China Expands Capital Support Across State Financial Institutions

On the insurance side, China Life Insurance Group, the country’s largest life insurer, will receive 35 billion yuan ($5.2 billion), while China Taiping Insurance Group will get 7 billion yuan. People’s Insurance Company of China said it planned to raise up to 15 billion yuan through a private placement of A-shares to the Ministry of Finance, with proceeds directed toward replenishing its capital. China Export and Credit Insurance Corp will receive 10 billion yuan from the finance ministry to boost its core capital, and China Reinsurance Group will raise 3 billion yuan.

The insurance sector has been grappling with eroding profitability driven by persistently low interest rates, and numerous small and mid-sized insurers have reported deteriorating solvency ratios. State insurers had also been directed to support the stock market with medium- and long-term funds, placing additional demands on their capital bases. This China bank capital injection programme could help bolster those institutions while positioning them to assist regulators in managing smaller, higher-risk insurance companies.

China Life said the injection represented “an important step by the country to enhance the financial sector’s ability to serve the real economy and promote the high-quality development of the financial and insurance industries,” adding it would strengthen the group’s ability to withstand risks. Taiping said the funds would bolster its insurance solvency and other key indicators.

Capital Injections Target Bank and Insurance Resilience

Three major state lenders also announced on Sunday that they will receive a combined 290 billion yuan in capital. Agricultural Bank of China said it planned to raise up to 160 billion yuan, while Industrial and Commercial Bank of China planned to raise up to 100 billion yuan. Both banks will conduct private A-share placements to the Ministry of Finance, China National Tobacco Corp and its subsidiaries. Both lenders confirmed the proceeds would be used entirely to replenish core Tier 1 capital, aiming to help sustain credit expansion as Beijing leans on China state banks to support growth.

Weak loan demand remains a persistent drag on the world’s second-largest economy and continues to erode banking profitability. The Export-Import Bank of China, one of the country’s three policy lenders, said the Ministry of Finance China will inject 30 billion yuan into the bank, enhancing its capital base.

This latest round of China bank capital injection measures represents a broad-based effort to reinforce financial resilience across both the banking and insurance sectors. By distinguishing between direct ministry injections and private A-share placements, Beijing has structured the programme to address the specific capital needs of individual institutions while maintaining a unified approach to strengthening the wider financial system. The China bank capital injection initiative signals the government’s commitment to ensuring its largest financial institutions remain adequately capitalised amid ongoing economic headwinds.

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USI Insurance Services, headquartered in Valhalla, New York, is one of the largest insurance brokerage and consulting firms in the United States. The firm generates approximately $3 billion in annual revenue and employs more than 10,500 team members across nearly 200 US offices. USI provides property and casualty, employee benefits, personal risk, program and retirement solutions, with a strong focus on the middle market insurance space.

Aon Agrees $17 Billion USI Insurance Acquisition

The USI Insurance Acquisition has been unanimously approved by the boards of both Aon and USI. Aon plans to fund the transaction with new debt raised across a range of maturities, subject to market conditions, and expects to maintain its current credit ratings. The company has indicated it does not expect to repurchase shares in the near term as it prioritises debt repayment.

Greg Case, President and CEO of Aon, said the combination of capabilities and expertise, supported by proprietary data, analytics and technology, is essential for creating better outcomes for clients facing rising complexity. He pointed to the firm’s 3×3 Plan and Aon United strategy as having strengthened the company’s position ahead of this insurance acquisition.

Following the close, USI Chairman and CEO Mike Sicard will serve as President of Aon plc and global CEO of Middle Market, reporting to Case. Sicard described the USI Insurance Acquisition as an opportunity to accelerate momentum and unite the strengths of USI, NFP and Aon to deliver enhanced capabilities and service to middle market clients.

Deal Expands Middle Market Insurance Platform

Aon has said the acquisition will strengthen its presence in the more than $40 billion US middle market segment, which represents more than one third of US commercial property and casualty direct written premium. The transaction also expands Aon’s direct access to the Excess and Surplus segment, broadening the firm’s insurance brokerage reach. Aon has highlighted the potential for the combined data platform to support richer insights and AI-enabled solutions for clients.

The USI Insurance Acquisition follows Aon’s 2024 acquisition of NFP for approximately $13.4 billion in cash and stock, which similarly targeted the middle market insurance brokerage space. Together, these transactions reflect a sustained consolidation trend in insurance M&A, with major brokerages seeking scale and broader service offerings.

KKR originally acquired USI in 2017 alongside Canadian investment firm CDPQ for $4.3 billion including debt, and subsequently invested more than $1 billion in the business, becoming its largest shareholder.

The USI Insurance Acquisition is expected to close in the fourth quarter of 2026, subject to customary closing conditions including regulatory approvals. The transaction remains subject to these conditions and has not yet been completed.

This USI Insurance Acquisition positions Aon to compete more effectively across the full spectrum of commercial insurance, employee benefits, risk management and retirement solutions in the US middle market.

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The definitive agreement has been concluded, though closing remains subject to customary conditions, including required regulatory approvals. Completion is expected in the first quarter of 2027.

Munich Re Agrees $575 Million At-Bay Deal

This cyber insurance acquisition brings together Munich Re’s global reinsurance strength and At-Bay’s technology-driven approach to cyber risk management. At-Bay primarily serves the SME market in the United States, focusing on organisations that face increasing cyber threats but often lack the resources to manage cybersecurity effectively.

At-Bay has grown into a top-10 US cyber insurer with gross written premiums totalling $278 million as at 31 December 2025, plus cyber fee service revenues of $23 million. The company currently employs approximately 280 people in the US and Israel.

Mike Kerner, Member of the Board of Management at Munich Re, said: “At-Bay’s market position and unique capabilities make it a perfect addition to our specialty insurance portfolio and an essential component of our future cyber offering. We expect the business to evolve into a strong earnings growth driver over time.”

At-Bay Adds Cyber Insurance Technology

Founded in 2017, At-Bay combines cyber insurance and cybersecurity into an integrated risk solution. Through its unified security platform, At-Bay continuously identifies, monitors and reduces insured cyber risk across the full policy lifecycle while driving data insights to improve underwriting practices.

At-Bay provides insurance protection and security solutions to close to 40,000 businesses in the US, safeguarding up to $800 billion in collective business revenue. Coverage includes cyber, Technology Errors and Omissions, and Miscellaneous Professional Liability. At-Bay also offers proprietary security solutions including its Stance Managed Detection and Response service.

Rotem Iram, CEO and co-founder of At-Bay, said: “Joining Munich Re will accelerate At-Bay’s mission to close the cybersecurity protection gap for the 90% of businesses being left behind. With Munich Re, we gain the scale and reach to better address the evolving needs of every small business.”

Deal Expands Munich Re’s Cyber Risk Capabilities

Once the insurtech acquisition closes, the At-Bay business will be overseen by Hartford Steam Boiler, the technology-forward and cyber-focused arm of Munich Re’s Global Specialty Insurance business. HSB has been a main strategic partner since At-Bay’s founding.

Jeffrey O’Shaughnessy, president and chief executive officer of HSB Group, said: “The combination of At-Bay’s market-leading cyber capabilities and HSB’s intense cyber and underwriting expertise will significantly enhance our cyber offering and accelerate our speed to innovate in a market moving towards vertically integrated insurer-security platforms.”

This cyber insurance acquisition positions Munich Re for greater access to a cyber market evolving from standalone coverage towards integrated, continuously managed risk mitigation platforms. The deal highlights the growing role of technology-driven underwriting and proactive cyber risk management across the insurance industry.

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Recent industry analysis shows that paid losses, investigation expenses and settlement costs accounted for roughly 76% of US P&C premiums in 2022. That makes claims performance financially significant even before considering its effect on customer relationships and future business.

The commercial impact is becoming clearer as well. A 2026 survey covering more than 800 commercial claims experiences found that 78% of brokers said an unsatisfactory claims experience reduced their trust in an insurer, while 39% said they were unlikely to place business with that insurer after a poor experience. By comparison, 95% of brokers who reported a positive claims experience said they were likely to place business with the insurer again within the following year.

That puts claims service on both sides of the financial equation. Poor handling can increase rework, complaints and the risk of losing future business. Better handling can support stronger broker relationships, faster resolution and more efficient claims operations.

Claims Service is Becoming a Commercial Advantage

The financial value of claims service becomes particularly visible in commercial insurance, where brokers and businesses compare insurers not only on price and coverage, but also on what happens when a claim actually needs to be resolved.

A poor claims experience can weaken trust even when the underlying policy remains competitive. The commercial-lines research found that nearly four in five brokers who experienced an unsatisfactory claims outcome reported lower trust in the insurer. The fact that 39% would then be unlikely to place business with that insurer shows how a claims problem can extend beyond one loss and affect future premium opportunities.

The same pattern appears in retail insurance. Recent claims research found that 52% of customers who rated their digital claims experience as poor or only okay were at risk of attrition, compared with just 4% among customers who rated the experience excellent or perfect.

Claims experience is not the only factor behind retention. Price, coverage, competition and broader customer relationships still matter. But the difference in attrition risk shows that the claims journey can materially influence what happens after a customer has actually needed to use the policy.

For insurers, claims service is therefore moving beyond a customer-support function. It is becoming part of the commercial proposition, with potential consequences for retention, distribution relationships and long-term profitability.

Key takeaway: Claims service can influence broker trust and future business placement, making claims performance commercially important as well as operationally important.

Faster Claims Can Improve Both Experience and Efficiency

The value of claims service is not limited to whether a customer feels satisfied. How quickly and clearly a claim is handled can also influence operating efficiency, repair costs and the amount of work required before a case is finally closed.

Recent property-claims research shows how strongly speed can shape the customer experience. Average time from first notice of loss to final payment has risen to more than 44 days, while the average repair cycle is around 32.4 days. Claims completed within 10 days recorded an average satisfaction score of 762 out of 1,000, compared with 595 when repairs took more than 31 days.

The difference is not simply about making payments faster. Delays can create additional communication, repeat contacts, temporary accommodation costs, contractor coordination and other administrative work. When a claim remains open for longer, insurers may also need more staff time and resources to manage it.

Communication can make an equally important difference. In the same research, customers who found it very easy to communicate with their insurer recorded satisfaction of 777, compared with just 337 among those who found communication difficult. That suggests insurers can improve the claims experience not only by reducing the time needed to resolve a loss, but also by making the process easier to understand and follow.

For claims service, this creates a useful operational target: reduce avoidable friction without compromising the accuracy or quality of the decision.

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The financial incentive is substantial. Current industry estimates put the annual cost of insurance fraud to consumers and businesses at around US$308.6 billion across insurance lines. Property and casualty insurance represents a significant part of that burden, with recent industry analysis estimating that about 10% of P&C claims may be fraudulent, potentially representing around US$122 billion in annual losses. These figures are estimates rather than directly measured global losses, but they show the scale of the problem insurers are trying to address.

The challenge is that fraud is not always obvious. A deliberately staged accident may be easier to identify than a genuine claim where repair costs have been exaggerated or an injury has been overstated. Recent industry estimates suggest soft fraud accounts for about 60% of fraud incidents, while detection rates are estimated at only 20% to 40% for soft fraud, compared with around 40% to 80% for hard fraud.

That is where AI is becoming more relevant to insurance fraud. Instead of relying only on fixed rules, insurers can use machine learning and other AI techniques to examine patterns across claims and compare information from different sources.

AI is Expanding What Insurers Can See in a Claim

A modern insurance claim can contain much more than a written description of a loss. It can include photographs, videos, repair estimates, documents, audio, location information, telematics and other data. AI can analyse these different formats together, helping insurers identify inconsistencies or connections that may be difficult to spot through manual review alone.

This is particularly important for soft fraud, where the claim itself may be genuine but the value or circumstances have been exaggerated. Instead of asking only whether a claim meets a predefined fraud rule, AI can look for unusual combinations of information and direct investigators toward claims that deserve closer attention.

The technology is already gaining attention across the insurance industry. Recent research found that 35% of insurance executives identified fraud detection among their top five areas for developing or implementing generative AI applications. At the same time, the fraud-detection technology market is estimated to grow from US$4 billion in 2023 to US$32 billion by 2032.

Key takeaway: Insurance fraud remains a major financial cost, while lower detection rates for soft fraud create a clear opportunity for more advanced analytics.

The opportunity for insurers is therefore not simply to find more suspicious claims. It is to identify the right claims earlier, investigate them more efficiently and reduce losses without slowing down legitimate claims.

AI is Turning Fraud Detection into a Claims-Economics Problem

The financial case for insurance fraud detection is becoming harder to ignore, but finding more suspicious claims is only part of the equation. Insurers also need to consider the cost of investigating those claims, the time taken to settle legitimate losses and the additional customer and regulatory risks created by false positives.

Traditional fraud systems often rely on fixed rules and predefined triggers. These can still be useful, but they can struggle when fraud becomes more subtle or when the available evidence spans multiple formats. Current industry research estimates that soft fraud accounts for around 60% of fraud incidents, while detection rates remain substantially lower for soft fraud than for hard fraud.

AI can change that balance by examining claims across several sources at once. Instead of looking only at the information entered into a claims system, advanced models can combine text, images, audio, video, sensor information and other data to identify relationships or anomalies. This can help investigators focus their time on claims that warrant deeper review rather than manually examining every potentially suspicious case.

That distinction is important for insurance fraud because investigation itself has a cost. A model that flags thousands of questionable claims but sends too many legitimate customers into lengthy investigations may simply move the expense somewhere else in the claims process.

The more useful objective is therefore to improve the economics of detection by finding stronger signals earlier, reducing unnecessary investigations and preserving human expertise for complicated cases.

AI is Changing How Fraud Investigations are Prioritised

The biggest opportunity may come from changing how insurers allocate investigative resources.

Fraud teams have traditionally had to work through large volumes of claims and decide which cases deserve further investigation. AI can help score or prioritise those claims by looking for patterns that are difficult to identify manually, including unusual relationships between claimants, providers, repairers, previous claims and supporting evidence.

This is particularly useful in high-volume lines such as motor and property insurance, where even a small improvement in fraud detection can have a meaningful effect when applied across millions of claims.

The potential savings are significant. Industry analysis estimates that AI-driven technologies applied across the property and casualty claims lifecycle could potentially generate US$80 billion to US$160 billion in savings by 2032, depending on implementation and the sophistication of the systems involved. This is a forecast of potential savings, not money already captured by insurers.

At the same time, insurers are investing in the technology needed to pursue that opportunity. The fraud-detection technology market is estimated to grow from US$4 billion in 2023 to US$32 billion by 2032, reflecting growing demand for advanced analytics and automated detection.

Key takeaway: The growing cost of fraud and the potential savings from better detection are creating a financial case for insurers to invest in AI, but the value depends on how effectively those systems improve the entire investigation process.

The next challenge is making sure that investment produces better detection without creating more claims friction, because the economics of fraud detection ultimately depend on what happens to both fraudulent and legitimate claims.

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The market has grown beyond a niche financing mechanism. The NAIC has cited a US$17 billion global litigation-funding industry, based on 2021 data, with more than half of that capital deployed in the United States. More recent European research puts litigation-funding investment in the region at more than €3 billion, with further growth expected as collective actions and other forms of large-scale litigation expand.

For insurers, the importance of litigation funding lies in what happens after a claim enters the legal system. Liability claims can already take years to resolve, particularly where damages are uncertain or multiple parties are involved. External capital can give plaintiffs the financial resources to continue funding lawyers, expert witnesses and other case costs without carrying the full expense themselves.

That does not mean litigation funding automatically creates larger claims. The relationship is more complicated. Funding is one factor within a broader system that includes social inflation, litigation strategies, legal advertising, plaintiff behaviour and changing jury outcomes. But as more outside capital enters the process, insurers have another variable to consider when estimating the eventual cost and duration of liability claims.

Litigation Funding is Becoming Part of the Claims Economy

The basic model is straightforward. A third-party funder provides money to support a legal claim and receives an agreed return if the case succeeds. The arrangement can help claimants pursue disputes that may otherwise be too expensive to sustain, particularly where cases require significant legal work or expert evidence.

For the insurance market, the financial effect can extend beyond the amount eventually awarded. A better-funded case may be able to run for longer, withstand settlement pressure and support more extensive legal work. That can increase defence costs and keep a liability claim open for longer, making the eventual financial outcome harder to predict.

This matters particularly for long-tail insurance lines. Insurers may write a policy today but only discover the final cost of a liability claim several years later. When the legal environment changes during that period, historical loss data can become a less reliable guide to future claims.

The connection with social inflation is therefore important. Recent research identifies litigation funding as one contributor to the broader increase in liability claims costs, alongside factors such as higher jury awards, changes in plaintiff strategies and expanding litigation activity.

The strongest way to understand the shift is to look at the claims process as an economic chain. External capital can affect the ability to sustain litigation, which can influence legal expenses and settlement dynamics, which can ultimately affect the cost an insurer has to reserve and pay.

Key takeaway: Litigation funding has grown into a meaningful source of capital for legal disputes, creating another financial factor for insurers to consider when assessing liability claims.

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This is changing the role of AI regulation in insurance. It is no longer simply a question of whether an insurer is allowed to use artificial intelligence. The bigger issue is whether the insurer can show that an AI system is accurate, fair, secure, explainable and properly governed when it influences a claim.

That matters because claims decisions can directly affect policyholders. An AI system may help assess damage, identify unusual claims or recommend a settlement, but the consequences of an incorrect or biased decision can extend beyond an individual claim. They can create disputes, regulatory exposure, customer complaints and financial losses for insurers.

Regulators are responding in different ways across major markets. In the US, the insurance sector is building its AI oversight framework through state-level regulation and the National Association of Insurance Commissioners’ model guidance. By March 2026, 25 states had adopted the NAIC Model Bulletin, while regulators in 12 states were participating in a pilot AI Systems Evaluation Tool designed to examine insurers’ AI governance and risk-management practices.

Europe is taking a different route. The EU’s AI framework works alongside existing insurance regulation and places particular emphasis on governance, data quality, record-keeping, fairness, cybersecurity, transparency and human oversight. The European insurance regulator has also been working on how the AI Act applies to insurance-specific models and systems.

The result is not one global set of rules. Instead, insurers are facing a growing set of expectations that can vary by market while still focusing on many of the same risks.

AI Governance is Becoming Part of the Claims Process

The most important change is that AI regulation is moving closer to day-to-day claims operations.

An insurer using AI to summarise documents faces a different level of risk from one using an automated system to influence claim severity, fraud investigations or settlement decisions. The closer an AI model gets to a decision affecting a policyholder, the greater the need for human oversight, documentation and clear accountability.

The NAIC’s AI guidance makes clear that using an AI system does not remove an insurer’s existing legal obligations around unfair discrimination, unfair trade practices or consumer protection. Insurers also need governance and risk-management processes that allow regulators to understand how AI-supported decisions are being made.

That is creating a more practical model for claims teams. Automation can handle repetitive work, but insurers still need people who can review exceptions, challenge automated recommendations and take responsibility for final decisions.

At the global level, the International Association of Insurance Supervisors has identified five broad areas for AI supervision: risk-based oversight, governance and accountability, robustness and security, transparency and explainability, and fairness, ethics and redress.

For insurers, this means AI governance is becoming part of claims management itself, rather than something handled separately by technology or compliance teams.

Different Rules are Creating a Common Governance Challenge

The regulatory landscape is developing differently across markets, but the concerns behind AI regulation are becoming remarkably similar. Insurers are being asked to understand how AI systems work, what data they use, how decisions are monitored and who remains accountable when something goes wrong.

In the US, insurance regulation is developing largely through state-level frameworks. Regulators are increasingly asking insurers for more information about their AI systems, governance practices, risk controls and the data used by models. A regulatory AI evaluation tool was being piloted across 12 states as of March 2026, with the aim of helping supervisors assess insurers’ use of AI in areas including claims and other operations.

Europe is taking a more structured risk-based approach through the EU AI Act alongside existing insurance-sector rules. The European insurance supervisor has emphasised data governance, record-keeping, fairness, cybersecurity, explainability and human oversight. Importantly, not every AI system used by an insurer is automatically classified as high risk. The regulatory treatment depends on how the system is used and the risks it creates.

The distinction matters for claims teams. An AI system that summarises documents creates a different level of risk from one that influences a settlement recommendation or flags a policyholder for fraud investigation. As AI moves closer to decisions that affect coverage and payments, the need for clear controls becomes greater.

That is why global supervision is increasingly focused on risk-based and proportionate governance, rather than treating every AI application in the same way. International supervisory guidance now groups the main concerns around governance and accountability, system robustness and security, transparency and explainability, and fairness and redress.

Third-Party AI is Bringing New Responsibility into Claims

Another challenge is that insurers do not necessarily build the AI systems they use. Claims platforms, fraud tools, document models and image-assessment systems may come from external technology providers or rely on third-party models. That does not remove the insurer’s responsibility.

International supervisory guidance specifically highlights third-party risks and says insurers need to understand and manage the systems they use and the outcomes they produce.

This becomes particularly important when an AI system influences a claims decision. If a model produces an inaccurate recommendation, the insurer still has to deal with the customer, the regulator and potentially the financial consequences.

Recent European research shows how quickly this issue is developing. A 2026 survey found that insurers are adopting generative AI cautiously, with 49% of surveyed insurers having already developed dedicated AI policies, compared with roughly one quarter in 2023. The most frequently cited risks were inaccurate AI outputs, cybersecurity and data-protection concerns, while reliance on third-party providers was also widespread.

For claims operations, this means governance is becoming part of implementation rather than a final compliance check. Insurers need to know which decisions can be automated, which require human review, how model performance is monitored and how an outcome can be challenged or explained.

The broader direction of AI regulation is therefore becoming clear. Regulators are not simply deciding whether insurers can use AI. They are increasingly defining the conditions under which insurers can use it responsibly and demonstrate that the technology remains under control.

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The number is slightly lower than the US$141 billion recorded in 2024, but that does not mean the underlying risk has eased. The lower figure was partly linked to the absence of a major US hurricane landfall in 2025. Long-term industry analysis continues to place the real annual growth rate of insured catastrophe losses at around 5% to 7%, driven largely by growing exposure, higher asset values and rising reconstruction costs.

This distinction is important for catastrophe claims. Insurers cannot plan only around the result of one calendar year. They have to prepare for a claims environment where the baseline keeps moving higher and where a single severe event can still create a sudden surge in claims volume.

Secondary Perils are Driving More of the Claims Burden

One of the biggest changes is where catastrophe losses are coming from. Secondary perils, including severe convective storms, wildfires and floods, accounted for a record 92% of global insured natural-catastrophe losses in 2025. Severe convective storms alone generated around US$51 billion in insured losses, while the Los Angeles wildfires produced around US$40 billion.

This changes the operational challenge for insurers. A major hurricane or earthquake can create a huge claims surge, but repeated secondary events can keep claims teams under pressure throughout the year. Hailstorms, damaging winds, wildfires and floods can affect thousands of policyholders across different locations, creating a steady flow of claims that still require assessment, communication, repair coordination and settlement.

The growing importance of these events also means insurers are dealing with catastrophe exposure outside traditional peak-peril scenarios. In 2025, insured natural-catastrophe losses came from 190 events, with nearly half of total economic losses covered by insurance. That still left a significant protection gap between economic damage and insured losses.

For insurers, the issue is therefore becoming as much about claims capacity as financial loss. A large catastrophe can quickly increase demand for adjusters, engineers, repair networks and customer-service teams, while systems need to process much higher volumes without sacrificing accuracy.

Key takeaway: Catastrophe claims are becoming a persistent operating challenge because frequent secondary events are keeping the global insured-loss baseline elevated.

Catastrophe Claims are Creating Persistent Pressure on Insurers

The pressure created by natural catastrophes is no longer limited to a few major events each year. Insurers are increasingly dealing with a steady flow of storms, floods, wildfires and other secondary perils while still having to prepare for the much larger losses that can come from major hurricanes or earthquakes. In 2025, global insured natural-catastrophe losses reached US$107 billion, the sixth consecutive year above US$100 billion.

The lower figure compared with 2024 does not mean the underlying risk has eased. Swiss Re estimates that insured catastrophe losses are still rising by around 5% to 7% a year in real terms, largely because more homes, businesses and infrastructure are located in exposed areas and the value of those assets continues to increase. The 2025 loss total was also below the long-term trend partly because there was no major US hurricane landfall.

For catastrophe claims, that creates a difficult operating environment. Insurers have to manage the normal flow of claims while being prepared for sudden surges that can overwhelm adjusters, repair networks and customer-service teams.

Secondary Perils are Keeping Claims Pressure High

One of the biggest changes in the catastrophe market is the growing importance of secondary perils. Wildfires, severe convective storms and floods accounted for a record 92% of global insured natural-catastrophe losses in 2025. Severe convective storms generated about US$51 billion in insured losses, while the Los Angeles wildfires generated around US$40 billion.

This matters because secondary events can create a different kind of claims challenge from a single major hurricane. Instead of one large event producing a concentrated surge, insurers can face repeated claims from storms, hail, flooding and wildfires across different regions throughout the year.

The numbers also show how wide the exposure has become. Natural catastrophes generated 190 events and about US$220 billion in economic losses in 2025, of which US$107 billion was insured. That left a substantial protection gap, with households and businesses still carrying a large share of the economic damage themselves.

Key takeaway: Catastrophe claims pressure is increasingly being created by frequent secondary events, not only by major hurricanes and earthquakes.

The financial challenge becomes even more difficult when peak-loss scenarios are considered. Swiss Re estimates that insured natural-catastrophe losses could reach around US$148 billion in a trend year in 2026, but could rise to US$320 billion in a peak-loss scenario. By 2030, the modelled peak could reach about US$400 billion as exposure continues to accumulate.

That means insurers have to prepare for two different realities at the same time: a higher everyday claims burden from frequent events and the possibility of a much larger loss year that can place sudden pressure on claims capacity, reinsurance and capital.

Conclusion

Catastrophe claims are becoming a more persistent challenge for insurers as frequent secondary events keep claims volumes elevated while major catastrophes continue to carry the potential for much larger losses. The issue is not simply the total value of insured damage. It is also the industry’s ability to process large numbers of claims quickly, maintain service quality and manage the financial impact across the balance sheet.

The underlying trend makes preparation increasingly important. With insured catastrophe losses remaining above US$100 billion for a sixth consecutive year and long-term losses continuing to rise in real terms, insurers need to plan for both frequent claims activity and low-frequency, high-severity events.

For insurers, catastrophe claims are therefore becoming an issue of claims capacity, capital, reinsurance and customer trust at the same time. The strongest response will come from combining financial resilience with better claims preparation, data, technology and workforce capacity before the next major event arrives.

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The difference is important. Claims can reveal changes in loss severity, litigation behaviour, repair costs, fraud patterns, emerging exposures and differences between jurisdictions. When those signals are connected to underwriting, they can help insurers reassess risks before a policy reaches renewal rather than waiting for a loss trend to become obvious in historical results.

The shift is already visible across the market. Recent research found that 92% of property and casualty insurers expect big data to generate significant improvements in areas including pricing, underwriting, claims management and client experience. Separate research across European insurers found that AI is already being used across areas including pricing, underwriting, claims and fraud detection, showing how claims and underwriting are becoming increasingly connected through data and analytics.

This is creating a new role for claims intelligence. Instead of treating claims as a final record of loss, insurers can use patterns in those claims to identify changes in risk while there is still time to act.

Claims are Becoming an Early Signal for Underwriting

The value of claims data does not necessarily come from any one claim. It comes from patterns that appear across thousands of claims over time.

A rise in claims severity in one industry, for example, could signal that the underlying risk is changing. A growing number of disputes in a particular jurisdiction could point to a change in litigation exposure. Rising repair costs could indicate that existing loss-cost assumptions need to be revisited. Repeated claims involving a particular asset, process or type of incident could also reveal an exposure that was not fully understood when a policy was originally priced.

This makes claims intelligence useful well before a claim is finally closed. The Connected Claims industry agenda is increasingly focused on creating governance and data flows that move loss information into underwriting and actuarial teams ahead of renewal, including signals such as plaintiff tactics, jurisdictional trends and emerging exposures.

For insurers, that can change the underwriting cycle from a largely retrospective process into a more continuous one. Instead of waiting until a claim is closed to study what happened, insurers can monitor claims continuously, identify emerging risk patterns early, and use those insights to adjust underwriting decisions before the next policy renewal.

The commercial value lies in what happens next. An insurer may reprice a risk, change policy terms, adjust limits or retentions, increase risk controls, or reconsider its appetite for a particular exposure. The decision will vary by line of business, but the underlying idea is the same: claims data becomes an input into the next underwriting decision, rather than a report on the previous one.

That is why claims intelligence is becoming more than a claims-management tool. It is becoming part of how insurers decide where and how they deploy underwriting capacity.

Key takeaway: Claims data becomes more valuable when it is used to identify emerging risk before the next underwriting decision.

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