting market demands:

1. Modernizing legacy platforms by adopting scalable, cloud-based solutions to enhance operational efficiency, enable real-time data processing, straight-through processing, and improve customer experiences.
2. Investment in data analytics and AI, evidenced by a 220% boost in spending during the first three quarters of 2024, aimed at optimizing workflows, delivering personalized solutions, and drive greater profitability².
3. Attracting and retaining diverse talent pools is another priority, companies must focus on recruiting experts in AI, risk management, and climate analysis, while fostering a culture that supports flexibility, upskilling, and innovation.
4. Strategic adaptability underpins industry success. Firms must rapidly and dynamically respond to evolving risks, regulatory changes, and market demands in a fast-paced market by scaling proven strategies and embracing innovation.

The four themes are tied together by a growing trend of end-to-end digitization across the entire value chain, from insurer to broker to reinsurer, as it is still largely manual. This involves overcoming challenges such as integrating legacy systems, ensuring data interoperability, and managing substantial upfront costs. However, the end benefits are truly transformative: streamlined operations, enhanced data accuracy, and faster decision-making.

Fully digital workflows can enable brokers to provide real-time quotes and reinsurers to adjust risk models dynamically, fostering efficiency and innovation across the industry. For example:

• Lloyd's of London’s Blueprint Two is a comprehensive strategy aimed at digitizing the insurance marketplace to enhance efficiency and reduce costs³. The initiative focuses on creating a seamless digital ecosystem that standardizes transactions across brokers, insurers, and reinsurers. Key components include the development of a digital platform to streamline placement, accounting, and claims processes, thereby reducing manual interventions and errors. The implementation is planned in phases, with full digital adoption expected in 2025⁴.
• Another notable example is APRIL Group, the French insurance services provider⁵. After being taken private by CVC in 2019, APRIL embarked on an ambitious plan, named 'Spring,' to become a digital, omnichannel, and agile operator by 2023⁶. This transformation involved developing digital direct sales capabilities and enhancing the digital customer experience, positioning APRIL as a leader in the digital insurance ecosystem. After completing the transition in 2022, APRIL entered a long-term strategic partnership with KKR to support this new stage of growth and accelerate APRIL's development and digitalization, both in France and internationally⁷.
• Finally, WTW (Willis Towers Watson) offers 'AffinityConnect', a Platform-as-a-Service designed to accelerate the deployment of digitally powered insurance products⁸. This platform enables insurers to build and integrate solutions faster, reduce costs and complexity, and scale across various distribution channels. AffinityConnect provides consumable assets that support a connected insurance approach, facilitating seamless integration into business operations and enhancing the digital capabilities of insurers.
  
  Digitization across the value chain could connect the four themes meaningfully and allow commonalities to be leveraged. Further, various strategic imperatives remain to be tailored to each segment's unique challenges and opportunities.

Brokers are focused on growth initiatives to maintain high margins. By pursuing strategic M&A activities, leveraging advanced data analytics, building digital platforms, and implementing effective talent management strategies, top brokers are not only expanding their market presence but also enhancing operational efficiency. These growth-focused initiatives enable brokers to maintain high margins, up 9.2% on average in the UK and EBITDA margins at US brokers exceeding 23% for the first time, outperforming competitors and setting them apart from commercial lines businesses that concentrate on platform modernization, risk mitigation, adaptability, and managing technical debt.^{9, 10}

Consolidation

The intense M&A activity, while offering opportunities for expansion, leads to integration complexities that can strain resources and impact service quality. In the UK, for example, the M&A market is experiencing a supply crunch with significant M&A activity leading to a reduction of approximately 31.8% in the number of brokers since 2007¹¹. Despite high asset valuations, the UK remains a focal point for inbound US investment, serving as a gateway for international expansion due to its market maturity and robust infrastructure. However, as consolidation in the UK reaches saturation, attention is shifting to less mature markets with higher growth potential, such as mainland Europe – specifically Ireland, Germany, and Spain – and emerging hubs in Asia, including Singapore. This geographic diversification, while promising, introduces additional challenges, such as navigating diverse regulatory frameworks, overcoming cultural and operational differences, and addressing complex technology integration and reporting requirements.

Private equity firms favor the high-profit potential of acquiring and monitoring multiple brokers, yet they are increasingly facing difficulties in managing the disparate technical debt of acquired entities. Integrating varied IT systems and ensuring consistent reporting standards across a growing portfolio of diverse firms can lead to operational inefficiencies and hinder the realization of projected synergies. Consequently, while international acquisitions present new growth avenues, they also demand robust technology integration strategies and enhanced reporting frameworks to mitigate the risks associated with technical fragmentation and to ensure seamless operational cohesion across the consolidated entity.

Data & Technology

Another key focus area is data utilization which has become a critical profit centre for insurance brokers, enabling them to provide comprehensive risk advisory and loss control analytics services that extend beyond traditional insurance offerings. By harnessing vast amounts of client information through advanced data analytics, brokers can develop personalized solutions that not only enhance their insurance products but also directly contribute to the bottom line, evidenced by Aon’s Q3 24 17% increase in revenue growth in its commercial risk solutions unit driven in large part to its advanced data analytics capabilities¹². This strategic use of data allows brokers to offer tailored risk assessments and proactive risk management advice that meet the sophisticated needs of today’s market.

AI and Machine Learning are automating routine tasks, enabling brokers to gain a deeper understanding of client risks and identify the appetite of various insurance carriers. By leveraging data-driven insights, brokers can effectively negotiate the right coverage and premiums, thereby enhancing client satisfaction and fostering long-term relationships.

Cloud computing offers scalable infrastructure that streamlines M&A integration processes and supports global operations. It enables brokers to unify different systems quickly, reducing integration time and costs. Additionally, GenAI is enhancing customer interactions through AI chatbots – which are projected to manage 75% of customer interactions in 2025 – and automating policy documentation, leading to faster response times and improved client satisfaction¹³.

Real-World Applications and Case Studies

Several brokers have successfully implemented these technologies to drive growth and improve margins. For example:

• Howden Group utilized data analytics to tailor risk solutions, reportedly boosting client retention.¹⁴  By investing in their analytics arm, HX Analytics, Howden was able to offer more personalized services and enter new markets effectively¹⁵.
• Marsh McLennan implemented AI-driven integration processes following M&A activities, one example is their internal “LenAI”, reducing operational costs and enhancing internal operations and client services. ^{16, 17} Another is the GC AdvantagePoint, an advanced catastrophe analytics platform designed to enhance underwriting performance and catastrophe management workflows ¹⁸. The platform integrates point-of-sale decision-making with strategic portfolio planning and performance management, providing a comprehensive view of portfolio profitability components.
• Similarly, Aon has acquired the technology assets and IP of Humn.ai, an AI-powered platform focused on commercial fleet management. ^{19, 20} This acquisition is part of Aon's strategy to enhance its commercial fleet proposition and expand its technological capabilities and positions Aon as a leader in fleet risk management analytics, offering cutting-edge AI tools to address the increasing complexity of mobility solutions.

Global commercial insurers are contending with fragmented systems, which can be exacerbated globally, while also facing a fundamental shift in customer expectations and market dynamics. Clients now demand deeper insights, personalized products, and near-instantaneous support, reflecting the convenience they experience elsewhere.

At the same time, regional and private equity-backed insurers, along with Insurtech firms, are more agile, often operating without the technical debt that burdens traditional carriers, unless their own rapid growth through acquisitions creates similar integration issues.

Complicating matters further, risk remains poorly understood, and escalating claim costs erode profit margins. This environment amplifies the pressure on global commercial insurers to improve both efficiency and adaptability.

Consumer Expectations & Incrementalism

Insurers must leverage modern policy, claims and data-platforms, embrace data-driven insights, and pursue incremental technology upgrades rather than full-scale overhauls. These adaptive digital capabilities allow them to meet evolving customer appetites, remain competitive against nimble entrants, and navigate a market defined by increasing claim severity and regulatory demands.

This shift is particularly pronounced in the small and medium-sized enterprise segment, where stakeholders now expect insurance handling to be as immediate and seamless as other online transactions. The need for rapid automation and responsiveness presents a significant challenge for insurers burdened with extensive technical debt.

While large-scale platform implementations are expensive and disruptive, a more pragmatic approach focuses on adaptive planning. Many commercial insurers invested heavily in “one-size-fits-all” platforms like Guidewire 5-7 years ago, which now struggle to meet modern demands for agility, advanced analytics, and real-time insights. Retrofitting these systems, which require constant maintenance, adds to operational costs – c.70% of IT budgets – and complexity²¹. By selectively upgrading components of their technology infrastructure, replacing parts of the “engine” rather than the entire system, insurers can enhance efficiency, improve responsiveness, and deliver tailored experiences.

This incremental strategy keeps costs and complexity under control, allowing commercial insurers to meet heightened customer expectations without the prohibitive investments and delays often associated with complete technology transformations.

Data analytics allows for real-time risk assessment and the development of personalized policy offerings, meeting the growing demand for tailored insurance products. GenAI further streamlines customer service and internal knowledge sharing, improving responsiveness and reducing administrative burdens.

Cloud & Security

Cloud computing and modern platforms enable insurers to integrate global systems, reduce operational silos, and provide scalability. By moving to cloud-based solutions, insurers can unify disparate systems and facilitate seamless data sharing across the organization.

Moreover, while modern platforms incorporate advanced security and privacy measures to ensure data protection and compliance with diverse international regulatory requirements (GDPR in the EU, CCPA in the US, and LGPD in Brazil), this is only part of the story. ^22,23,24 Even with these systems, there is a growing need for industry-driven, highly focused security and privacy strategies. The role of security and privacy mitigation has evolved into a vital, hybrid function – one that must not only leverage modern capabilities but also identify and address additional requirements tailored to a carrier’s unique needs, such as geography, product offerings, and lines of business.

Real-World Applications and Case Studies

Several commercial insurers have successfully implemented these technologies.

• Sompo International centralized its platforms, improving efficiency and enabling digital innovation across multiple regions. By standardizing its technology infrastructure, Sompo reduced system redundancies and facilitated better collaboration among its global teams. Sompo Holdings, the parent company of Sompo International, achieved a record-high adjusted consolidated profit of ¥291.0 billion (c. $1.9 billion) in FY2023, with an adjusted consolidated ROE of 9.2%. ²⁵ This growth is attributed to digital transformation efforts, including platform centralization and the utilization of advanced technologies.
• Zurich Insurance Group partnered with AWS to modernize its enterprise IT infrastructure and accelerate digital transformation to unify fragmented systems. This move not only decreased operational expenses, expected to save approximately $30 million annually, but also enhanced Zurich's ability to respond quickly to market changes and regulatory requirements ²⁶
• Allianz Partners utilized AI to automate its claims process, Universal Claims Portal, aimed at simplifying the travel insurance claims process for customers²⁷. The insurer is replacing Robotic Process Automation (RPA) technology with advanced AI systems to accelerate claim adjudication by auto-paying eligible claims, ensuring quicker payouts²⁸. This improvement will drive faster claims settlements and increased customer satisfaction.

Specialty and Excess & Surplus (E&S) lines, spanning niche products and programs as well as higher-risk exposures, are experiencing increased demand in a rapidly evolving risk landscape. While many traditional insurers maintain specialty divisions to address these opportunities, the cost and complexity of underwriting, along with the substantial investment in talent and systems, mean that not all carriers can afford to fully commit.

Niche carriers, Lloyd’s syndicates, and high-specialty insurers, on the other hand, are achieving profitable growth, evidenced by the specialty markets rapidly expanding growth from $89.87 billion in 2023 to $99.26 billion in 2024, at a CAGR of 10.5%.²⁹  The E&S market has also seen substantial growth, with homeowner premiums expected to exceed $3 billion in 2024 attributed to the flexibility of E&S carriers in underwriting complex and high-risk exposures. ³⁰ Key players are leveraging advanced analytics, AI-driven risk modeling, and forging strategic relationships with wholesale brokers and risk aggregators to expand product lines and increase market share. They have mastered the art of doing more with less, carefully balancing cost and agility to stay competitive and profitable.

Data-driven Decision Making

These carriers operate in an environment where underwriting complexity and regulatory variance is a given, and price sensitivity must be continually managed to avoid triggering competition from commercial lines players. They know that success hinges on precise, data-driven decision-making.

While integrating diverse data sources such as social media, third-party aggregators, and Internet of Things (IoT) devices and networks can enable richer insights into insured assets and exposures, specialty carriers often lack the scale or leverage to mandate direct IoT deployment within client organizations. Instead, they must aggregate and analyze data from multiple external sources, including IoT, to enhance their understanding of risks. For example, IoT devices can provide real-time data on asset conditions, while social media and third-party aggregators offer contextual insights into client behavior and market trends. This multifaceted approach allows specialty carriers to make informed decisions despite their scale limitations.

This approach is data-intensive and can be expensive, reinforcing the necessity for advanced analytics and cost-efficient technology ecosystems. While GenAI offers significant advantages in rapidly synthesizing diverse inputs and delivering immediate insights, it does not necessarily reduce the cost of acquiring third-party data, which must often be purchased for use. Additionally, the large models required to "teach" GenAI can be resource-intensive, making it more of a tool for efficiency and quick interpretation rather than direct cost reduction.

In contrast, advanced machine learning (ML) capabilities, particularly scenario-based and interpretative algorithms, excel at doing more with less. These models require fewer data inputs while remaining highly prescriptive in their usage, offering a cost-effective alternative. By cutting down on data needs and focusing on specific, actionable outcomes, ML can reduce expenses and provide insurers with targeted insights for loss prevention and portfolio optimization. Together, these technologies complement each other, balancing immediate efficiency with cost-conscious, high-value analytics.

We are seeing tangible results from these strategies in the field. AI-driven underwriting enhancements have refined cyber risk pricing, considering cyber the fastest-growing subsector with premiums expected to hit $23 billion by 2025. ³¹ Meanwhile aggregated IoT and third-party data streams have improved the predictability and prevention of losses in areas such as marine and aviation.

For industry leaders, the question is no longer whether to adopt these technologies, but rather how to do so optimally and cost-effectively. Aligning technology investments with underwriting strategy and ensuring systems, data, and talent acquisition efforts produce sustainable returns are paramount. Whether expanding a specialty division within a large carrier or scaling up as a niche player, the goal is consistent: sustain profitability, remain nimble, and respond swiftly as new entrants consider encroaching on previously misunderstood niches.

Real-World Applications and Case Studies

Several specialty insurers have successfully implemented these technologies to enhance their operations.

• At Lloyd's of London, there has been a concerted effort to embrace innovation through the Lloyd's Lab accelerator program. ³² This initiative encourages the development of Insurtech solutions to improve efficiency and reduce administrative costs. While specific figures are not publicly disclosed, these efforts signify a commitment to leveraging technology for operational improvements.
• Beazley, for instance, utilized AI-powered underwriting tools to improve risk assessment in cyber insurance partnering with Cytora and launched Beazley Quantum’, a new cyber consortium in 2024. ^33,34,35 By analyzing vast amounts of data on cyber threats, they enhanced their ability to price policies accurately and respond to the rapidly evolving cyber risk landscape.
• Convex Insurance successfully leveraged Snowflake and Dataiku to build a robust data platform, transforming how the company accesses and utilizes data. ^36,37 By centralizing all data workflows within Snowflake, Convex eliminated silos and enabled seamless collaboration across business units. Dataiku further enhanced this capability by automating critical processes like actuarial reserving, ensuring accurate and reliable insights. Their approach, which embedded skilled data engineers into business teams, bridged technical and operational expertise, creating user-friendly yet powerful data pipelines. This combination of technology and collaboration allowed Convex to move beyond manual spreadsheets, fostering innovation and enabling data-driven decision-making at scale, surpassing $3 billion in gross written premium (GWP) in 2023, less than four years since the collaboration. ³⁸
• AXA XL is proactively addressing security and privacy (S&P) challenges associated with GenAI. ³⁹ In response to the European Union's AI Act, which imposes stringent requirements on AI system development and deployment, AXA XL expanded its cyber insurance coverage to encompass risks linked to GenAI. The solution is tailored for businesses navigating regulatory uncertainty and increased exposure to cyber risks from Gen AI adoption and available in key markets (US, Canada, UK, Europe, and Asia), reflecting AXA XL’s global strategy to address evolving cybersecurity challenges.

Reinsurers face mounting pressure to refine their risk models and optimize capital allocation as climate change intensifies and geopolitical tensions escalate. Traditionally, reinsurance has relied heavily on reactive and aggregative models to pool and manage risks from primary insurers. However, leading reinsurers are now transitioning towards a more proactive approach, utilizing advanced analytics and AI to strategically select and manage the risks they undertake.

This shift enhances their ability to navigate the complexities of today’s dynamic risk landscape with greater precision and foresight, managing "horse-trading" of risk across books to maximize returns.

Climate Change

The surge in catastrophic events such as hurricanes, wildfires, and floods, largely driven by climate change, has rendered traditional reactive risk models less effective. These models, which primarily focus on aggregating risks, struggle to accurately predict the increasing scale and impact of such disasters, leading to potential under-pricing and insufficient capital reserves. In 2024, natural catastrophes resulted in approximately $320 billion in economic losses worldwide, marking a significant increase from previous years. Notably, only about $140 billion of these losses were insured, underscoring a substantial protection gap. ⁴⁰

Additionally, capital optimization remains a critical concern. Reinsurers must adeptly balance maintaining adequate reserves to cover potential losses with the need to achieve satisfactory returns on equity, necessitating sophisticated capital management strategies.

Moreover, regulatory scrutiny has intensified globally, with regulatory bodies heightening their oversight on capital adequacy and risk management practices within the reinsurance sector. Compliance with frameworks such as Solvency II and accounting standards like IFRS 17 demands greater transparency and robust risk assessment methodologies. ^41,42

Advanced Analytics

Reinsurers handle vast and complex datasets from diverse sources, including historical loss data, real-time environmental information, and socio-economic indicators. The ability to analyze this data accurately is essential for identifying patterns, forecasting trends, and making informed underwriting decisions.

Machine learning is instrumental in identifying patterns within large datasets, improving forecasting capabilities. These algorithms can detect anomalies and emerging risks that traditional methods might overlook, enabling reinsurers to proactively manage their portfolios. For example, machine learning models can analyze weather patterns, seismic activity, and other risk factors to predict catastrophic events more accurately.

GenAI offers advanced capabilities in scenario planning and stress testing. By simulating a range of potential future events, GenAI helps reinsurers assess the impact on their portfolios and adjust their strategies accordingly.

Complementing these capabilities, synthetic data enables the creation of realistic yet anonymized datasets for training machine learning models, facilitating robust scenario modeling while maintaining data privacy. Additionally, specialist large language models (LLMs), fine-tuned with industry-specific data, empower reinsurers to analyze unstructured information, such as claims documents and policy records, with far greater accuracy.

Real-World Applications and Case Studies

Several leading reinsurers have successfully implemented these technologies, resulting in tangible benefits. For instance:

• Munich Re has invested in AI and machine learning to enhance its risk modelling and has integrated a new generative AI-powered CoPilot feature into its REALYTIX ZERO underwriting platform, streamlining the automation of underwriting processes for the development and modification of insurance and reinsurance products. ⁴³ By integrating advanced analytics into their catastrophe models, they have improved their ability to assess risks associated with natural disasters and climate change.
• Swiss Re has utilized machine learning algorithms to analyze large datasets for better risk prediction and portfolio management, its Impact+ suite of analytics products. ^44,45 This has enabled them to identify correlations and dependencies across different lines of business, leading to a more balanced and resilient portfolio.
• Another example is SCOR SE, which has employed machine learning for enhanced risk aggregation and portfolio optimization. ⁴⁶ By analyzing extensive datasets across various business lines, SCOR SE has identified patterns that facilitate a more proactive approach to risk selection, thereby enhancing overall portfolio performance and financial stability. The launch in collaboration with Insmart JSC, has introduced an AI-powered predictive engine tailored for the Vietnamese insurance market. The engine addresses dynamic insurance demands in Southeast Asia, offering a competitive advantage in a region with diverse and evolving insurance needs and can adapt to specific insurer needs or operate as a universal solution, leveraging SCOR’s global expertise. For SCOR SE this move reinforces its leadership in digital insurance solutions and positions the company as a pioneer in AI-driven innovations for the reinsurance sector.

¹ https://www.thebusinessresearchcompany.com/report/commercial-insurance-global-market-report
² https://www.lifeinsuranceinternational.com/news/insurance-data-analytics-investment-rebounds-as-firms-pursue-ai-personalisation/
³ https://www.lloyds.com/about-lloyds/blueprint-two
⁴ https://www.lloyds.com/news-and-insights/news/an-update-on-delivery-of-blueprint-two-digital-services
⁵ https://www.april.com/en/
⁶ https://www.april.com/en/press-releases/april-group-launches-its-spring-2027-strategic-plan-with-the-ambition-of-becoming-a-european-leader-with-a-global-stature-in-the-mass-market/
⁷ https://www.april.com/en/press-releases/april-signs-strategic-partnership-with-kkr-for-its-next-phase-of-growth/
⁸ https://www.wtwco.com/en-gb/solutions/products/affinityconnect-platform-as-a-service
⁹ https://www.ibisworld.com/united-kingdom/industry/insurance-agents-brokers/3790/
¹⁰ https://www.insurancejournal.com/news/national/2024/02/26/762119.htm
¹¹ https://www.alvarezandmarsal.com/sites/default/files/2023-10/AM%20Global%20Insurance%20Brokerage%20Report%202023.pdf
¹² https://www.reuters.com/business/finance/insurance-broker-aon-beats-profit-estimates-strong-business-growth-2024-10-25/
¹³ https://www.insurancethoughtleadership.com/ai-machine-learning/december-itl-focus-generative-ai
¹⁴ https://www.howdengroup.com/uk-en?geoLocationActioned=yes
¹⁵ https://www.howdengroup.com/news-and-insights/howden-group-brings-digital-data-and-analytics-closer-to-clients
¹⁶ https://www.marshmclennan.com/
¹⁷ https://www.marshmclennan.com/news-events/2023/november/marsh-mclennan-develops-new-generative-ai-tool.html
¹⁸ https://www.guycarp.com/solutions/capabilities/strategic-advisory/catastrophe-modeling/advantage-point.html
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²⁰ https://www.prnewswire.com/news-releases/aon-acquires-ai-powered-platform-to-help-fleet-and-mobility-clients-make-better-insight-driven-decisions-302081808.html
²¹ https://blog.adacta-fintech.com/insurance-legacy-systems
²² https://gdpr-info.eu/
²³ https://oag.ca.gov/privacy/ccpa
²⁴ https://iapp.org/resources/article/brazilian-data-protection-law-lgpd-english-translation/
²⁵ https://www.sompo-hd.com/-/media/hd/en/files/doc/pdf/annualreports/2024/annualreport2024_1.pdf
²⁶ https://www.lifeinsuranceinternational.com/news/zurich-insurance-business-applications-aws/
²⁷ https://www.allianzworldwidepartners.com/usa/media-center/press-releases/Allianz-Partners-launches-new-claims-portal-and-adopts-AI-to-take-customers-experience-to-the-next-level.html
²⁸ https://www.insurtechinsights.com/allianz-partners-launches-new-ai-enhanced-claims-portal/
²⁹ https://www.thebusinessresearchcompany.com/report/specialty-insurance-global-market-report
³⁰ https://www.reuters.com/markets/us/international-domestic-insurers-push-into-catastrophe-hit-us-property-markets-2024-12-16/
³¹ https://www.cfodive.com/news/companies-lean-ai-push-cheaper-cyber-insurance-security/726519/
³² https://www.lloyds.com/news-and-insights/lloyds-lab
³³ https://www.beazley.com/en-US/fullspectrumcyber/
³⁴ https://www.intelligentinsurer.com/insurance/beazley-partners-with-ai-insurtech-cytora-to-unlock-scalable-growth-29188
³⁵ https://www.beazley.com/en-US/news-and-events/beazley-adds-to-its-cyber-capabilities-with-launch-of-beazley-quantum/
³⁶ https://convexin.com/
³⁷ https://www.dataiku.com/stories/detail/convex/ 
³⁸ https://www.techmonitor.ai/partner-content/convex-insurance-data-democratisation-at-scale-delivering-business-value
³⁹ https://insurtechdigital.com/articles/axa-xl-launches-gen-ai-cyber-cover-amid-regulatory-pressure?utm_source=chatgpt.com
⁴⁰ https://www.ft.com/content/76d1e4b6-ac70-47c0-82c0-76faca1c22e7
⁴¹ https://www.eiopa.europa.eu/index_en
⁴² https://www.ifrs.org/issued-standards/list-of-standards/ifrs-17-insurance-contracts/
⁴³ https://www.munichre.com/en/solutions/reinsurance-property-casualty/realytix-zero.html
⁴⁴ https://www.swissre.com/risk-knowledge/advancing-societal-benefits-digitalisation/machine-intelligence-in-insurance.html
⁴⁵ https://analytics.swissre.com/details/poi
⁴⁶ https://www.scor.com/en/news/scor-lh-launches-ai-based-predictive-engine-vietnam