Independent agencies are always looking ahead, but they're still focused on how much work it takes to place and service a policy today. Underwriting remains tight, carrier appetites change, and remarketing eats up time and resources. At the same time, clients expect clear, timely communication and fewer surprises.

In a recent Vertafore survey, independent agencies told us what will shape their operations in 2026. Three trends emerged:

• Using AI automation to reduce manual work
• Preparing for uncertainty in carrier appetite and placements across admitted and E&S markets
• Prioritizing proactive, consistent client communication as a defining trait of high-performing agencies.

What connects these trends? Operational efficiency supported by the right technology.

High-performing agencies aren't trying to predict every market shift, and they're not just adding headcount to keep up with more work. Instead, they're focused on how the work gets done. The agencies that win in 2026 are using technology designed for their workflows to reduce manual effort, adapt quickly to appetite shifts, and communicate consistently with clients.

Using AI to reduce manual work in insurance agency workflows

Carriers always ask for more information, pose follow-up questions, and make adjustments as requirements change. When agencies run that work through manual steps and disconnected systems, it quickly turns into duplicate entry, rework, and resubmitting applications.

To manage this workload, agencies are turning to more AI automation because they can't scale this work by hiring alone. In the survey, nearly 30% said they expect AI-driven process improvements to deliver the strongest return on investment in 2026, and more than one-third said the greatest value will come from AI embedded into the solutions they already use.

Today, agencies are successfully using AI-powered solutions to pull data from ACORD forms and carrier documents so the information can be reviewed instead of re-entered. AI supports everyday service work, validating information before it reaches service or sales, and even determining whether it makes sense to move forward before spending time investing in a full rating.

Over time, these improvements will expand further. AI takes on more of the manual work—redundant clicks, document routing, and non-licensed processes that consume time. This automation works in the background so teams can focus on licensed work and client conversations.

Managing carrier appetite shifts and E&S placements efficiently

Carrier appetite changes are expensive when agencies aren't structured to adapt. Every shift means rebuilding submissions, re-entering information, and spending more time redoing work that was already completed.

In the survey, nearly half of agencies said they expect to place about the same amount of business in the E&S market this year, and 40% expect to place more than they did in 2025. That tells us E&S isn't going away. Agencies are already seeing movement in both directions—some accounts shifting back to admitted markets as appetite expands, and others moving into E&S as underwriting tightens.

When an agency uses systems that are not integrated, a carrier decline can mean significant manual work—re-entering data, rebuilding documents, and recreating submissions from scratch. In the E&S market, where supplemental applications often appear late in the process, this friction adds up fast.

To manage these changing processes, agencies are using submission and application management tools to collect and organize risk information once by integrating with their agency management system. They pair these tools with benchmarking solutions to understand which carriers or MGAs will be most likely to write the risk, then carry the information out to the market without someone having to re-enter it. Solutions that talk to each other keep the work moving and teams don't have to rebuild the same submission over and over.

Why consistent client communication separates high-performing agencies

After years of pricing pressure, underwriting changes, and continuing remarketing, clients rely on access to their agents more than ever. They want to talk about coverage shifts and rate increases before they happen. When they have to call and ask why something's changed, it's often too late—they're already shopping.

This emphasis on consistent communication showed up in the survey. More than half of respondents agreed that providing proactive, timely communication will set high-performing agencies apart in 2026.

But for busy agencies, it's difficult to provide a higher level of outreach. Juggling renewals, remarketing risk, and doing the daily work that keeps the doors open keeps teams from picking up the phone or sending an email. When delivery depends on individual effort, important messages become reactive and clients get frustrated.

That's why using a marketing automation tool for communications is so important. These solutions can either be integrated within your existing management system or built into your CRM or AMS tool. Agencies that use these tools turn routine messaging like renewal updates, document readiness, and general market info over to technology, and use the time savings to have deeper conversations about more important issues. Automating second-tier communication means clients stay informed and agency professionals aren't spending time manually writing and sending emails.

Using this technology also means clients receive intentional touchpoints that show a higher level of attention and care. Agencies can automatically send focused reminders to homeowners about fire season, educational materials about important topics, or alerts about upcoming trends. Templated messages go out on time and people step in where judgment and empathy are required.

It may feel counterintuitive, but automating routine communication often improves satisfaction. Clients feel informed and have clearer expectations. Teams aren't overwhelmed trying to reach everyone at once. With the right solutions carrying some of the load, agencies can elevate their clients' experience and reduce workloads.

Operational efficiency matters for independent agencies in 2026

Across the three themes—AI-driven workflows, E&S readiness, and automating client communication—the common thread is operational efficiency.

The agencies that will perform best in 2026 won't predict every market move. They will invest in how work gets done—making sure their systems talk to each other, their processes are disciplined, and their teams aren't buried in manual tasks.

It's not really a technology story. It's about how agencies manage their business day to day. And the agencies that focus on that now are going to be in a much better position in 2026 and beyond.

Many insurance companies struggle to articulate and operationalize a precise appetite for risk. When underwriting guidelines lack clarity, producers and agents lack context for submission decisions. As a result, misaligned risks crowd pipelines and slow quoting timelines, reducing overall productivity. With competitive pressures rising across property and casualty (P&C) lines, improving the precision of risk intake is becoming essential.

Insurers embedding artificial intelligence (AI) into core functions, like underwriting and intake, can realize efficiency gains of more than 30%, primarily through reduced manual workload and better decision flows, according to Boston Consulting Group (BCG) research.

Why Traditional Appetite Communication Falls Short

Like it or not, communicating appetite through static documents, such as PDFs, spreadsheets, or email blasts, is still the norm. These formats are often misplaced, degrade quickly, and offer little real-time clarity. Agents often submit risks with incomplete information or insight into what aligns with underwriting goals, and underwriting teams then spend valuable time reviewing misaligned leads.

Further, while 88% of insurers use AI in at least one business function, few have scaled predictive decision-making tools enterprise-wide, according to a 2025 McKinsey & Company survey. This gap between experimentation and enterprise adoption presents an opportunity for first movers to gain a strategic edge.

The Rise of Predictive Appetite Scoring

Predictive appetite models bring intelligence to the earliest point of engagement by scoring submissions at pipeline entry. These models evaluate internal guidelines, performance metrics, and third-party data to calculate appetite fit and route each lead accordingly.

Instead of relying on static underwriting rules, predictive appetite scores interpret real-time risk context to determine which submissions align with portfolio goals. High-fit leads move straight to quoting or prioritized review while others are flagged for additional enrichment, redirected, or held back from underwriter queues altogether. Industry best practices followed by many insurers dictate the use of clean, normalized third-party data on-demand for pre-fill. Typically, third-party data is sourced for risk classifications (like NAICS), revenue, headcount, years in business, and more.

This shift enhances decision accuracy while minimizing effort on low-potential submissions.

Embedding Appetite into Distribution Workflows

When appetite scores appear directly in agency portals or APIs, agents are guided toward an insurer's desired result during the quoting process. In that scenario, submissions become more aligned, quote quality improves, and back-and-forth clarification drops. For the existing or potential policyholder, the process becomes faster and more transparent.

This embedded logic supports producers while reinforcing underwriting discipline without changing the core infrastructure.

The Underwriting Efficiency Advantage

Predictive appetite enables strategic triage. Underwriters spend more time on viable opportunities and less on sorting through noise. When intake is clean and appetite is operationalized, quote-to-bind ratios increase and loss ratios stabilize.

It's not theoretical. BCG reports that predictive models reduce acquisition waste and improve conversion rates across insurers actively using them in distribution and underwriting.

The Future of Appetite as Intelligence

Predictive appetite is the foundation for broader transformation. Insurers that build scoring into submission pipelines today will soon use those models to steer portfolios, enhance pricing, and select distribution partners more effectively.

Over the next 12 to 24 months, expect a widening gap between insurers using appetite scoring to optimize workflows and those still relying on static documentation. Appetite scoring will become central to broader underwriting transformation, informing everything from risk selection to strategic planning.

A Strategic Imperative

Insurers will increasingly turn to predictive appetite modeling to make smarter decisions earlier in the pipeline, reducing manual work and improving distribution alignment without needing to replace core systems.

For insurance companies looking to operationalize predictive appetite, there are some critical elements to add to the roadmap, including:

• Appointing a senior champion: A CIO, CTO, or chief underwriting officer (someone with actual decision-making power) should sponsor the initiative.
• Assembling a cross-functional team: Give underwriting, data science, product, and distribution stakeholders equal input when it is time to define appetite criteria and success metrics.
• Starting modular: Use existing submission workflows and enrich them with third-party data and scoring logic.
• Measuring and iterating: Use real-time feedback loops to refine models and improve appetite alignment over time.

Done well, predictive appetite modeling strengthens broker relationships, protects margins, and unlocks smarter growth. At the end of the day, it's about quoting faster (and more accurately) by building intelligent underwriting engines that learn and adapt over time.

The catastrophe insurance crisis facing American municipalities isn't a crisis of risk — it's a crisis of imagination.

When the insurance industry tells municipal leaders to "invest in resilience" without developing insurance products that reward those investments with affordable coverage, they're asking communities to subsidize the industry's own innovation deficit. When carriers collect premiums from communities while simultaneously withdrawing coverage and providing no recourse to close protection gaps, they leave municipalities to bear the burden of adaptation alone. And when carriers pull away from vulnerable communities while posting record profits, their risk decisions become a way of abdicating their core function of pooling and transferring risk.

This mismatch is unsustainable and dangerous, and the consequences cascade far beyond equity concerns. Low-income residents can't rebuild after disaster. Without business interruption coverage, small businesses close, devastating local employment and sales tax revenue. Affordable housing providers, facing uninsurable properties, stop developing in climate-vulnerable areas, exacerbating housing shortages and displacement. These "equity issues" then become systemic economic failures that threaten the tax base carriers rely upon for commercial lines business.

If carriers don't evolve, they won't just be disrupted by climate change; they'll be made irrelevant by it. To meet the needs of climate-challenged jurisdictions and address these cascading failures, carriers must dramatically increase their R&D investment across product, distribution, and capital structure.

Product Innovation

The catastrophe insurance products available to municipalities today are fundamentally inadequate for current climate realities, let alone future projections. Carriers must invest in genuine product R&D across several critical areas.

Microinsurance for Vulnerable Populations: Carriers should develop lower-limit, lower-premium products specifically designed for low-income households and small businesses. Communities with functioning insurance markets maintain economic activity after disasters while communities without insurance face cascading failures that destroy the commercial premium base.

Advanced Modeling That Provides Incentives for Novel Adaptation: Current catastrophe models treat risk as static or worsening, with no meaningful premium reduction for innovative resilience projects. Carriers must bolster their modeling capabilities, so they can actually quantify the risk reduction from novel adaptation investments. This requires fundamental R&D investment in coupled physical-financial modeling, not incremental improvements to existing cat models.

Parametric Insurance for Rapid Response: Traditional indemnity insurance is ill-suited for disaster response, but parametric products that trigger immediate payouts based on objective measurements (flood depth, wind speed, earthquake magnitude) enable rapid recovery while reducing time and costs. The question is whether carriers will invest in developing parametric products tailored to municipal needs or whether municipalities will turn to specialized parametric providers who will.

Distribution Innovation

Even when adequate insurance products exist, traditional distribution channels systemically fail to reach those who need coverage most. Insurance carriers must invest in new distribution models, particularly embedded insurance mechanisms that integrate coverage directly into existing community touchpoints.

Affordable Housing: Catastrophe coverage can be embedded directly into affordable housing and stay with the property, not the individual tenant or owner. This closes protection gaps for vulnerable residents while creating stable, pooled risk for carriers.

Utility Bill: Municipalities can embed catastrophe coverage directly into customers' utility bills, creating universal protection while drastically reducing acquisition costs and adverse selection.

Employee Benefit: Municipalities and anchor institutions (hospitals, universities) employ thousands of workers, many of whom lack adequate catastrophe coverage. Embedding basic catastrophe protection in employee benefit packages closes protection gaps while creating group purchasing efficiency.

Small Business District: Local business districts and chambers of commerce can serve as aggregators for embedded small business catastrophe coverage. Small businesses often lack business interruption and property coverage. District-level embedded insurance solves the distribution problem while enabling risk pooling across merchants.

Capital Structure

Perhaps the most fundamental innovation required is at the capital structure level. Traditional reinsurance capital is designed to maximize returns for institutional investors and reduce exposure to complex, small-scale risks that don't fit standardized cat bond structures.

Community Development Reinsurance Institutions (CDRIs) — mission-driven nonprofit reinsurers structured similarly to community development banks — offer an alternative capital model specifically designed to support municipal resilience and insurance market function.

Traditional carriers should see CDRIs not as competitors but as catalysts for market development. By providing reinsurance for products serving underserved markets, CDRIs enable primary carriers to write business they otherwise couldn't while maintaining risk tolerance within board-approved limits. This expands the overall insurance market rather than displacing existing business.

Yet most carriers remain unaware of or unengaged with the emerging CDRI sector. If carriers don't invest in understanding, partnering with, and leveraging mission-driven reinsurance capacity, they'll soon discover that CDRIs have enabled an entirely new ecosystem of insurance providers serving municipalities—providers who didn't need traditional carrier participation to succeed.

The Choice Before Carriers: Lead, Follow, or Become Obsolete

The insurance industry faces a stark choice: invest in the R&D necessary to develop products, distribution channels, and capital structures adequate to municipal climate realities or continue business as usual and risk becoming obsolete.

The communities that carriers are abandoning won't simply accept uninsurability. They'll build alternatives — self-insurance pools, parametric coverage through specialized providers, embedded insurance through MGAs, risk financing through CDRIs and innovative bonds that will chip away at carriers' market relevance. Eventually, the "alternative" insurance ecosystem serving municipalities will become the mainstream, and traditional carriers will lose out on large parts of the market.

This isn't speculation; it's already happening. The only question is whether the insurance industry will rise to meet the demands and challenges in the municipal markets.

The insurance industry in 2026 is no longer just policy-driven — it is data-driven. From underwriting and claims processing to fraud detection and customer personalization, data services in the insurance industry are redefining how insurers operate, compete, and innovate.

As insurers face rising customer expectations, regulatory complexity, climate-related risks, and digital disruption, robust insurance data services have become the backbone of sustainable growth and operational excellence.

The Evolution of Data Services in Insurance

Historically, insurers relied on legacy systems and siloed databases. Data was fragmented across underwriting, billing, claims, and customer service departments. Decision-making was often reactive rather than predictive.

In 2026, modern insurers can leverage:

• Cloud-native data platforms
• Real-time data processing
• Advanced insurance data analytics
• AI and machine learning models
• Integrated enterprise data ecosystems

Today's data services in insurance focus not only on storing information but also on transforming raw data into actionable intelligence.

Key Components of Insurance Data Services in 2026

1. Data Management and Governance

Strong data governance in insurance ensures accuracy, compliance, and security. With increasing global regulations and privacy standards, insurers must:

• Maintain clean, validated datasets
• Implement structured data governance frameworks
• Ensure secure storage and access control
• Meet regulatory compliance requirements

Effective data management reduces risk exposure and strengthens reporting capabilities.

2. Insurance Data Analytics and Predictive Modeling

Predictive analytics has become central to underwriting and risk assessment. Using historical and behavioral data, insurers can:

• Assess risk with greater precision
• Improve pricing accuracy
• Identify high-risk policies earlier
• Forecast claim likelihood

Predictive analytics in insurance enables proactive risk management rather than reactive claim handling.

In 2026, AI-powered models also enhance fraud detection by identifying anomalies in real time — reducing losses and improving profitability.

3. AI and Machine Learning in Insurance Data Services

Artificial intelligence (AI) is deeply embedded in modern insurance data services. Applications include:

• Automated underwriting decisions
• Claims triage and prioritization
• Customer sentiment analysis
• Intelligent chatbots powered by real-time data
• Personalized product recommendations

By leveraging AI in insurance, carriers reduce operational costs while improving accuracy and customer satisfaction.

Machine learning models continuously learn from new datasets, making systems smarter and more efficient over time.

4. Cloud Data Platforms and Scalable Infrastructure

The migration to cloud-based ecosystems has transformed data management in insurance. Cloud platforms offer:

• Scalable data storage
• Real-time analytics capabilities
• Enhanced disaster recovery
• Faster deployment of new tools
• Improved integration across systems

Cloud-enabled insurtech data solutions empower insurers to launch products faster and respond to market shifts dynamically.

In 2026, hybrid and multi-cloud strategies are common, ensuring resilience and flexibility across global operations.

How Data Services Improve the Insurance Value Chain

Underwriting Excellence

Advanced data analytics improves risk segmentation and pricing models. Insurers can incorporate alternative data sources such as IoT devices, telematics, and behavioral insights to refine underwriting accuracy.

Faster Claims Processing

Data automation reduces manual intervention, shortens claim cycle times, and enhances transparency for policyholders.

Fraud Prevention

AI-powered fraud detection systems analyze patterns across millions of claims, flagging suspicious activities before payouts occur.

Customer Experience Personalization

Using customer data platforms, insurers can deliver tailored communication, policy recommendations, and proactive risk alerts — increasing retention and loyalty.

Challenges in Insurance Data Services

Despite its advantages, implementing modern data services in the insurance industry comes with challenges:

• Data silos across legacy systems
• Inconsistent data quality
• Cybersecurity risks
• Compliance complexities
• Skill shortages in data science and AI

To overcome these obstacles, insurers must invest in strong data architecture, governance policies, and skilled analytics teams.

The Strategic Importance of Data Services in 2026

By 2026, competitive advantage in insurance will depend heavily on data maturity. Insurers that successfully implement comprehensive insurance data analytics solutions will benefit from:

• Reduced loss ratios
• Improved underwriting profitability
• Higher customer satisfaction
• Faster innovation cycles
• Stronger regulatory compliance

Data is no longer a support function — it is a strategic growth driver.

Forward-looking insurers are building centralized data hubs, leveraging AI-driven insights, and integrating real-time analytics into every operational layer.

Future Trends in Insurance Data Services

Looking ahead, several trends will shape data services in the insurance industry:

• Embedded insurance powered by real-time APIs
• Increased use of IoT and telematics data
• Climate risk modeling using advanced analytics
• Blockchain integration for transparent claims processing
• Responsible AI frameworks for ethical data usage

Insurers that prioritize innovation while maintaining data security and compliance will lead the market.

Conclusion

In 2026, data services in the insurance industry are not just about managing information — they are about unlocking intelligence. From predictive analytics and AI automation to cloud-enabled scalability, data-driven strategies are redefining underwriting, claims management, fraud detection, and customer engagement.

Insurance organizations that invest in modern data infrastructure, governance frameworks, and advanced analytics capabilities will gain a decisive edge in an increasingly competitive landscape.

The future of insurance belongs to insurers who turn data into insight — and insight into action.

Insurers are moving quickly to adopt generative AI in the form of chatbots, summarizers, document analyzers, and recommendation tools across underwriting, claims, and servicing.

While these innovations deliver immediate value by automating tasks and improving productivity, most are only isolated features instead of a part of a cohesive intelligence strategy. Insurers risk creating a patchwork of smart tools that don't learn from one another or scale strategically. They just…exist, with the whole never becoming greater than the sum of its parts.

The next wave of competitive advantage won't come from adding more gen AI features. It will depend on a shared, intelligent foundation that spans all core systems and learns across the enterprise. In fact, the Geneva Association now encourages insurers to invest in strong data infrastructure and hybrid architectures if they hope to execute productive AI deployments.

Isolated solutions won't cut it. To unlock sustainable business value, insurers must construct gen AI with broad foundations.

Gen AI Features vs. Foundation

Gen AI features – each one built to solve a narrow issue – are all the rage. These tools, limited in scope, include claims processing tools, underwriting analysis, suggestion engines, and, most notably, customer service chatbots. Indeed, conversational AI chatbots are already widely used to handle routine policyholder interactions, offering real‑time assistance through intelligent chat and voice interfaces.

Yes, these tools are valuable. They're also disconnected.

These systems can execute tasks but cannot communicate with each other – not sharing insights, not learning collectively, not evolving as an "AI suite" greater than the sum of its parts. A gen AI foundation, by contrast, provides a shared intelligence layer that continuously learns from every interaction and use case. This transforms isolated automation into collective intelligence, where each new AI capability strengthens the whole.

The Cross-System Reality: No Hyper-Personalization

Most insurers operate in complex, multi-system environments, guided by multiple policy administration systems (PAS) with decentralized claims, billing, and CRM platforms. Each system contains only a partial perspective, creating dangerous silos across the policy lifecycle that don't empower insurers to offer hyper-personalization or satisfying customer journeys.

A true gen AI foundation does not replace these systems of record. It connects and contextualizes them through a unified intelligence layer. In fact, recent trend analyses emphasize that a unified semantic layer is essential for contextualizing disparate insurance data sources and enabling consistent AI reasoning. The objective is not just data access – it's shared understanding that evolves across systems, functions, and time.

The Brain of Record: Systems to Synapses

Traditional systems of record (SoR) are designed to manage both static data and dynamic data, including real‑time transaction details, plus historical data, to support audits and compliance checks.

Alternatively, a brain of record (as insurance innovation leaders call it) goes a step further by capturing understanding, not just information – integrating structured and unstructured data, maintaining lineage and traceability, and enhancing data with learned relationships and insights.

This living intelligence and memory is constantly evolving, able to categorize insights, learn from interactions, and organize context from individual transactions to enterprise-wide patterns. Metadata plays a foundational role in this foundation layer by tagging every piece of information with its source, purpose, and relationship.

AI can reason using that context and generate its own metadata, identifying new concepts, clusters, and emerging connections across the enterprise. This helps create a seamlessness appreciated by employees and customers.

How to Unify Two Worlds

Application programming interfaces (APIs) provide access, but not context.

These platforms offer only a narrow transactional view of policies, claims, or payments, but cannot connect insights across systems or reason over time. Similarly, traditional data warehouses – optimized for storing and querying structured business data, but not for handling high‑scale, real‑time, AI‑driven workloads – can centralize information, but can't execute intelligence or reasoning.

An overarching AI data foundation unifies both worlds by continuously synchronizing structured and unstructured data. It also expands the function of metadata by identifying new risk categories, linking similar claims, and surfacing emerging relationships. This foundation enables real world scenarios, such as AI-driven underwriting agents that proactively assess renewals, detect emerging risks, and support human decision-making with contextual intelligence.

But building this kind of gen AI foundation is not a plug-in exercise. You can't fake it. It requires robust AI data and context fabric that is dynamic, hybrid, layered, governed and evolving. This evolving fabric should act as the backbone of enterprise intelligence.

Why the Foundation Matters

Insurers are moving from siloed deployments to enterprise‑wide AI platforms, where generative and multimodal models simultaneously improve claims, underwriting, and customer engagement by sharing insights across functions. This shift provides more consistent, real‑time intelligence across the value chain by creating a shared context across underwriting, claims, and servicing departments.

AI data foundations also empower insurers with three huge benefits:

1. Adaptive intelligence: AI systems that get smarter with every interaction
2. Governed trust: every insight is explainable, traceable, and compliant
3. Scalable reuse: each new AI use case strengthens the shared foundation rather than creating another silo

This effectively turns AI from a collection of decentralized tools into an engine of end‑to‑end enterprise intelligence.

Ride the wAIve

The next wave of AI in insurance will be created by the collective depth of organizations' data foundations, not the number of features they host.

Insurers that invest in a brain of record – an evolving intelligence layer that learns, organizes, and grows alongside the business – will unlock lasting competitive advantage. Though complex, this data architecture is very achievable with the right integration strategy and governance expertise.

The insurers that build a strong foundation for their AI today will define the intelligence standards of the industry tomorrow.

Artificial intelligence (AI) is no longer an experimental technology in the insurance sector. By 2026, AI in insurance has become a core driver of underwriting accuracy, claims automation, fraud detection, and customer personalization. Insurers worldwide are leveraging artificial intelligence in insurance, predictive analytics, and machine learning to transform traditional operating models into intelligent digital ecosystems.

However, while AI delivers measurable benefits, it also introduces risks and ethical considerations that insurers must manage carefully.

1. The Impact of AI in Insurance 2026

The impact of AI in insurance extends across the entire value chain — from policy issuance to claims settlement.

In 2026, insurers are using AI-powered systems to:

• Analyze real-time risk data
• Automate underwriting decisions
• Accelerate claims processing
• Detect fraud patterns instantly
• Personalize insurance products

AI-driven platforms process vast amounts of structured and unstructured data in seconds, enabling insurers to make faster and more informed decisions. The shift from reactive to predictive operations has significantly improved operational efficiency and customer satisfaction.

2. Advantages of AI in Insurance

The adoption of AI insurance solutions brings multiple strategic advantages.

Improved Underwriting Accuracy

AI in underwriting uses predictive analytics to evaluate risk factors with greater precision. Machine learning models analyze historical claims, behavioral data, IoT inputs, and demographic insights to generate accurate pricing models.

Faster Claims Automation

AI claims automation reduces manual review processes. Image recognition, natural language processing (NLP), and intelligent workflows allow insurers to approve simple claims within minutes.

Fraud Detection Enhancement

Fraud remains a major challenge in insurance. AI-powered fraud detection systems identify anomalies, suspicious behavior patterns, and claim inconsistencies more effectively than traditional rule-based models.

Cost Reduction and Efficiency

Automation minimizes administrative overhead, reduces processing errors, and lowers operational costs.

Personalized Customer Experience

AI enables insurers to offer tailored coverage recommendations, proactive risk alerts, and 24/7 chatbot support, improving customer engagement and retention.

These advantages position AI as a competitive differentiator in 2026.

3. Effects of AI on Insurance Operations

The operational effects of AI in insurance are transformative. Core processes such as underwriting, policy servicing, billing, and claims are becoming increasingly automated and data-driven.

Insurers are moving toward:

• Cloud-based AI platforms
• Real-time risk modeling
• Embedded insurance powered by APIs
• Data-driven decision frameworks

AI integration also supports better regulatory reporting and compliance management through automated monitoring systems.

As a result, insurers can launch new products faster, respond to market changes more effectively, and maintain stronger operational resilience.

4. Disadvantages and Challenges of AI in Insurance

Despite its benefits, AI adoption in insurance presents challenges that cannot be ignored.

Data Privacy and Security Risks

AI systems rely heavily on customer data. Ensuring compliance with global data protection regulations is critical.

Algorithm Bias

If AI models are trained on biased data, they may produce unfair or discriminatory outcomes in underwriting or claims decisions.

High Implementation Costs

Developing and integrating AI insurance platforms requires significant investment in infrastructure, technology, and skilled talent.

Workforce Disruption

Automation may reduce certain job roles, requiring workforce reskilling and organizational change management.

Regulatory and Ethical Concerns

Regulators increasingly scrutinize AI decision-making processes to ensure transparency and accountability.

To mitigate these risks, insurers must adopt responsible AI frameworks, robust governance models, and continuous monitoring systems.

5. The Future of AI Insurance Beyond 2026

Looking ahead, AI in insurance will become even more sophisticated. Emerging trends include:

• Real-time underwriting using IoT and telematics
• Advanced climate risk modeling
• AI-powered conversational insurance platforms
• Blockchain integration for secure and transparent claims
• Autonomous risk assessment engines

Machine learning in insurance will continue evolving, enabling smarter risk pricing, improved loss prevention strategies, and enhanced customer-centric solutions.

Insurers that strategically invest in AI-driven digital transformation while maintaining ethical standards will lead the industry.

Conclusion

AI in insurance 2026 represents both opportunity and responsibility. The impact of artificial intelligence on underwriting, claims automation, fraud detection, and customer engagement is undeniable. Advantages such as efficiency, personalization, and predictive risk management are driving widespread adoption.

However, insurers must carefully manage disadvantages including data privacy concerns, algorithm bias, regulatory complexity, and workforce disruption.

In the coming years, success in AI insurance will depend not only on technological innovation but also on governance, transparency, and trust. Insurers that balance innovation with responsibility will shape the future of the industry.

While the excess and surplus lines market was once an option of last resort, today it is all too frequently a first step in the process of insuring risk.

A coastal property facing new catastrophe models. A business navigating cyber exposure. A specialized liability account that has outgrown admitted guidelines. For agents and brokers, E&S is now part of everyday operations.

This shift has forced the distribution side of the industry to move faster, communicate more clearly and operate with greater precision. Technology is becoming a bridge to help insurance agencies keep pace while also strengthening relationships with their carrier partners.

Speed matters, but clarity matters more

Unlike admitted markets, where rates and underwriting changes can take time to filter through regulatory processes, non-admitted appetites can shift quickly based on loss trends, capacity and real-time market conditions. A carrier writing a class of business today may pull back tomorrow or adjust pricing as results demand it.

For the retail agent, this reality creates a constant challenge: Where does this risk belong currently? In the past, the answer has required multiple submissions, follow-up emails and trial-and-error market shopping. That cycle slows service, strains staff resources and frustrates underwriters who receive incomplete or misrouted submissions.

Technology can help avoid this cycle of frustration and wasted time, not by replacing relationships but by reducing the friction that can damage them.

Streamlined placements support better partnerships

Modern E&S placement platforms are designed to make submissions cleaner, faster and more consistent. The best tools help agents submit once, validate completeness and route risks to the right markets based on current appetite.

This kind of upfront triage benefits all involved. Agents spend less time chasing dead ends. Underwriters spend less time sorting through half-built submissions. Carriers receive applications closer to their appetites, with clearer exposure data and fewer missing pieces.

The result is a more efficient exchange that respects the time and expertise on both sides of the relationship. We’re seeing that with the deployment of Xchange - Powered by SIAA, which provides our members a faster, cleaner and easier way to access and place E&S business. 

Reducing errors and improving underwriting confidence

One of the most persistent challenges in E&S is submission accuracy. When clients want fast answers, agency teams sometimes make assumptions to move the process along. These seemingly educated guesses can create big delays later when an underwriter must circle back for corrections.

Technology that enriches submissions with third-party data sources can reduce the burden. Property records, hazard data and other verification tools can help confirm details before the submission ever reaches the carrier.

Doing this leads to fewer surprises, fewer resubmissions and a smoother path to a quote. More importantly, it helps carriers trust what they are seeing, which ultimately contributes to stronger carrier-agent relationships.

AI should be an optimizer, not a replacer

Artificial intelligence is playing a growing role in the E&S workflow, but the industry must be clear-eyed about its realities.

AI can help organize information, identify inconsistencies and accelerate routing. It can reduce manual data entry and make it easier for agents to package risks in an underwriter-ready format.

What it cannot do is replace underwriting judgment.

Complex accounts still require human experience, context and expertise. Technology works best when it clears away administrative clutter so underwriters and agents can focus on conversations that matter: coverage structure, risk controls, exclusions and long-term strategy. When positioned correctly, AI supports relationships rather than threatening them.

Strengthening carrier relationships through better submissions

Carrier relationships are built on trust, consistency and professionalism. In the E&S space, where underwriters face heavy submission volume, standout agencies are those that deliver clear narratives and decision-ready accounts. Technology helps agencies meet that standard at scale.

By standardizing intake, improving exposure clarity and managing workflow discipline, agents become better partners to their markets. Carriers benefit from lower acquisition expense per policy, improved risk selection and fewer wasted cycles.

Over time, these operational advantages translate into stronger long-term collaboration.

Carriers tend to prefer distribution partners who can deliver reliable data quality and efficient servicing without requiring carriers to expand headcount at the same rate as submissions.

Agencies that adapt will protect their growth

For agents and brokers, the risk of ignoring technology is not about missing a trend. It is about falling behind both the market and the competition.

As risks become more complex, turnaround time is becoming a competitive differentiator. Agencies relying solely on inbox-driven workflows will find it harder to shift books of business, maintain service levels and compete for talent.

The goal is not to adopt technology for the sake of shiny tech solutions. Rather, the goal is to protect the value of the agency by making the placement process faster, cleaner and easier to hand off to the next generation.

Relationships remain at the center

E&S will always involve more complexity than standard business. But complexity does not have to mean inefficiency. With the right technology, agents and brokers can keep pace with a rapidly evolving market while building better carrier relationships through stronger submissions, smarter routing and clearer communication.

The future of E&S distribution will not be defined by replacing people. It will be defined by empowering them. When technology reduces friction, relationships have room to grow.

We've all had a chuckle about the occasional hallucination by generative AI: the time when it recommended using glue to keep cheese from sliding off a piece of pizza; when an Air Canada chatbot promised a passenger a bereavement fare despite a policy to the contrary, and the airline had to live up to that promise; when a lawyer unknowingly submitted a brief to a judge that was based on citations from court cases that never happened; and so on. 

But a couple of recent stories go well beyond the chuckle level. While generative AI continues to show all the promise in the world, these stories demonstrate consistent problems that, unchecked, would lead to severe consequences. 

Let's start with the one about war games in which large language models almost always recommended escalating to nuclear weapons.

As Axios reports, a researcher at Kings College London pitted three popular LLMs — GPT-5.2, Claude Sonnet 4 and Gemini 3 Flash — against each other in 21 war games in which the AIs acted as the leaders of major nations. The scenarios included threats to survival, but also included lower-stakes conflicts, such as border skirmishes and resource competition. Yet 95% of the time, at least one of the LLMs "used" nuclear weapons, and escalation typically ensued. 

For anyone without a strong Dr. Strangelove streak, those results reflect a scary misjudgment. While the U.S. and the Soviet Union considered tactical nuclear weapons to be legitimate parts of their arsenals in the early years of the nuclear age, those were also the times when the countries casually considered using nuclear weapons for industrial uses such as mining and natural gas extraction. It's been clear for decades that nuclear weapons are simply too powerful for their effects to be limited to legitimate military or industrial targets. 

Even at one kiloton, the smallest payload for what's considered a tactical nuclear weapon, the explosion would be 100 times as powerful as the biggest conventional bomb in the U.S. arsenal. At the top end of the range for a tactical nuclear weapon (generally considered to be 100 kilotons), the explosion would be some seven times as powerful as the bomb dropped on Hiroshima, which destroyed a military target but also killed an estimated 140,000 people, the vast majority of them civilians. The radiation released can also reach far beyond the targeted area. 

While the Kings College researcher noted that no one is handing AIs the keys to nuclear weapons systems, he said, "Militaries are already using AI for decision support — and research suggests those systems may lean into rapid escalation under pressure."

The other article that caught my eye relates to ChatGPT Health. The app, launched in January, is consulted by some 40 million people every day — and a study found the potential for major problems with the app's diagnoses. For more than half of the study's hypothetical patients who should have sought immediate medical care, ChatGPT Health told them they should stay home or wait to schedule a regular appointment with a doctor. 

The article, in the Guardian, said: "In one of the simulations, eight times out of 10 (84%), the platform sent a suffocating woman to a future appointment she would not live to see.... Meanwhile, 64.8% of completely safe individuals were told to seek immediate medical care."

For the study, published in the journal Nature Medicine, researchers created 60 realistic patient scenarios covering health conditions from mild illnesses to emergencies, then presented those scenarios to ChatGPT Health in various ways: changing the gender of the patient, sometimes providing test results, sometimes adding comments about what "friends" advised, etc. Three independent doctors reviewed each scenario and agreed on the level of care needed, based on clinical guidelines.

The study found that ChatGPT Health did well on textbook emergencies such as stroke and severe allergic reactions. But "'what worries me most,'" a doctor is quoted as saying in the article, "'is the false sense of security these systems create. If someone is told to wait 48 hours during an asthma attack or diabetic crisis, that reassurance could cost them their life.'”

Any number of health experts have extolled the potential for AI-based health advice, coupled with wearables and telemedicine, to revolutionize healthcare — providing care to the elderly and to people in rural areas, who would otherwise have difficulty getting access, while slowing the inexorable rise in healthcare costs. And I've bought in: Chunka Mui, Tim Andrews and I included a lengthy scenario about the potential for AI-based healthcare in our 2021 book, "A Brief History of a Perfect Future."

I still think the potential is there, too. As OpenAI, the developer of ChatGPT, told the Guardian, the app is updated and improved all the time, and I hope they keep charging ahead. (OpenAI also said it doesn't believe the study reflects how people actually use ChatGPT Health.)

But I also hope they are constantly checking for problems such as those identified in the study, and anyone else using AI in situations with major consequences should exercise similar care. That includes insurers, and not just in healthcare. As we feel our way toward using AI agents, we need to be very careful to not only vet them before putting them into production but to then supervise them — because they absolutely will make mistakes — and to keep improving them.

Cheers,

Paul

In the timeless words of Sun Tzu in The Art of War: "If you know the enemy and know yourself, you need not fear the result of a hundred battles." Today, in the battle against fraud in business and government programs, entity resolution—combined with knowledge graphs and geospatial analytics—serves as that ultimate weapon, akin to Excalibur, the legendary magical sword that could cut through anything.

When it comes to fighting fraud, it cuts through layers of deception, revealing hidden connections between people, businesses, transactions, and locations that fraudsters purposefully endeavor to keep obscured. By mapping out entities and resolving disparate records across dispersed systems to the real individuals and organizations behind them, investigators gain the clarity to validate transactions, expose invalid transactions, and dismantle fraudulent networks.

Fraud in government programs and business operations thrives in the shadows of fragmented data: mismatched names, shell companies, fake addresses, synthetic identities, and manipulated locations. Without a unified view, billions of dollars are lost annually to schemes like improper benefit claims, procurement kickbacks, subsidy abuse, "paper mills," and phantom vendor payments.

Entity resolution bridges these gaps, linking records across databases—names and addresses, tax filings, business registries, transaction logs, social media, and public records—to create a "360-degree" profile of every entity involved.

Entity Superpower — Unmasking the True Actors

At its heart, entity resolution determines when multiple records refer to the same real-world person, business, or location, despite variations in spelling, abbreviations, typos, or deliberate obfuscation. Advanced algorithms and machine learning handle the noise: "John A. Smith LLC" might resolve to the same entity as "JAS Enterprises" owned by "Jon Smith," especially when tied to shared addresses, phone numbers, or transaction patterns.

When integrated into knowledge graphs, these resolved entities form connected networks of relationships—ownership links, family ties, shared board members, or transaction flows. Adding the basics of address geocoding and geospatial analytics overlays physical reality: mapping addresses, proximity of claimed locations, or clustering of suspicious activities in specific regions. This data fusion transforms isolated data points into a battlefield looking glass that maps where fraud patterns emerge clearly.

Consider a classic red flag in government-funded programs: more licensed or funded daycares than the number of children in an area could possibly require. Entity resolution uncovers this by resolving provider records to actual owners and cross-referencing enrollment claims against demographic data. Knowledge graphs reveal networks of colluding owners registering multiple entities at the same address or funneling funds through connected shell companies. Geospatial views highlight unnatural concentrations—clusters of daycares in low-population rural zones or urban blocks with improbable child-to-provider ratios—signaling potential ghost operations or subsidy farming.

So, as with childcare, insurance companies may apply entity resolution to chiropractors, MRI facilities, and clinics, but in addition now the named insured, agent, claimant, and adjuster meld in with medical providers, equipment, legal staff, vendors, and others in the graph across any line of business. As lines are combined and companies join forces, this process can literally map trillions of dollars of historical premiums and claims that could influence real-time payments.

The King's Sword Trumps All Use Cases

Drawing from innovative applications across business and government using knowledge graphs for fraud detection, the combination of entity resolution, knowledge graphs, and geospatial tools exposes fraud across diverse domains:

• Government Benefit and Subsidy Fraud: In childcare subsidies, housing assistance, unemployment benefits, or agricultural grants, resolved entities expose operators claiming impossibly high volumes. Geospatial analysis flags unnatural provider distributions relative to demographics, while knowledge graphs uncover collusive networks funneling funds through connected shells or using stolen identities for enrollment claims.
• Procurement and Contract Fraud: Vendors often conceal conflicts via layered ownership or bid-rigging. Entity resolution connects bidders to officials' associates or hidden entities; geospatial overlays reveal fictitious delivery sites or illogical routing; graphs detect circular payments or anomalous bidding patterns indicative of corruption.
• Fake Business and Identity Schemes: Fraud rings create phantom companies for loans, grants, tax credits, or PPP-style programs. Resolution merges digital and physical footprints—such as mismatched websites/IPs with abandoned addresses—while geospatial clustering pinpoints registration hotspots tied to broader scams.
• Money Laundering and Illicit Flows: In trade-based or benefit-related schemes, resolved entities link actors across jurisdictions. Knowledge graphs map multi-hop transaction chains; geospatial tools visualize fund movements against claimed origins, exposing laundering through high-risk locations or mismatched geographies.
• Insurance Claims Fraud: In property insurance schemes, fraudsters stage incidents like water damage during homeowners' vacations, directing repairs to complicit restoration providers. Entity resolution links claimants, properties, and service providers across cases, revealing common identities or ownership ties; knowledge graphs highlight recurring patterns in damage types, timing, and vendor involvement; geospatial analytics maps claim locations against provider clusters, unmasking organized rings exploiting insureds and property owners.

In auto insurance, staged accidents generate multiple unrelated passengers all seeking medical treatment from the same provider and being represented by the same lawyer even though they themselves may live far apart and curiously are frequently unable to be located.

The schemes for various lines of casualty and property in auto, home, workers' compensation, and commercial insurance all are well mapped by the NICB (National Insurance Crime Bureau). And new schemes are emerging all the time — especially with the backing of transnational criminal organizations, but also with just everyday people getting creative with generative AI.

En Garde — the Industry Keeps Its Hand on the Hilt

As fraud schemes grow more sophisticated with digital mapping tools and global reach, entity resolution in knowledge graphs—enhanced by geospatial context—will only sharpen. Real-time monitoring, AI-driven anomaly detection, and dynamic mapping will make deception harder to sustain. The result? Interdiction of transactions. Faster and better recoveries. Frustrated, if not deterred, criminals. Lower premiums for insureds. Safeguarded public funds.

In the war on fraud, knowledge is power—but resolved, connected, and spatially aware knowledge is the key to victory. Like Excalibur drawn from the stone, we across these industries, companies, and public bodies draw data from our legacy and modern systems. This combination of data and technology empowers those who wield it to cut through illusion and restore justice.

Let's talk about something that's been hiding in plain sight in insurance and healthcare operations for the better part of three decades: You have no idea what your processes are actually doing.

I don't mean that as an insult. I mean it as a structural observation. You have dashboards—God, do you have dashboards. Gorgeous ones with KPI tiles and sparklines trending whichever way the builder needed them to trend. You have reporting teams producing decks for Monday standups—assemblies of data that's six weeks old, filtered through three layers of organizational telephone, and crafted—not maliciously, but inevitably—to support a story someone already believed.

What you mostly don't have is instrumentation. Real instrumentation. The kind that tells you, in something close to real time, what your core processes are producing, where they're breaking, and what that's costing you.

That gap is about to get much more expensive to ignore.

Process excellence folks will recognize DMAIC—Define, Measure, Analyze, Improve, Control. The problem is that in most operations, the M and the A have always been the expensive, politically fraught parts. So organizations Define—sometimes brilliantly—and then Jump. Straight to Improve. They hire consultants, run workshops, launch initiatives, celebrate launches. A year later, they do it again. That isn't improvement. It's expensive thrash—innovation theater in a process‑excellence costume.

Instrumentation was always theoretically worth it. It just never made it to the top of the list.

Enter AI, which changes this calculation in two ways—one a mandate, one a gift.

The mandate first, because it's the one that gets you fired.

You can't drop operational AI into a live process environment without knowing precisely what it's doing. AI systems in claims processing, prior authorization, utilization management—these make decisions at a speed and scale no human team can realistically audit afterward. If you don't have instrumentation showing, in near‑real‑time, what your models are producing, where they're drifting, and where edge cases are piling up into systematic errors, you'll have a very bad day. Possibly a regulatory very bad day. Possibly a front‑page very bad day.

Operational AI forces the instrumentation conversation in a way Six Sigma consultants never could.

Now the gift.

AI also makes instrumentation cheaper and easier than it's ever been. Process‑mining tools can map your actual workflows—not the idealized Visio diagram, but what's really happening—by reading keystrokes, logs and system events that already exist. Natural language processing (NLP) can monitor unstructured outputs: call transcripts, clinical notes, adjuster comments, member complaints. Modern data pipelines can connect legacy systems in a fraction of the former time and cost. All without creating risk or dependencies.

By instrumenting your operation for AI, you end up using AI to measure what you should have been measuring all along. The mandate and the gift are the same. You don't get the AI transformation without building the measurement infrastructure—and once you've built it, you finally have something most organizations have never possessed: a real‑time picture of their own operations.

The counterintuitive part nobody talks about: people assume a fully instrumented, heavily automated operation becomes robotic. Soulless.

The opposite is true.

When 80% of your operation runs smoothly—instrumented, measured, automated, in control—something remarkable happens to your meetings. The variance archaeology, the defensive explaining, the "why did this metric move?" inquisitions—all move into dashboards that don't need a room full of people to interpret. What's left in your daily standup are exceptions. Real exceptions. The claim that fell outside every parameter. The member experience that defied categorization.

Exceptions are where operations learn. They're where customer‑service stories live—the quietly devastating and the genuinely remarkable—and those stories, surfaced in a room of engaged humans, are where innovation happens. Not in workshops or hackathons, but in noticing an exception, connecting it to context, and realizing it points to something structural.

The daily meeting becomes tactical again—focused on real issues, resolved quickly, without drifting into philosophical fog. Strategy moves to the quarterly business review, where it belongs. Mixing the daily and the quarterly is how organizations end up doing neither well.

The even better news is that a genuinely well‑run operation—one that knows what it's doing, measures what matters, and improves based on evidence—can deliver on a real mission. Instrumentation isn't separate from culture; it's the infrastructure culture runs on.

The more automated your operation becomes, the more human it can afford to be.

The instrumentation imperative is real, and AI is making it urgent. The organizations that win will be the ones that treat it not as compliance, but as what they should have built 20 years ago—finally achievable, finally affordable, and harder to ignore every quarter they wait.