The reason everyone seems to have a scam story these days isn't due to an increase in reporting; it's because scams have become a near-universal experience. As underscored by a significant increase in the past five years of both reported incidents to the FBI and mainstream media coverage, scams are more frequent, more costly, and more difficult to discern than ever before.

The State of Scams USA 2025 report, conducted by the Global Anti-Scam Alliance (GASA) and sponsored by Iris Powered by Generali, showed that 77% of American consumers encounter scams on a daily basis, with over 70% indicating they had been scammed in the last 12 months. The report also found that one in five Americans lost money to a scam in the same time frame, with an average of over a thousand dollars lost per person and over $64 billion stolen in total.

With scams and fraud on the rise, consumers have turned to institutions and communication platforms for help. Almost three in four (74%) respondents who had experienced a scam reported it to an authority or company for assistance. That is consistent with the findings of the Iris 2025 Identity and Cybersecurity Concerns survey (“ICC”) conducted in April, which found that most consumers reach out directly to companies that have been part of a data breach. However, over half the time, nothing is done – with 57% of reported incidents having no discernible action taken. Even worse, of the 82% of U.S. consumers who reported scams to payment services or financial institutions, less than half (44%) were able to partially recover money in the end, and 38% received nothing back at all.

This gap between consumer action and institutional response feeds a dangerous sense of futility: if reporting scams doesn’t lead to meaningful outcomes, why report at all? This mentality can allow scammers to gain the upper hand. Americans need an ally in the fight to defend themselves against scammers, and they’re expecting financial and communications platforms to step up.

Digital Platforms Top the List for Scammer Channels

By and large, scammers are targeting consumers digitally. Most consumers reported encountering scams via SMS messenger, followed closely by emails and phone calls. Americans reported that 82% of scam attempts occurred on platforms with direct messaging capabilities, including social media, instant messengers, online marketplaces, and even digital ads.

In terms of platforms, Gmail ranked highest in reported instances at 45%, followed closely by Facebook at 41%. TikTok, Snapchat, and X (Twitter) ranked notably lower, but consumers tended to take the longest to recognize that they were being scammed on those platforms.

Consumers are offered little recourse through the platforms themselves. Recent reports indicate that large social or digital communications platforms can take weeks to act when scams are reported. This lack of urgency contributes to an erosion of consumer trust.

Most Lose Money Through Debit Cards and PayPal

Debit cards were the most common method used by scammers, accounting for 30% of reported losses to fraud, followed by PayPal at 25% and credit card payments at 23%. When fraud occurred, most consumers discovered it themselves: 66% discovered it on their own, while only 14% were alerted by their bank or financial services provider.

Americans who were affected overwhelmingly reported the fraud to banks or payment services, with 82% reaching out for support once they realized they had been scammed. But again, this ultimately had mediocre returns for consumers. Likewise, according to Iris' ICC survey, 46% of Americans say their first call would be to their bank after receiving a notification of a data breach, making it their top choice.

These patterns make it clear that consumers view banks and payment platforms as their frontline defense. But when response and recovery prove insufficient, trust is eroded.

Consumers Blame Commercial Organizations – But U.S. Laws Don't

Consumer protection authorities are contacted only 12% of the time, compared with banks or payments services at 25%.

While one in three Americans believe that commercial organizations should be responsible for protecting consumers, U.S. laws and regulations don't agree. For instance, authorized user payments, such as those through platforms like Zelle or Venmo, have no legal requirement for banks to reimburse customers. Additionally, newer scams like imposter scams or AI/deepfake scams are not covered by older FTC regulations and U.S. laws, leading to confusion and denials from banks to reimburse victims.

Where Third-Party Identity Protection Services Fill the Gap

Consumers need stronger support in the fight to protect their identities – and wallets – online. Yet major commercial organizations and digital communications platforms are failing to provide adequate protection.

Iris' ICC survey found that most consumers want a comprehensive, all-in-one solution and are willing to pay for it. While just three in 10 Americans indicated they follow all recommended data protection practices, close to eight in 10 said they would likely use identity protection features if they were integrated into an app they already use, with banks and credit card providers being one of their top picks to purchase from.

Third-party identity protection solutions help close critical gaps. Tools that monitor for compromised data on the dark web, help to spot scams, and offer expert fraud recovery services aren't new but are increasingly sought after. These services not only accelerate resolution by managing outreach to banks and authorities but also help ease the emotional toll of falling victim to scams – a cost that's often overlooked. Additionally, they often take critical steps on the consumer's behalf to prevent further damage.

Consumers want accountability from today's institutions, but they also want protection and peace of mind. There are bills currently under review by the U.S. Congress – like the Protecting Consumers from Payment Scams Act – that are aimed at addressing accountability gaps with banks and payment providers. But responsible businesses shouldn't wait for laws to catch up with the rising threats; they should show up today for their customers and be the ally they need by offering protection.

Not only is it the right thing to do – but it's a powerful investment in customer loyalty and trust.

Walk through any insurtech conference today and you'll hear "agentic AI" mentioned at every turn. Every vendor booth promises autonomous systems that can think, act, and learn. But when you examine these solutions more closely, many turn out to be large language model (LLM) implementations with intelligent automation added. These are valuable advances, certainly, but not the autonomous agents they claim to be.

This terminology inflation creates a fundamental problem. When insurance executives hear every vendor claiming to have "agentic AI," the market becomes so cluttered that companies that invest in building these new capabilities get lost among rebranded automations.

Defining Agentic AI

Part of the problem is that there's no uniform definition of what makes AI "agentic." Different experts emphasize different aspects: Some focus on autonomous decision-making, others on learning capabilities, and still others on goal-directed behavior. But while the exact boundaries remain fuzzy, we can certainly identify what agentic AI is not.

It's not just a chatbot with a fancy prompt. It's not a series of LLMs strung together with if-then logic. And it's definitely not traditional automation rebranded as AI.

One proposal is that true agency requires at least three core capabilities:

1. Tool usage - the ability to navigate and interact with different systems
2. Memory - maintaining context and learning from past interactions
3. Real-time adaptation - adjusting approach based on results when something unexpected happens

The coding assistants like Cursor and Claude Code offer a useful reference point. These tools represent the current state of the art in AI, and most industry observers would comfortably label them as "agentic." If these are our benchmarks for genuine agency, the gap with most "agentic AI" solutions in insurance becomes clear.

This distinction matters because it reveals a spectrum. On one end, you have simple automation following predetermined paths. On the other end, you have fully autonomous systems that set their own objectives and continuously evolve. Most of what's being called "agentic" in insurance today sits firmly at the automation end, despite the marketing claims.

Current Market Examples

The evidence for this is everywhere. Take one major claims administrator's recent announcement of their "agentic AI" solution. Dig deeper, and it's a bundle of voice bots, intelligent document processing, and some alerting.

Another prominent vendor markets six different "AI agents" as part of their agentic platform. Remove the marketing speak, and you find a data layer with LLMs for document routing, a chatbot that accesses internal data, and template generation with compliance checks. These are often solid implementations that deliver real value, but they're a far cry from being truly "agentic."

The Market Distortion

The pressure to appear cutting-edge creates an arms race of terminology. When every vendor feels compelled to claim "agentic AI" to stay competitive, an insurtech that invested heavily in genuine foundations—tool usage, memory, and real-time adaptation—gets lumped in with one that simply added "agent" to their chatbot's name.

This creates an unfortunate dynamic. Insurance executives face an impossible task: evaluating solutions when every vendor uses the same terminology for vastly different capabilities. Even sophisticated buyers struggle to identify which systems will grow into true agency as AI matures versus those that are essentially dead ends with fancy names.

When implementations fall short of vendor promises, it naturally reinforces skepticism about AI investments. The insurtechs building for the future get caught in this backlash, making meaningful transformation even more challenging. Everyone loses: Buyers miss out on genuinely transformative technology, real innovators struggle to differentiate themselves, and the industry's digital evolution slows to a crawl.

The Path Forward

The insurance industry doesn't need to claim false sophistication. Current AI applications can provide tremendous value. Intelligent document processing saves countless hours. Well-designed chatbots genuinely improve customer experience. Predictive analytics enhances decision-making in measurable ways. These are powerful tools that augment human capabilities. The industry benefits when we accurately describe what these tools accomplish and match them to appropriate use cases.

For those evaluating solutions, start with a more fundamental question: Do you actually need agentic AI? If your goal is to reduce document processing time by 80%, intelligent automation might be exactly what you need. If you want to improve first-call resolution rates, a well-designed LLM-powered chatbot could be the perfect solution. These aren't agentic, but they solve real problems with proven technology available today.

Reserve the search for true agentic capabilities for problems that actually require them: complex claims that need dynamic investigation across multiple systems, underwriting decisions that must adapt to unique scenarios in real-time, or fraud detection that needs to evolve its approach as schemes change. For these use cases, ask the hard questions: Can this system actually use tools to solve problems? Does it maintain context across interactions? Can it adapt when things go wrong?

As agentic AI capabilities mature, they will transform how we handle claims, assess risk, and serve customers. But we'll only realize that potential if we're honest about where we are today and deliberate about where we're investing for tomorrow.

As buyers and builders, we all have a role in maintaining clarity about what AI can actually accomplish. This ensures that success goes to companies building on real capabilities rather than marketing claims, while preserving confidence in AI's genuine transformative potential.

In P&C insurance, delay can cost millions. Yet critical insights are often stuck in spreadsheets, buried in SQL queries, or waiting in the inbox of an overloaded data team. Underwriters, actuaries, analysts, and executives depend on timely answers, but bottlenecks in access to data slow decisions, erode agility, and hold back growth.

From Queries to Conversations: Redefining P&C Analytics

AI-powered conversational analytics, exemplified by solutions like Snowflake Cortex Analyst, represents a significant leap forward, transforming how P&C insurance companies access and use their structured data. By offering a natural language interface, these solutions empower business users to directly interact with data, providing instant answers without the need to write complex SQL queries.

Whether you are an analyst buried in SQL requests, an actuary tracking loss severity trends, or an executive making growth decisions, conversational analytics ensures you can get trusted answers instantly.

Six Strategic Benefits of Conversational Analytics for P&C Leaders

1. Empowering P&C Professionals

Conversational business intelligence (BI) tools directly enable underwriters, claims adjusters, and actuaries to obtain immediate answers to their business questions, significantly reducing their dependency on overloaded data teams.

Conversational Analytics in Action:

An actuary reviewing quarterly loss ratios notices a spike in severity but cannot pinpoint the cause without a custom report. Traditionally, this would mean submitting a request to the data team and waiting days for an answer. With conversational analytics, they simply ask: "Show me loss severity by line of business over the past 12 months." Within seconds, they have the insights needed to recommend pricing adjustments before the next underwriting cycle.

2. Targeted Executive and Operational Insights

Executives, marketing, and actuarial teams are ideal beneficiaries of ad hoc querying and discovery. They can quickly explore data, understand trends, and inform decisions without waiting for pristine, pre-defined reports.

Conversational Analytics in Action:

During a monthly leadership review, executives see an unexpected drop in renewal conversions. Normally, they would debate whether the numbers are accurate, request more data, and revisit the topic weeks later. Conversational analytics eliminates the wait. An executive can drill into renewal performance by product line, geography, or agent channel instantly, making it possible to align strategy and respond in real time, not after the quarter has closed.

When a catastrophic weather event strikes, claims volumes can spike overnight. Executives need immediate insight into incurred losses, reserve impacts, and cashflow forecasts to make operational and customer decisions quickly. With conversational analytics, leaders can instantly model claims by region, line of business, or severity—turning what once took weeks of reporting into rapid, informed response.

3. Rapid Analysis of Critical P&C Metrics

The solution enables fast analysis of profitability, growth, and risk metrics:

• Profitability: Combined Ratio, Incurred Loss Ratio, Underwriting Expense Ratio (UWE)
• Loss Costs: Loss Frequency, Loss Severity, Loss and Reserve Development (LRD)
• Growth: New Business (NB) Binds, Renewal (Ren) Conversion Ratio, Written Premium (WP)
• Cashflow: Earned Premium (EP), Collected Premium

By tying these directly to strategic levers such as pricing, retention, and claims management, leaders can act faster and with confidence.

4. Enterprise-Grade Security and Governance

Solutions built within secure data platforms ensure that data, metadata, and prompts remain strictly within governance boundaries, with role-based access controls (RBAC) intact. Every query adheres to enterprise policies, protecting privacy and compliance.

5. Streamlined Data Access and Control

Integration within a unified platform creates easier, more controlled access to data. This reduces friction, enhances trust, and ensures analysts and executives are always working from the same single source of truth.

Conversational Analytics in Action

Analysts in many insurers spend more time fulfilling one-off requests than actually analyzing data. A marketing leader might ask for retention ratio by region, while underwriting wants conversion rates by channel, each requiring new queries and validation. With conversational analytics, those stakeholders can self-serve the basics. Analysts are freed from being "SQL order takers" and instead focus on deeper projects like identifying emerging risk drivers or developing predictive models.

6. Low-Investment AI Experimentation

For P&C companies already on platforms like Snowflake, conversational analytics offers a low-investment way to explore AI-driven insights. Leaders can experiment without major upfront commitments, building both organizational confidence and future readiness.

AI-powered conversational analytics is more than a technology upgrade. It is a strategic imperative. For actuaries, it means faster insights into loss ratios and risk drivers. For analysts, it means shifting from query fulfillment to strategic analysis. For executives, it means eliminating delays and making confident, timely decisions.

In a market where every day of delay can cost millions, the insurers who embrace conversational analytics now will lead. Those who do not risk being left behind.

Enterprise success depends not merely on being data-rich but on decision quality. As insurers strive to scale beyond pilots and deliver better outcomes, it is imperative to diagnose the gap and find an approach to bridging the divide. This article demonstrates how Decision Intelligence—combining decision memory, adaptive analytics, and explainable AI—codifies tacit knowledge into institutional capability, reducing variability and enabling consistent, transparent, and resilient performance in a volatile and dynamic environment.

Problem Statement: What?

In today's VUCA environment [volatility, uncertainty, complexity and ambiguity], insurers face a paradox. Despite abundant data from broker submissions, underwriting systems, claims, third-party sources, and significant investments in analytics, better insights do not always translate into better outcomes.

Consider a commercial property underwriting scenario, where two underwriters, given the same portfolio, guidelines, data and insights, often arrive at different decisions. This divergence stems from how each interprets insights, applies them in workflows, and navigates multiple decision paths. This tacit knowledge that drives these choices remains uncaptured, limiting institutional learning and consistent performance.

Bridging this gap requires a new paradigm, Decision Intelligence, to translate the art of decision making into a science that adapts to the environment.

Understanding the Gap in Decision-Making

Commercial property underwriting is inherently complex, involving numerous factors that influence risk exposure, insurability, pricing, and profitability. Most insurers have focused on improving underwriting cycle time, but with limited focus on consistency of decisions. Current systems accelerate processes but do not capture the rationale behind decisions or institutionalize the tacit knowledge. As a result, decision making remains highly dependent on individuals, creating variability in outcomes. For instance, underwriting a portfolio with higher premium (to cede the risk), or optimal premium with certain limits and exclusions, or seeking additional information from risk assessors or actuaries on assumptions, anomalies, etc.

How to bridge the Gap?

To close this gap, insurers need to move beyond process automation toward Decision Intelligence—a framework that embeds decision memory, simulations and explainability, knowledge graph powered by a decision agent. This digital capability connects data, insights, and decision logic, ensuring decisions are explainable, repeatable, and adaptable to changing market conditions. Below are some key components of Decision Intelligence.

Adaptive Analytics Agent

Discover hidden patterns/groups and key influencing features from the captured datasets using unsupervised learning and apply contrastive learning techniques to generate scenarios that will maximize the value and minimize risk. This decision assist agent provides its recommendations in natural language to the underwriter with explanations/rationale, for their review/feedback. The feedback loop is a critical linchpin for the agent to capture tacit knowledge – such as observations with respect to anomalies in roof condition scores and high claims related to roof repairs in that region with similar property characteristics.

Scenario Modeling and Explainability

Tools to simulate various scenarios (e.g.: open-source platforms such as Oasis Loss Modeling Framework, Fathom etc.) and determine annual average loss (AAL) for the portfolio. This risk assessment/modeling is governed by COPE framework (construction, occupancy, protection, and exposure).

It involves analysis of scenario values at location level, insurance-to-value (ITV) analysis to assess the coverage sought vs. cost to replace or repair insured property. ITV is driven by factors such as inflation, cost of replacement materials, labor shortages, etc. and helps to avoid underinsurance and coinsurance penalties. The underlying exposure (AAL) insights and pricing/premium governed by ITV analysis, helps to determine/forecast the profitability of the portfolio, based on cohorts and associated features that is driven by the principle of value maximization and risk minimization.

These in silico trials, augmented by ontology and powered by knowledge graph-driven decision memory and explainable AI, help underwriters to choose the decision paths. This capture of tacit knowledge and decision rationale will continuously evolve with integration to real-world environment (sensors, IoT, spatial imagery, geocoding etc.) and foresights, guiding underwriters in effective decision making.

Decision Memory

Building decision memory involves capturing the "why" and "what happened" in the workflows, including micro-decisions. This includes risk assessor notes, pricing notes and assumptions, property characteristics, perils considered, patterns/key influencing factors such as primary, secondary modifiers, anomalies between broker submission and risk assessment, risk mitigation strategies, exclusions, learnings/feedback loops from good and bad decisions. This would also involve digitizing the underwriting decisions by creating and integrating UI/prompts to capture and validate this additional information (human-in-the-loop) in the workflows.

Potential Benefits

By capturing rationale, simulating scenarios, and closing the loop with feedback, insurers reduce decision variance, improve price adequacy, and speed time-to-quote. This will result in better outcomes such as higher quote-to-bind on target segments, more written premium per underwriter, tighter loss and combined ratios, and stronger model governance—measured through KPIs such as decision-rationale coverage, outcome variability across underwriters with similar risk profiles, ITV accuracy, technical-price adherence, and audit-ready explainability with assumptions, trade-offs and overrides.

The Way forward

To achieve these objectives, organizations should prioritize the integration of advanced analytics and AI-driven decision support tools across underwriting, risk management, and data governance functions.

This includes establishing cross-functional teams to continuously refine models and decision frameworks based on real-world feedback, fostering a culture of transparency and learning, and leveraging technology to streamline workflows while upholding rigorous controls.

By aligning strategic goals with operational practices, insurers can unlock greater efficiency, consistency, and resilience in an evolving marketplace.

Cyber Awareness Month may occur only once a year, but cyber risks are ever-present for businesses, regardless of their size. With the frequency and sophistication of these attacks growing, and costing the U.K. economy an estimated £27 billion per year, it's vital that organizations understand the full cost of a cyber breakdown and how they can minimize this.

Among SMEs, ransomware attacks, data breaches and phishing incidents continue to rise, often leaving these businesses with fewer resources to respond and recover effectively. The average cost for businesses to remedy a cyberattack is estimated to be £21,000, an amount that can even bankrupt smaller companies.

U.K. businesses are facing a sobering reality: The actual cost of a cyberattack extends far beyond immediate recovery. While initial costs cover detection and response, hidden costs, such as lost business income, data restoration, regulatory fines, and reputational management, can linger.

Why Recovery Costs Don't Tell the Whole Story

For businesses, it's not a matter of if a cyberattack will hit you, but when. Just over four in 10 businesses (43%) reported experiencing a cyber breach or attack in the past 12 months, equating to approximately 612,000 businesses.

Reports suggest that 60% of small companies go out of business within six months of a cyberattack. For those who survive, they face major setbacks. For small businesses, a cyber breach or attack can set them back £65,000. However, this can be an underestimation of the full scale of the impact, as there is other fallout from cyber breaches, not limited to economic losses, but also reputational damage. The full cost of a cyber breach can involve challenges such as:

Business Interruption & Income Loss

The cyber landscape risk is changing, and increasingly, attackers aren't just looking to steal data but also to disrupt business.

A cyber breach or attack isn't an isolated incident. Once that happens, there are knock-on effects on businesses, from downtime that disrupts sales, issues with supply chains and eroded client trust. Many businesses will lose weeks of income, which can cripple small operations, especially in industries heavily reliant on online sales and client management.

For instance, if a business is targeted with ransomware and a demand for payment, accompanied by a threat that the company's data will not be restored unless payment is made, the business would be unable to conduct its day-to-day operations. Having a cyber policy that covers Direct and Dependent Cyber Business Interruption would be essential in this case to minimize losses.

M&S, for example, estimated that its cyberattack, which started in April 2025 and resulted in subsequent downtime, would cost them around £300 million in profit due to lost sales and increased operational costs from suspending online orders.

Data Recovery & System Restoration

Rebuilding technical processes, whether it involves systems, restoring backups, or investigating vulnerabilities, creates additional costs. Businesses may require specialist security experts to investigate and mitigate the loss. In the case of SMEs, they often don't have this expertise in-house.

Regulatory Compliance, Legal Fees & Penalties

Cyber breaches that result in a business's loss of personal or confidential information can lead to customer claims, breach of contract disputes, or regulatory fines under the Data Protection Act (GDPR).

Fines and legal fees under GDPR can push recovery costs even higher, particularly for SMEs that may lack in-house compliance expertise. The highest maximum an organization can be fined for this is £17.5 million or 4% of the total annual revenue in the previous financial year. Moreover, depending on the type of attack, there may be requirements to report to the Information Commissioner's Office (ICO).

Legal representation costs and external consultancy fees are high. However, with the right insurance policy in place, these costs can be covered.

Reputational Damage

The full cost of a cyberattack isn't always financial; it's often reputational, as well. If customers' data is stolen, it can affect future relationships, lead to customer churn, and ultimately affect the brand's value.

The Insurance Safety Net

Cyber insurance provides (indirect and direct) financial protection and access to expert legal and risk management support, enabling businesses to improve their operational resilience, defenses and adopt a proactive security approach.

Given the prevalence of cyber attacks, with AI making them more sophisticated, it's more critical than ever that businesses of all sizes invest in cyber insurance. It shouldn't be an afterthought; it needs to be a key priority for business resilience.

Encouragingly, many small businesses are taking note, with an increased uptake of cyber insurance from 49% in 2024 to 62% in 2025. There has also been an increase in security risk assessments and business continuity plans that address cybersecurity.

Coverage now extends far beyond simple data breaches. It can include ransomware payments, business interruption, legal fees, and even the cost of notifying affected customers.

Building Resilience Beyond Insurance

Insurance is a vital part of the cyber risk puzzle, but it's not a one-stop fix. The best insurance policies will be those that embed risk management into their policies, offering advice on how to appropriately train staff, create a risk management plan, implement multi-factor authentication, conduct regular audits, and more. Increasingly, the role of insurers is evolving to enable them to act as partners in prevention, not only by paying claims when things go wrong.

The insurance industry finds itself at a fascinating crossroads. While AI dominates board meetings across every sector, the claims space tells a more nuanced story: one of cautious optimism tempered by legitimate concerns about trust, accuracy, and regulatory compliance. A recent survey commissioned by our team at Wisedocs and conducted by PropertyCasualty360 reveals this paradox in detail, offering insights into how claims professionals view AI adoption and what it will take to gain trust across the industry.

The 2025 survey, "AI in Claims: The 4x Trust Effect of Human Oversight," polled claims professionals from PropertyCasualty360's audience, including adjusters, carrier-side claims managers, and third-party service providers. What emerged was a clear picture of an industry ready for technological transformation, but only under the right conditions.

Key Insight #1: The Trust Deficit

The survey's most striking finding centers on trust, or, rather, the lack thereof. Only 16% of respondents expressed medium or high trust in AI-generated outputs when used independently, with a mere 2% indicating high trust. This skepticism isn't born from technophobia but from practical concerns rooted in the high-stakes nature of claims work.

The primary barriers to AI adoption reveal why claims professionals remain cautious. Accuracy concerns topped the list at 54%, followed closely by compliance and regulatory risks at 49%, and integration challenges with existing systems at 45%. These aren't abstract worries – they reflect the reality that claims decisions carry significant legal, financial, and reputational consequences.

This cautious approach becomes even more apparent when examining current adoption patterns. A substantial 58% of respondents either don't use AI in their claims process or are uncertain whether their organization employs AI tools. This uncertainty itself is telling, suggesting that AI implementation in many organizations remains fragmented or poorly communicated.

Yet this trust deficit doesn't reflect a wholesale rejection of technology. Instead, it reveals an industry that understands the stakes involved and demands proven reliability before embracing new tools.

Key Insight #2: The Human-in-the-Loop (HITL) Solution

The survey's most compelling discovery lies in how dramatically trust levels shift when human oversight enters the equation. When respondents were asked about their confidence in AI outputs validated by expert reviewers, the percentage expressing medium or high trust jumped to 60% from 16%. Those reporting high trust soared from just 2% to 22%.

This trust multiplier effect varies by current AI usage. Among occasional AI users, 33% report medium or high trust in the technology, compared with 0% among those who don't use AI and have no adoption plans. This suggests that familiarity breeds confidence, but only when paired with appropriate oversight mechanisms.

Key Insight #3: Efficiency Over Everything

While trust in AI's decision-making capabilities remains limited, its value as a productivity enhancer is widely recognized. An overwhelming 75% of respondents believe AI can boost efficiency through improved speed and resource optimization, with nearly half (49%) also citing productivity gains via increased work volume capacity.

The areas where claims professionals see the most potential for AI impact align with administrative processing tasks rather than strategic decision-making. Document automation and data extraction led the way at 69%, followed by operational efficiency and workflow automation at 57%. Claims decision support, while still significant at 31%, ranked lower – a telling indication that professionals want to handle the groundwork, not the judgment calls.

This pattern extends to perceived benefits for claimants, as well. Respondents believe AI's primary contribution will be reducing administrative delays (71%) and enabling faster claims resolution (60%). They're less optimistic about AI improving accuracy (25%) or transparency (18%), suggesting a realistic understanding of current AI capabilities and limitations.

Key Insight #4: Industry Readiness for Adoption

Several broader trends emerge that paint a picture of an industry poised for significant change, albeit on its own terms. The claims sector's approach to AI adoption reflects a mature understanding of both the technology's potential and its limitations. The emphasis on efficiency gains over accuracy improvements reveals a pragmatic strategy. Claims professionals recognize that AI's current sweet spot lies in handling repetitive, time-consuming tasks that don't require complex judgment. By automating document processing, data extraction, and workflow management, AI can free human experts to focus on the nuanced work that genuinely requires their expertise.

This division of labor – AI for processing and humans for decision-making – represents a sustainable path forward. Rather than replacing claims professionals, AI becomes a force multiplier, enabling teams to handle larger caseloads while maintaining quality standards. The survey data suggests this approach resonates strongly with practitioners who see AI as a tool to enhance rather than replace their capabilities.

Key Insight #5: The Regulatory Reality Check

The highest ranking of compliance concerns (49%) in the survey reflects the claims industry's unique regulatory environment. Unlike consumer-facing applications, where AI adoption can move quickly, claims processing operates under strict regulatory oversight. Any AI implementation must meet not only operational requirements but also legal and compliance standards that vary by jurisdiction and line of business.

This regulatory awareness actually strengthens the case for HITL approaches. Expert oversight provides a vital compliance layer, ensuring that AI-driven efficiency gains don't come at the expense of regulatory adherence. The combination offers a way to modernize operations while maintaining the defensibility and auditability that regulators demand.

Key Insight #6: Building Toward a Broader Adoption

The survey reveals an industry transition, with 37% of respondents occasionally using AI and a further 38% considering adoption. This represents a significant cohort of organizations actively evaluating how AI fits into their operations. The key to converting consideration into implementation lies in addressing the trust concerns identified in the survey.

Organizations that lead in AI adoption will likely be those that successfully implement HITL processes from the start. Rather than viewing human oversight as a temporary bridge to full automation, successful adopters will likely embrace it as a permanent feature that enables both efficiency and trust.

Looking Forward to a Collaborative Future

The survey findings point toward a future where AI and human expertise can work in tandem. This collaborative model addresses both the operational pressures facing the claims industry and the trust requirements necessary for sustainable adoption. As AI continues to advance and demonstrate reliability in controlled environments, we can expect to see a gradual expansion of its role in claims processing. However, the HITL principle identified in this survey is likely to remain a priority in AI implementation in the claims space.

The claims industry's thoughtful approach to AI adoption may well serve as a model for other high-stakes sectors grappling with similar questions about balancing innovation with responsibility. By insisting on human oversight and focusing on efficiency gains over decision-making authority, claims professionals are charting a course that could accelerate AI benefits while maintaining the trust and reliability that the industry demands.

The survey data make clear that the question isn't whether AI will transform claims processing but how that transformation will unfold. The answer appears to lie not in choosing between human expertise and AI, but in finding the optimal combination of both.

Despite the critical role played by workers' compensation, the technology infrastructure supporting it—particularly in state-run insurance organizations—has long relied on legacy systems and plan-based software development (PBSD). These systems are often characterized by rigid architectures, lengthy release cycles, and siloed team structures, which hinder responsiveness and innovation.

To meet evolving business needs and improve service delivery, many public insurers are moving from traditional waterfall methodologies to agile frameworks. This shift to agile software development (ASD) promises enhanced collaboration, faster feedback loops, and iterative value delivery—laying the foundation for a more adaptive and customer-centric product ecosystem.

Pain Points of Waterfall Methodology

The traditional waterfall methodology presents several drawbacks when applied to managing workers' compensation projects, due to its rigid, sequential nature. While beneficial for static projects, its inflexibility clashes with the dynamic, unpredictable aspects of workers' compensation, such as evolving regulatory requirements, claim complexities, and the need for responsive communication.

• Rigidity and Expensive rework

The workers' compensation landscape is subject to frequent changes in regulations, legislation, and legal precedents. A waterfall approach assumes requirements are fixed from the start, making it difficult and expensive to incorporate new rules or policy changes that emerge during the project lifecycle. If a flaw is discovered late in a waterfall-style project—for instance, how an endorsement/form is implemented in relation to the compliance rules defined by a regulatory body —it can necessitate a complete redesign. This requires reverting to earlier phases, leading to significant delays and cost overruns.

• Limited Feedback Loop and Delayed Testing

There are also the challenges of limited/delayed feedback, scope creep, misaligned stakeholder expectations, and delayed testing. Stakeholders may be involved only at the beginning and end of the project, reducing collaboration and shared ownership. This can lead to a final product, like a policy administration system, that fails to meet their actual needs or new operational demands that arose during the long development process. Testing and quality assurance are typically reserved for the final stages of a waterfall project. This means critical system flaws, integration problems with existing products, or inaccurate claims processing may not be identified until after significant time and resources have been invested. Fixing these issues at this late stage is often very costly and time-consuming.

• Prolonged Delivery Time and Unpredictable Projects

Waterfall methodology comes with prolonged delivery time and risk of obsolescence. The sequential nature of the waterfall model (requirements → design → development → testing → deployment), where each phase must be fully completed and documented before the next begins, can significantly lengthen project timelines. Releases may occur quarterly or even annually, delaying value delivery and feedback loops.

For complex, long-running projects, the final product could be outdated by the time it is deployed. Market needs or new technology may have already surpassed the capabilities of the system, rendering it less effective, affecting policyholder experience and internal efficiency. The linear progression of a waterfall project means that an error or flaw in an early phase can have a cascading, negative effect on all subsequent phases, jeopardizing the entire project.

Because of these limitations, many workers' compensation organizations adopt more flexible, iterative methodologies, such as Agile, to better adapt to change and more closely align the final product with user needs.

Managing the Transition From Waterfall to Agile

Moving from waterfall to agile in a workers' compensation insurance environment requires a strategic blend of cultural change, process reengineering, and stakeholder alignment. Key success factors include executive sponsorship, clear product vision, product-centric road mapping, role adaptation, stakeholder engagement, integration of compliance into agile workflows, and continuous communication across business and IT.

A successful transition from waterfall to agile methodologies in a regulated insurance environment hinges on strong leadership alignment. Executive sponsorship and governance reform are foundational to enabling agile delivery at scale.

• Securing Executive Sponsorship:

Leadership must visibly champion agile adoption, allocate resources, and model the cultural shift required for iterative delivery.

• Establishing Agile-Supportive Governance Models:

Traditional oversight structures must evolve to support decentralized decision-making, adaptive planning, and continuous feedback loops.

• Readiness Assessment Questions for Leadership:

To evaluate organizational preparedness, the following questions can be posed to leadership teams:

• Does the organization's leadership actively support the adoption of agile practices?
• Is there a clear understanding of the benefits of applying agile methods across departments?
• Are the challenges and costs of implementing agile realistically acknowledged?
• Are project teams empowered to make decisions without constant managerial intervention?
• Does progressive product release add measurable value to customers?
• Are expectations aligned with the realities of agile delivery (e.g., iterative progress vs. full-feature launches)?
• Are team members held accountable for both their deliverables and the broader project goals?
• Does the organization have access to resources with prior agile experience?
• These questions help surface gaps in mindset, structure, and capability—enabling leadership to proactively address barriers and set the stage for a successful transformation.

Product Vision & Road Map

Shifting from waterfall to agile in workers' compensation insurance requires a fundamental shift in how initiatives are conceived, prioritized, and delivered. Central to this evolution is the move from project-based execution to product-centric thinking with an emphasis on outcome over output.

• Shifting From Projects to Products

In legacy environments, work is often organized around discrete projects with fixed scopes, timelines, and budgets. Agile reframes this approach by focusing on long-lived products that evolve continuously to meet user needs. This shift enables teams to prioritize customer value, respond to feedback, and iterate based on real-world outcomes rather than static requirements.

• Creating Adaptive Road Maps

Waterfall is characterized by rigid road maps with planned releases scaling over months, sometimes years. Agile roadmaps are dynamic tools that guide delivery without locking teams into rigid timelines. Instead of detailing every feature upfront, adaptive roadmaps emphasize:

• Business outcomes over output
• Short-term goals aligned with long-term vision
• Flexibility to adjust priorities based on stakeholder feedback, regulatory changes, or market shifts

These road maps foster transparency, encourage collaboration, and ensure that product development remains aligned with strategic objectives—critical in regulated domains like workers' compensation.

Role Evolution

By redefining core roles and investing in capability-building, Workers' compensation insurers can unlock the full potential of agile delivery—empowering teams to deliver value faster, with greater alignment and resilience.

• Redefining Core Roles

This role evolution empowers teams to respond faster to change, deliver incremental value, and maintain alignment with customer and regulatory needs.

o Product Owner

From: Project sponsor or business lead with limited day-to-day involvement

To: Embedded decision-maker responsible for prioritizing the product backlog, defining value, and aligning delivery with business goals

Key Shift: Ownership of outcomes over oversight of tasks

o Scrum Master

From: Project coordinator or team lead focused on timelines and task tracking

To: Servant-leader who facilitates agile ceremonies, removes impediments, and fosters team autonomy

Key Shift: Coaching and enablement over command and control

o Business Analyst

From: Requirements gatherer producing static documentation

To: Collaborative partner who co-creates user stories, refines backlog items, and ensures business needs are continuously understood

Key Shift: Continuous engagement over one-time handoffs

o Development & QA Teams

From: Sequential executors of design and testing phases

To: Cross-functional contributors engaged throughout the sprint cycle

Key Shift: Shared accountability for quality and delivery

o Business SMEs:

From: Waterfall SME that provides upfront requirements, reviews documentation, and signs off at key milestones.

To: Agile SME that actively collaborates with the product team throughout the sprint cycle, shaping user stories, validating features, and ensuring business relevance.

Key Shift: Gatekeeper to Collaborator

• Upskilling Legacy Teams for Agile success

Legacy teams must be equipped with the mindset and tools to thrive in agile environments. Key upskilling strategies include:

• Agile Training & Certification

Formal learning paths (e.g., Certified Scrum Product Owner, SAFe Agilist) help establish foundational knowledge.

• Role-Based Coaching

On-the-job mentoring tailored to new responsibilities accelerates adoption and confidence.

• Cross-Functional Exposure

Encouraging collaboration across disciplines builds empathy, shared ownership, and delivery agility.

• Feedback-Driven Learning

Retrospectives and peer reviews create a culture of continuous improvement and accountability.

Change Management & Communication

• Managing cultural resistance

o Acknowledge legacy mindsets: Waterfall teams often value predictability and control. Recognize their concerns about agile's iterative nature.

o Offer role clarity: Redefine responsibilities (e.g., BA to Product Owner, PM to Scrum Master) to reduce ambiguity.

• Transparent communication and stakeholder engagement

o Set a clear transformation vision: Articulate why agile is being adopted—tie it to business outcomes like speed-to-market, adaptability, and customer centricity.

o Visualize progress: Share transformation roadmaps, sprint metrics, and feedback loops to build trust.

o Feedback-driven adaptation: Regularly solicit input and adjust transformation plans accordingly.

Execution & Delivery

• Sprint planning, backlog grooming, and release cadence

o Sprint Planning: Align cross-functional teams around prioritized user stories, capacity, and sprint goals. Use velocity metrics to forecast delivery.

o Backlog Grooming: Conduct regular refinement sessions to clarify acceptance criteria, decompose epics, and ensure stories are INVEST-compliant (Independent, Negotiable, Valuable, Estimable, Small, Testable).

o Release Cadence: Shift from monolithic releases to incremental delivery—e.g., biweekly sprints with quarterly production drops. Use feature toggles to decouple deployment from release.

• Integrating compliance, QA, and UAT into Agile workflows

o Compliance: Embed regulatory checkpoints into the Definition of Done. Use traceability matrices and audit logs within agile tools to satisfy insurance governance.

o Quality Assurance: Shift left with automated unit, integration, and regression testing. Include QA in sprint ceremonies and pair testing with developers.

o User Acceptance Testing (UAT): Replace end-of-cycle UAT with rolling demos and stakeholder reviews. Use personas and business scenarios to validate functionality iteratively.

• Leveraging tools for visibility and collaboration (e.g., Jira, Confluence)

Centralize backlog, sprint boards, and reporting. Use custom workflows for compliance and defect triage. Maintain living documentation—user stories, design decisions, retrospectives, and compliance artifacts. Use burndown charts, velocity reports, and release tracking to drive transparency.

Metrics That Matter

• Velocity, cycle time, defect rates, and customer satisfaction

o Velocity: Story points completed per sprint. Helps forecast capacity and stabilize planning.

o Cycle Time: Time from work start to completion. Shorter cycle times indicate leaner flow and faster value delivery.

o Defect Rates: Track production defects, escaped defects, and test coverage. Use severity-weighted scoring for regulatory impact.

o Burndown/Burnup Charts: Visualize progress against sprint goals and release scope.

o Time to Value: Measures how quickly new features deliver measurable business outcomes (e.g., faster claims intake, improved underwriting accuracy).

• Establishing KPIs that reflect both delivery health and business impact

• Blend delivery health with strategic outcomes:
• KPI Category Sample KPIs
• Delivery Health Sprint predictability, defect leakage rate, test automation coverage
• Business Impact Policyholder satisfaction, claim cycle time reduction, digital adoption rate
• Compliance & Risk Audit traceability, regulatory defect closure time
• Team Maturity Agile ceremony adherence, cross-functional participation, backlog hygiene

Conclusion

Reengineering product delivery from legacy waterfall to lean agile is far more than a methodology swap—it represents a profound cultural and strategic evolution. In the context of state-run workers' compensation insurers, this transformation is especially consequential. These organizations operate under heightened regulatory scrutiny, serve vulnerable populations, and often manage aging technology ecosystems. Moving to agile unlocks the ability to respond faster to legislative changes, deliver policyholder-centric enhancements, and foster cross-functional collaboration.

Agile practices—when thoughtfully adapted to the compliance-heavy insurance domain—enable iterative delivery, transparent stakeholder engagement, and continuous improvement. Teams become more empowered, feedback loops shorten, and systems grow more resilient. Most importantly, the transformation enhances the insurer's ability to serve injured workers and employers with empathy, speed, and precision.

This journey demands intentional change management, robust metrics, and executive sponsorship. But for those who commit, the payoff is clear: a modernized delivery engine that aligns with both business imperatives and human impact.

References & Sources:

1. UST. (2023). From Waterfall to Agile—How an Insurance Giant's QA Transformation Accelerated Delivery & Elevated Quality. Retrieved from https://www.ust.com

2. McKinsey & Company. (2022). Scaling Agility: A New Operating Model for Insurers. Retrieved from https://www.mckinsey.com

3. Agile42. (2021). Insurance Success Story: Enterprise Transition Framework™. Retrieved from https://www.agile42.com

The insurance industry has been circling AI like a curious bystander peering into a shop window: intrigued, hopeful, but hesitant to step in and buy. Money is flowing into AI pilots, proofs of concept, and experiments, yet few insurers can point to meaningful results. Why? Because they're looking in the wrong direction.

The obsession with shiny, peripheral use cases is distracting leaders from where AI can deliver tangible business gains today: underwriting accuracy, faster claims processing, and customer engagement that builds loyalty at scale. Instead of chasing abstract AI visions, insurers need to double down on practical, high-value applications that directly improve efficiency and profitability.

In doing so, they can unlock the kind of sustained performance gains that separate leaders from laggards. McKinsey found that over the past five years, AI leaders in the insurance sector have created 6.1 times the total shareholder returns of those trailing behind.

Missed Opportunities Staring Insurers in the Face

Take claims processing. It remains one of the most manual, error-prone, and expensive parts of the insurance value chain. While some carriers have experimented with chatbots, the real leap comes from using AI-driven document processing and natural language models to analyze claims documents, detect fraud, and trigger near-instant payouts. Zurich's use of ClaimsX, which uses publicly available data for real-time claims assessments, is a clear example of this in action. Yet many insurers still rely on siloed, human-heavy processes that delay settlements and frustrate customers — perhaps not surprising, given that fewer than half have taken meaningful steps to integrate AI across core functions, according to KPMG.

Underwriting is another underexplored goldmine. Dynamic, AI-enabled underwriting models can use health data, wearables, and behavioral insights to set fairer, more personalized premiums. Nedbank Insurance's funeral policy, which adjusts coverage based on partial premium payments instead of outright cancellation, shows how flexible AI-based underwriting can strengthen customer retention and profitability simultaneously. Still, some insurers cling to rigid rules and outdated actuarial models.

Then, there's customer engagement. Most customers don't want to call an agent or visit a branch; they want clear answers, instantly. AI-powered virtual agents, trained on vast troves of customer interactions, can now handle routine queries, claims updates, and policy recommendations with human-like fluency. Companies like Ethos Life are already deploying chatbots that ease buying and servicing, but this remains the exception rather than the norm.

Why Insurers Keep Missing the Point

Why aren't these obvious opportunities scaling? The barriers are depressingly familiar: legacy systems, fragmented data, and AI projects being treated as "IT experiments" rather than strategic business priorities. Too often, CIOs chase broad AI roadmaps without tying them to the insurer's growth strategy or customer value. The result? AI stuck in pilot purgatory.

The industry's fixation on futuristic models like embedded insurance or speculative AI-powered products, while exciting, is diverting focus from fixing the foundations. Without streamlined underwriting, efficient claims, and responsive service, even the most innovative product ideas will fall flat.

A Smarter Path Forward

Insurance leaders need to reframe their AI agenda from "what's possible" to "what matters now." Here are practical steps to make that shift:

• Start with process groups, not isolated tasks. Automating only one step — say, claims registration — simply creates bottlenecks downstream. Automate the entire claims intake-to-validation flow to see measurable improvements in turnaround time and customer satisfaction.
• Invest in data readiness. AI is only as good as the data feeding it. That means provisioning high-quality structured and unstructured data, harmonizing it across legacy systems, and setting up robust data governance. Think of this as the fuel that powers underwriting precision and claims accuracy.
• Deploy agentic AI for operations. Instead of static bots, use AI agents that can orchestrate multistep workflows across policy servicing, fraud checks, or compliance reporting. These agents don't just respond; they act, making operations faster and more adaptive.
• Tie AI use cases directly to revenue and retention. A chatbot experiment might look modern, but unless it reduces call center costs or improves cross-sell rates, it's wasted effort. CIOs should partner with business leaders to pick use cases that have measurable business outcomes like higher premium conversions, faster claims closures, or lower churn.
• Monitor and adapt continuously. AI models drift, customer behaviors shift, and regulations evolve. Establish practices to measure AI's effectiveness in production and make course corrections before the business impact erodes.

The Payoff: Efficiency That Fuels Growth

The real story isn't about AI as a futuristic add-on to insurance. It's about AI as a force multiplier for the basics: assessing risk, handling claims, serving customers. Get that right, and growth follows naturally through reduced costs, improved customer trust, and faster product innovation.

Insurers that stop chasing shadows and start applying AI where it matters most will not just improve efficiency; they'll reset the industry's playbook for profitable growth. The question is simple: Will your organization keep watching from the sidelines, or will you step onto the field where the real gains are waiting?

Insurance claims processing often feels like organized mayhem. Piles of documents demand attention, while phone lines buzz with frustrated callers. Decisions stretch out over days or weeks, draining your resources and testing your patience.

But agentic AI steps in as a game-changer, acting like a skilled ally equipped with smart tools. It helps you shift from disorder to effortless efficiency. Agentic AI in insurance claims paves the way for quicker settlements and satisfied policyholders, while freeing your teams to tackle complex issues.

Let's explore how agentic AI redefines claims handling in insurance.

Understanding Agentic AI in Insurance Claims: Beyond Traditional Bots

Agentic AI stands apart from conventional automation because it doesn't follow rigid scripts. Instead, it sets goals and navigates toward them dynamically. Rooted in advanced large language models, it integrates reinforcement learning to refine actions based on outcomes. Frameworks like LangChain enable sequential reasoning, while AutoGen supports collaborative multi-agent setups. These systems adapt in real time, handling uncertainties that stump older AI.

In the insurance realm, traditional bots might flag a claim for review. Agentic versions investigate further by pulling data, analyzing patterns, and deciding next steps. They evolve through interactions, learning from vast datasets to improve accuracy. Instead of one-size-fits-all responses, agentic AI in claims management offers tailored intelligence that anticipates needs.

Consider the building blocks:

• Core Components: Large language models process natural language, while tools like application programming interfaces (APIs) fetch external information.
• Learning Mechanisms: Reinforcement learning rewards successful outcomes, sharpening decision-making over time.
• Adaptability Features: Agents replan if obstacles arise, ensuring resilience in unpredictable scenarios.

This foundation empowers agentic AI to thrive where static systems falter, making it ideal for the fluid world of claims.

The Challenges in Traditional Claims Processing

Claims handling has long been a bottleneck in insurance. The process kicks off with the first notice of loss, flooding in via calls, emails, or apps. Human teams then verify details, collect evidence, and assess validity. But inefficiencies abound. Delays from manual reviews cost time and money. McKinsey estimates weeks-long cycles that bleed billions annually in lost productivity.

Unstructured data compounds the issue. Think fuzzy images of accident scenes or verbose incident descriptions. Fraud slips through undetected, inflating losses. Meanwhile, adjusters juggle high volumes, leading to burnout and errors. When it comes to exceptional cases like claims for unique items, they can grind everything to a halt.

Key pain points include:

• Data Overload: Sifting through varied formats slows verification.
• Human Dependency: Reliance on manual input invites inconsistencies.
• Scalability Issues: Peak seasons overwhelm resources, extending wait times.
• Fraud Vulnerabilities: Subtle anomalies go unnoticed without advanced scrutiny.

These hurdles not only frustrate customers but also erode trust, highlighting the urgent need for smarter solutions.

How Agentic AI Transforms Claims Handling: A Step-by-Step Guide

Agentic AI in claims management takes charge right from the initial report. It processes incoming data with precision, using natural language processing to decode conversations or texts. Fine-tuned models like BERT extract essentials: incident specifics, damage extent, and emotional cues for prioritization.

Document checks follow seamlessly. Computer vision analyzes uploads, comparing against policy records via integrated APIs. Anomaly detection builds graphs to spot irregularities, flagging potential fraud early.

Orchestration ties it all together. State machines guide workflows, pulling in IoT readings or external validations. Reinforcement learning optimizes simulations for payout decisions. Tools such as Orkes Conductor ensure fluid integration, routing complex cases to humans when needed.

Breaking it down further:

• Intake Phase: NLP parses inputs, categorizing and prioritizing claims instantly.
• Verification Stage: Vision tech and APIs confirm authenticity, reducing manual reviews.
• Analysis and Decision: Agents simulate outcomes, approving straightforward cases in minutes.
• Escalation and Learning: Unresolved items flag for oversight, with feedback loops enhancing future performance.

This structured approach cuts processing from days to moments, streamlining operations without sacrificing thoroughness.

Real-World Success Stories: Agentic AI in Action

Insurers are already reaping benefits from agentic AI deployments. Cognizant has tested systems that tackle unusual claims efficiently, matching the speed of routine ones. McKinsey's initiatives show generative AI automating processes, enhancing fraud detection, and generating substantial savings.

Salesforce enables end-to-end automation, from intake to disbursement, with real-time verifications. In one EY project with a Nordic firm, AI handled unstructured data extraction at 70% accuracy, allowing staff to focus on engagement. Initial trials report productivity boosts of up to 65%.

These examples illustrate tangible impacts:

• Efficiency Gains: Reduced handling times lead to faster resolutions.
• Cost Reductions: Automation lowers operational expenses significantly.
• Improved Accuracy: Advanced detection minimizes errors and fraud.
• Customer Satisfaction: Quick responses build loyalty and trust.

Such stories underscore agentic AI's practical value, turning theoretical promise into proven results.

Addressing the Challenges: Ethics, Biases, and Implementation Hurdles

Agentic AI brings power, but not without pitfalls. Training data biases can lead to unfair outcomes, putting certain groups at a disadvantage. However, we can mitigate this risk with hybrid approaches that blend AI with human review.

Regulations like GDPR require clear accountability. Privacy safeguards including robust encryption are also critical as they protect sensitive information.

Agentic AI implementation isn't always going to be seamless. While legacy systems resist integration, initial investments can sting. As such, gradual adoption, starting with pilots, eases the transition.

Consider these strategies:

• Bias Mitigation: Diverse datasets and regular audits ensure equity.
• Compliance Measures: Built-in logging and transparency features meet legal standards.
• Rollout Tactics: Phased introductions minimize disruption.
• Continuing Monitoring: Continuous evaluations adapt to emerging issues.

By tackling these head-on, insurers can harness agentic AI responsibly, balancing innovation with integrity.

Looking Ahead: The Future of Agentic AI in Insurance Claims

The evolution of agentic AI promises even greater advancements. Multi-agent collaborations could manage entire ecosystems, incorporating IoT for live risk assessments. We might even leverage enhanced modeling to predict catastrophes more accurately and reshape our response strategies.

Future trends include:

• Integrated Ecosystems: Seamless connections with external data sources for proactive handling.
• Predictive Capabilities: Advanced simulations forecasting claim trends.
• Scalable Solutions: Swarm intelligence for high-volume scenarios.
• Ethical Evolutions: Stronger frameworks for fair, transparent AI use.

Agentic AI is gaining a lot of momentum, evolving into a cornerstone of modern insurance, and paving the way for resilient, efficient operations.

Beyond just offering tools, it serves as a pathway to intelligent claims automation.

Everyone in the cyber insurance market can see it: Rates are declining, coverage is expanding, and carriers are aggressively competing for new business. However, the underlying market dynamics reveal a troubling reality: These conditions are fundamentally unsustainable and signal an approaching period of substantial market correction.

Through decades of market observation, I've witnessed how today's competitive pricing environment consistently leads to tomorrow's coverage restrictions and dramatic rate increases. Insurance companies are compromising established underwriting principles, pricing coverage below actuarial requirements, and expanding risk exposure beyond their capacity to support.

The economic fundamentals are clear: Inadequate pricing today necessitates severe rate corrections in the future. When this inevitable adjustment occurs, insureds that prioritized short-term premium savings over sustainable partnerships will face restricted coverage options, reduced capacity, and rate increases that far exceed any temporary savings.

This market environment represents more than cyclical volatility—it's a systematic trap that becomes more problematic the longer these conditions persist. Understanding these dynamics is essential for strategic risk management planning.

The capital abundance paradox

Substantial capital continues to flow into the insurance market, creating what appears to be abundant capacity. While this might seem advantageous for buyers, I've learned through experience that excess capital often leads to problematic outcomes.

When insurers have significant capital reserves, they frequently become less disciplined in their risk assessment and pricing. Competition intensifies as carriers seek to deploy this capital, often resulting in terms and pricing that aren't actuarially sound. The fluid cyber landscape is challenging for actuaries to assess as the decades-long lookback is in its infancy when it comes to trends and loss development, unlike other insurance products–such as D&O–that offer decades of rich data. This lack of historical data creates an environment where short-term competitive advantages come at the expense of long-term market stability.

The concern is that buyers may become accustomed to these favorable conditions, not realizing they're unsustainable. When market corrections occur—and they inevitably do in these rate environments—the adjustment can be severe.

Misguided competitive strategies

Another big concern I have is the way in which carriers are choosing to differentiate themselves in this environment. Rather than competing on service excellence, claims handling expertise, or risk management capabilities, many insurers are resorting to tactics that compromise the fundamental integrity of coverage.

I'm seeing carriers choose to eliminate exclusionary language and expand coverage limits beyond what their pricing models support. While these modifications may help win new business, they create significant short- and long-term risks. When insurers compete by assuming risks they haven't properly evaluated or priced, they're effectively compromising the financial security provided by way of healthy reserves.

This approach may generate short-term growth, but it fundamentally undermines the insurance fundamentals and takes a gamble on the integrity of risk transfer, potentially passing on more risk to reinsurance treaties or forcing the insurer to siphon reserves from alternative capital.

Coverage creep

We're experiencing significant coverage expansion across multiple areas that historically required separate policies or weren't covered at all. This is driven by other lines of commercial insurance removing risk from their products of any possible cyber exposure, resulting in coverage creep into the cyber liability policy. Pollution liability, bodily injury exposures, property damage coverage, and various media risks are increasingly being included in standard policies without corresponding adjustments to premium or risk assessment processes.

While expanded coverage may appear beneficial, this trend often reflects competitive pressure rather than sound practice. When coverage broadens without proper underwriting justification and pricing support, it creates latent liabilities that may only surface during claims, potentially leaving policyholders with less protection than they anticipated.

This is particularly troubling because it gives buyers a false sense of comprehensive coverage while potentially compromising the insurer's ability to respond adequately when claims occur.

Loss ratios are deteriorating

The rate change data reveals the extent of current market pressures. When rates decline or remain flat despite increasing exposure values and evolving risk landscapes, it indicates that competitive forces are overriding actuarial principles.

This pricing environment creates deteriorating loss ratios across the industry. When insurers price coverage inadequately to maintain or grow market share, they're essentially creating future financial obligations that may exceed their ability to fulfill them. The mathematics are straightforward: inadequate pricing today necessitates dramatic rate corrections tomorrow. This has been the unwavering history of market cycles.

From my perspective, this represents one of the most concerning aspects of the current market cycle, as it sets up both insurers and policyholders for significant challenges when the inevitable correction occurs. As vast growth in the cyber insurance market has been anticipated over the coming years, it is incumbent upon the market to ensure capacity can meet the anticipated need. If current economics persist, we will see curtailment of capacity and coverage capability, introducing a hardening market.

Understanding true value in insurance

There's a crucial distinction between price and value that many buyers overlook in the current environment. True value in insurance relationships stems from working with financially stable insurers that demonstrate consistent claims handling capabilities, provide meaningful risk management support, and offer coverage that's properly structured to respond when needed.

It is also advantageous for buyers to partner with insurers that can adjust the rate to the risk to garner visibility into their risk exposure.

An insurer's financial stability, claims handling efficiency, and genuine expertise in specific risk areas far outweigh a race to the bottom for premium savings. Buyers need to know, now more than ever, that it's not words on paper being purchased, it's expertise, right-fitted coverage, claims handling, and the "tiger team" there with you in an organization's "worst hour" that brings value. The relationship with your insurance partner matters significantly more than many buyers realize. Collaboration from a risk management standpoint, and the powerful ways that cyber maturity posture can be shaped by working with holistic cyber solutions, set up policyholders for long-term insurability.

This is especially important in the current market, where some carriers are essentially buying business with unsustainable pricing. When the inevitable corrections occur, buyers who prioritized relationships with financially sound, well-managed insurers will find themselves in a much stronger position.

What happens when the market changes?

The current soft market conditions are fundamentally unsustainable. Historical patterns demonstrate that cycles of inadequate pricing and aggressive competition eventually self-correct, typically with significant market disruption. Understanding this cycle is essential for strategic planning.

Forward-thinking risk managers and insurance buyers should use this period to establish relationships with carriers and brokers who maintain disciplined underwriting approaches. Rather than focusing solely on current pricing advantages, buyers should evaluate potential partners based on their long-term stability, financial strength, and demonstrated expertise in relevant risk areas.

The insurance industry's cyclical nature means today's favorable conditions will eventually give way to more restrictive markets. Organizations that prioritize sustainable partnerships over short-term savings will be better positioned to navigate the challenges ahead. The question isn't whether market conditions will change—it's whether organizations will be prepared when they do.