For independent agents, the to-do list never gets shorter. New clients to win, policies to place, and revenue to grow. But there's one conversation that doesn't always make it onto the planning agenda, and it might be the most important one of all. What happens when it's time to hand things off?

Succession planning has long carried a reputation as something to worry about later. A conversation for agents nearing the end of their career, not those in the thick of building their business. But that thinking can be costly. The agencies that make the transition most smoothly aren't the ones that started planning at the last minute. They're the ones that built transferable tech operations from day one.

Here's the good news: if you're already using an agency management system to run your daily operations, you're likely closer to succession-ready than you think. The tools that help you manage client account data, track performance metrics, and stay on top of renewals can do double duty. Used consistently, they build the kind of organized and documented operation that makes handing things off far less daunting.

Performance Metrics Tell Your Agency's Story

When it comes time to demonstrate value, data speaks louder than anything else. Potential successors and buyers will want a clear picture of your agency's performance, including which lines of business are driving the most revenue, which producers are performing, and where coverage gaps exist across the book. Those answers need to be readily accessible.

A robust agency management system gives you this performance visibility in real time. Dashboards and reporting tools surface the metrics that matter most, from total annualized premium and active policies per customer to a detailed breakdown of your book of business by transaction type, often presented in intuitive visual layouts.

You can customize these reports too, filtering and drilling down into the data points that matter most. Some systems even let you benchmark your performance against peer agencies, giving you a clearer sense of where you stand. That kind of insight doesn't just serve a future transition. It sharpens your decision-making today, helping you spot growth opportunities and course correct before small issues become bigger ones.

Over time, these reports build a compelling picture of your agency's health and trajectory, one that tells a clear story to a successor and makes you a stronger agency today.

AI Keeps Client Knowledge Transferable

Serving clients without missing a beat is one of the first challenges any incoming leader faces. That means being able to find policy details quickly, understand coverage history, and get up to speed on the relationship without having to track down the person who used to handle it. When information is scattered across inboxes, desktop folders, and spreadsheets, that handoff becomes harder and more costly than it needs to be.

AI-powered agency management tools change that, and not just when a transition is on the horizon. Picture this: a newly onboarded staff member pulls up a long-standing client account in their first week. Rather than digging through months of email threads and agent logs, they get an instant summary of the relationship, enabling more knowledgeable client interactions and a much faster path to getting up to speed.

Clients expect continuity. They don't want to repeat themselves or re-explain their history with their agency. They expect whoever picks up the phone to already know them. AI makes that possible whether you're onboarding a new hire, navigating a leadership change, or simply trying to deliver a better client experience every day.

Renewal Tracking Protects What You've Built

Retention is the metric that tells the clearest story about an agency's health. A consistent renewal process signals that clients are being taken care of and that the book of business is stable.

A good management system gives you and any future leader a single view of every upcoming renewal, what has changed between the current policy and the renewal offer, and which clients are most at risk of shopping around. Predictive analytics flag at-risk policies before they become problems. Automated remarketing workflows retrieve updated rates and surface them alongside renewal details, so whoever is managing the book can act quickly and make informed recommendations.

For a buyer or successor, a clean and consistent renewal process is one of the most compelling things they can walk into.

No Matter Where You Are in Your Career — Start Now

Whether you're just launching your agency, in the middle of a growth run, or beginning to think seriously about the future, the time to invest in technology-enabled workflows is now.

The efficiencies you gain today will compound over time, and when the moment comes to pass the pen and the policies, you'll be glad you started early.

Walk into any bank branch, insurer's office, or contact hub, and you'll see the same challenge playing out: frontline teams juggling conversations, systems, and expectations in an environment where customers expect answers now -- and on the channel of their choice.

Consumers today benchmark banking, financial services and insurance (BFSI) service not against competitors but against the best digital experiences they see anywhere. The organizations that succeed in 2026 won't simply add more tools; they will blend human advice with smart, invisible technology, so their advisors, agents, relationship managers, and service reps can engage faster, personalize better, and build trust at scale.

What Customers Want (And Where Organizations Still Fall Short)

Customers want digital convenience, but they also want expert human guidance for financial decisions that carry risk:

• Choosing an insurance plan
• Applying for a loan
• Completing know-your-customer (KYC) requirements
• Making investment decisions
• Resolving service or dispute issues

McKinsey's customer-experience research across financial services shows why this matters:

CX leaders achieve stronger revenue growth, lower costs, and higher employee engagement than their peers - and generate meaningfully higher shareholder returns.

But many journeys across BFSI remain fragmented.

Customers begin online, switch to chat, walk into a branch or connect with an advisor, only to repeat themselves at every step. Follow-ups may be inconsistent, and context often gets lost between channels.

Yet the human element remains critical: across financial services; trusted advisors and relationship managers consistently rank as the most valued touchpoint, so much so that switching behavior rises when advisors leave or service becomes inconsistent.

The message is universal across BFSI:

Customers don't want to choose between people and technology. They want both, seamlessly connected.

Why Frontline Productivity Is the New Customer Experience

Customer experience and employee experience are inseparable.

When advisors and agents spend time hunting for information, switching screens, or reentering data, customers feel the friction immediately.

McKinsey finds that organizations that engage frontline teams as co-creators in CX transformation see:

• Higher customer satisfaction
• 20% improvement in employee satisfaction
• Faster turnaround time
• Stronger cross-sell and retention

In BFSI, this link is especially clear:

• A banker who has instant access to a customer's full financial picture can advise better.
• A field officer with mobile access reduces turnaround times.
• An insurance advisor with contextual prompts can personalize without delay.
• A contact center agent with unified history avoids escalations.

Technology doesn't replace expertise - it amplifies it.

Accenture's latest research reinforces the urgency: customers increasingly report difficulty reaching a human when needed, or navigating service journeys. In a landscape where attrition is high and expectations are rising, BFSI organizations must remove friction, not add new layers of complexity.

Where AI Fits: Practical, Not Hype

AI's role is not to automate away financial conversations - it is to shorten the distance between a customer question and a confident, compliant answer.

IBM's Institute for Business Value says that:

75% of financial services executives believe AI will improve personalization and CX but warn that fragmented systems and weak data foundations remain barriers.

The promise of AI becomes real when paired with clean data, integrated workflows, and empowered frontline teams.

In BFSI, this looks like:

1. Advisor / agent assist

Real-time summaries, next-best-actions, explanations of product rules, contextual prompts - helping frontline teams spend more time advising and less time searching.

2. Smarter routing

AI triages inbound requests to the right channel:

• Self-service for simple tasks
• Human support for complex or high-value conversations

This reduces drop-offs and improves first-contact resolution.

3. Engagement

Detecting repayment cycles, renewal windows, eligibility changes, life events, and portfolio gaps - then nudging advisors with timely reasons to reach out.

4. Faster, cleaner onboarding

AI-assisted KYC checks, document classification, real-time validation, multilingual support, especially important for banks, insurers, and non-banking financial centers (NBFCs).

Designing the "Human + Tech" Operating Model Across BFSI

To make this transformation real, BFSI leaders are anchoring around a few core principles:

Human-led, tech-accelerated journeys

Map the moments where customers need reassurance (loan approvals, investments, claims, disputes). Then use tech to handle the rest - context-gathering, data prep, routing, and follow-ups.

Modernize the service core

Accenture's "Service on the Brink" findings highlight a striking gap: customers don't feel technology has meaningfully improved service quality yet.

That gap closes only when:

• Data is unified
• Systems are connected
• AI is grounded in real rules
• Handoffs are seamless

Measure what matters

Beyond cost KPIs, leading BFSI organizations track:

• Advisor time saved
• First-contact resolution
• Customer effort scores
• Quality and consistency of advice
• Employee adoption and satisfaction

AI and automation must serve people, not distract them.

The New Standard of Service in BFSI

Picture a loan customer messaging a query.

A virtual assistant validates identity, surfaces repayment options, and flags eligibility for a top-up offer.

The customer asks a nuanced question; within seconds a relationship manager joins, armed with a clean summary of past interactions, risk profile, and suggested talking points.

The conversation finishes quickly, the customer feels understood, and the advisor moves confidently to their next engagement.

This is where BFSI is heading:

Frontline expertise supported, not overshadowed, by technology.

When AI, automation, and connected systems fade into the background, advisors and agents can lead every interaction with clarity, empathy, and insight.

That's the real frontier.

References

● McKinsey & Company. “Elevating customer experience: A win–win for insurers and customers,” 2023.
● IBM Institute for Business Value. “Insurance in the AI era,” 2025.
● Accenture. “Customer Service on the Brink,” 2025.
● McKinsey & Company. “The Human Advantage in Banking Customer Experience,” 2023.

In the legal profession, the work is only as strong as its support. A good argument isn't just persuasive, it's backed by citations. You can point to the contract clause, the case, the exhibit, and the chain of reasoning that got you to the conclusion. That's not academic formality. It's how legal work survives scrutiny in a court of law. 

Insurance is moving in the same direction.

For decades, insurance operations could lean on experience and muscle memory. A tenured underwriter knew what "this form usually covers." A claims leader knew the standard response posture. A broker knew which markets were flexible. That knowledge still matters, but the environment has changed. Regulators, legal teams, and procurement groups now expect decisions to be documented, explainable, and consistent over time.

"Trust me" is no longer a reasonable operating model.

Why legal workflows look the way they do

Legal work is predicated on the ultimate potential that it will end up in front of a judge. This fear shapes all the work lawyers do. 

A motion, an opinion letter, or a contract position might get scrutinized months or years later by a judge, with millions of dollars at stake. The only way to truly prepare for that situation is if the work product is structured to be audited. That's why legal workflows emphasize three things:

Citation. Show exactly where the claim comes from.

Reasoning. Make it possible to retrace the reasoning steps from source to conclusion.

Conclusion. Make it easy for another expert to validate or challenge the conclusion by having a very clear and articulate conclusion.

These practices aren't about slowing work down. They're how the legal industry moves quickly while staying defensible when the stakes rise.

Insurance is discovering the same truth, especially in claims and coverage interpretation.

Insurance is already under similar scrutiny

Insurance has always been regulated, but scrutiny is broader now and comes from more directions, including clients. Decisions can trigger omissions, bad-faith allegations, and liabilities that far exceed a coverage dispute.

State-by-state variability adds another layer. A defensible decision in one jurisdiction may be incomplete or risky in another.

At the same time, the work is deeply document-driven. Policies, endorsements, submissions, claim files, and correspondence are still stored in PDFs, scans, and formats that were created for manual review.

That means insurance decisions are often anchored in unstructured language that must be read carefully, compared across documents, and defended later.

In short, insurance faces legal-like constraints whether it realizes it or not.

The AI factor raises the bar, not just the speed

AI is often discussed as a productivity lever, but in insurance, the real challenge is credibility. When an AI-supported decision gets scrutinized, you need to show the basis for it. If the answer is a black box, you've created a new type of risk.

That's why the industry is increasingly prioritizing accuracy, explainability, and consistency over speed alone.

It's also why "model drift" matters. If a tool's behavior changes over time, it undermines consistency and auditability in regulated workflows.

This is one place where legal has a head start. Many legal technology workflows were designed around precedent and review. The focus is less about generating text and more about interpreting documents with citations and a clear path from source to conclusion.

Insurance now needs the same.

The future of insurance work

This shift isn't theoretical. It changes how teams should define quality.

In an insurance workflow built to withstand scrutiny, a good outcome isn't only correct—it's defensible. That means:

Your conclusions should point back to the policy language. Coverage positions and claim decisions need to be anchored in the actual text, not just institutional memory. Insurance has long depended on expert judgment in how professionals read policies, interpret exclusions, and apply precedent. The next step is making that judgment visible and reproducible.

Your reasoning should be transparent enough for peer review. If a colleague can't follow how you got there, an auditor or regulator won't either. Transparent reasoning isn't a luxury in high-stakes decisions, it's a requirement.

Your process should be consistent across teams and time. Insurance is full of niches and specialized expertise, but inconsistency is costly. As experienced practitioners retire, decision quality can decline if judgment stays trapped in individuals. Understanding the policy is the hardest problem in insurance. You can't solve it with expertise that only exists in people's heads.

Your documentation should be structured for "future you." Claims files, coverage analyses, and underwriting notes should read like work that's expected to be examined later. That's the legal mindset, and it's becoming the insurance mindset.

This is also why many leaders are talking less about flashy AI and more about repeatable operating models. The most valuable AI in insurance will look like consistency, documentation, and fewer surprises.

The practical takeaway for insurance leaders

If you're leading claims, legal, compliance, or brokerage operations, the question isn't whether your teams will use AI and automation. They already are. 

The real question is whether you're building systems that will hold up when scrutiny arrives. That means setting standards for citation, traceability, and reviewability in the work itself, not as an extra step at the end.

It also means resisting tools and processes that optimize for speed while sacrificing transparency. In insurance, "close enough" is often where the risk begins. 

Insurance is learning a lesson the legal industry learned long ago: when the stakes are high, the work must show its sources.

The AI market in the insurance industry is set to hit $80 billion by 2032, yet nearly two-thirds of carriers have a gap between their AI vision and reality. In essence, carriers understand what they want from AI and see the significant value it can drive but are struggling to actually put this into practice, especially in the property sector.

Marrying vision and reality is critical, however, as carriers look to scale automation, re-orchestrate and transform workflows, and fully realize the potential of AI across the lifecycle of a claim. There is a substantial downstream impact on claimants and clients, as well. 

There are a number of steps a carrier needs to take to find success with AI in 2026 and ensure they are future-proofed and ready for the next era of property claims.

The AI Journey for Property Carriers

First and foremost, carriers need to understand where they are on their AI journey. This is important in identifying what the next steps are, what's feasible in the short term, and eventually in the long term, and aligning company communications, operations, workflow, training, and more.

At the moment, anywhere from 58-82% of carriers are leveraging AI tools in their operations, but only 12% claim to have fully mature capabilities, and only 7% have achieved scalable AI success. This means that 93% of carriers are still in the part of their AI journey in which they are identifying how to scale AI to a point at which it is driving real, measurable outcomes. What we've seen so far is that adoption of AI has been popular in areas such as intake, triage, and documentation, but fully integrated technology and end-to-end AI workflows are still far away for most carriers. This, in turn, results in a fragmented technology experience, rife with different tools, vendors, and solutions. This limits AI's impact. It keeps value confined to each step in the lifecycle of a claim, can lead to inconsistent data or silos across systems, and weakens output.

Reliance on pilot programs or point solutions is the first step in an AI journey, but it certainly can't be the last. Most importantly, this technology is rapidly advancing, and the longer it takes carriers to find value and scalability, the further they'll fall behind the competition.

The Challenges Facing Carriers

There are three main challenges facing carriers. First is integration of AI into legacy technology. The majority of claims systems weren't built with API connectivity in mind, which introduces difficulties immediately into scaling this technology across workflows. Before integration even begins, carriers need to ensure that their claims systems can support orchestration.

Second is training a disconnected property workforce. An often-overlooked aspect of AI in the property space is preparing for the challenges that can arise when adjusters are managing heavy caseloads and working in the field. Support systems are critical to success in this area, and AI and any other new tools cannot feel like a burden to them. Training and communication in best-use cases are important in presenting these tools as benefits. This can be streamlined through rollout plans that align with day-to-day workflows, prioritize flexibility, and implement continuing training opportunities.

Finally, expecting AI tools to drive perfection is a key challenge. This technology won't deliver perfect outcomes from day one, but through gradual improvements can drive real change in processes. If too much focus is placed on perfection, then widespread implementation can be delayed. Instead, carriers should prioritize progress first and perfection second when measuring AI against real-world baselines, with the goal of refining capabilities over time.

What Real Impact Can Look Like

When challenges are addressed and overcome, and carriers understand how to progress from point A to point B in their AI journey, real impact can be achieved. This will be seen in a number of ways across operations.

Main points of impact will be evident in faster claims reviews in which AI is helping adjusters summarize claims, extract data, and capture notes more efficiently, as well as in improved program outcomes, smoother workflows across internal and external systems, and smarter claim routing. AI tools can evaluate loss severity, complexity, and fraud risk at intake, assisting in routing claims to the right recovery resource sooner.

In addition, adjusters will see stronger field operations through enhanced drones, sensors, and tablets, which enable faster mitigation, better assessments, and quicker resource mobilization.

The data backs up this impact. Intake automation has reduced average claim processing from 10 days to 36 hours, AI photo analysis boosts claim handling efficiency by 54%, and much more.

Prioritizing Human Expertise

A key consideration in the integration of AI is the increasingly important role that humans play, and will continue to play, in this process. AI should be seen as a tool to augment and support workforce expertise, rather than replace it.

This technology is powerful, but cannot be used to replace human judgment, empathy, or real-world experience in the claims process. Losses can be ambiguous, emotionally sensitive, or require nuanced, complex coverage decisions that AI cannot handle, and require human professionals to consider context, communicate clearly, and advocate for policyholders throughout each stage.

In essence, AI is a catalyst, not a cure-all, and carriers must aim to apply AI selectively while keeping people at the center of their claims decisions. Striking this balance will be the difference in staying ahead and remaining competitive in a rapidly changing technological and regulatory landscape.

For Sedgwick's full report on "Future-Ready Property Claims," click here.

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.

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.