For the vast majority of 2025, the future of the Federal Emergency Management Agency (FEMA) has been in doubt.

President Donald Trump originally announced plans to phase out the agency by the end of the year, and the administration has already helped dismantle programs FEMA manages, such as the advisory group responsible for updating national flood maps.

Since then, the Trump administration has backed away from a full cancellation of FEMA, instead expressing plans to "remake" the agency.

Point being, there's quite a lot we don't know. What is clear, though, is that FEMA is already operating much differently than it has before, and this distinction is likely to grow sharper.

That's difficult news for home insurance providers, which work hand-in-hand with the agency on a number of issues, from flood insurance to disaster preparedness and relief. Below, we've broken down four big ways that providers can prepare for the uncertainty to come.

Expanding disaster coverage

If FEMA disappears or is greatly diminished, insurance companies will see the earliest, largest repercussions in the National Flood Insurance Program (NFIP), which works with private providers to sell policies in areas with a high flood risk.

The NFIP is also, notably, very popular. According to data from 2023, more Americans purchased flood insurance from the NFIP than they did from all private insurers combined, by a split of 43% to 35%, respectively.

For providers that can, the easiest solution is to simply expand coverage offerings and fill the gap in this much-needed arena.

As of now, only about 4% of Americans have flood insurance, even though around 10% live in an area with a significant flood risk. With flooding and extreme weather becoming increasingly severe due to climate change, those at risk from flood will only increase.

While the NFIP is a helpful and much-needed resource for many, there are limitations to its coverage. For example, the program typically offers building and contents coverage separately, with restrictions around what's included in both. Here, private providers can improve on an existing framework by offering policies with greater flexibility and customization.

Partnering with state and local governments

Another way providers can adapt to a future without FEMA is by shifting focus.

Instead of working on the national scale, insurers will be wise to form partnerships with state and local governments — particularly in areas with a high flood risk — to create similar programs that benefit all parties, especially the homeowners who need this coverage most.

This principle goes beyond flood insurance, as future collaborations could also include disasters like wildfires and earthquakes, neither of which FEMA directly provides coverage for.

The California Earthquake Authority is a great example of cooperation in action. Much like the NFIP, the publicly managed nonprofit works with private insurers in California to offer earthquake protection where it's needed.

Companies operating in California — or in any of America's other most earthquake-prone states, which include Alaska, Hawaii and Texas — should seek strengthened partnerships with these programs where they exist, and should make efforts to help create them in places where they don't.

The same goes for damage from wildfires, which are not always covered by standard home insurance policies, despite their widespread impact.

In fact, unlike earthquakes, fires are no longer a regional issue. In 2023 alone, there were 23 states that had over 10,000 acres of land burned by wildfires. The prevalence of this problem is a sign that insurance providers could be increasing their work with state and local governments across the country, ensuring that more homeowners are able to get the coverage they need.

Becoming preparedness experts

Even with FEMA working at its full capacity, most Americans are not ready for an emergency like flooding, fires, earthquakes or storms.

That fact is evident in survey data from 2024 — before Trump was in office for a second time — in which 57% of Americans said they felt unprepared for a natural disaster, the highest that figure had been since 2017.

The reality is that many people are not getting the information they need, at least not in the places they're looking. Regardless of what happens to FEMA, insurance companies are in a prime position to become greater subject matter experts on disaster preparedness.

In practice, this could be as simple as educating new homeowners about the most common disasters in their area, with the next level being a custom policy plan that bundles flooding, earthquake or fire insurance with more regularly grouped options, like home and auto.

Insurance providers have a great opportunity here, as they have a captive audience of customers who both trust and rely on them for good information. It should be easy for insurers to activate their customers around the issues they need to be aware of.

Monitoring risk independently

One of FEMA's most notable jobs is its role in monitoring risk for all major disaster types nationwide. This information is relied on by both consumers and insurance companies to calculate risk in a given area.

With this role in jeopardy, providers may be left to navigate the data themselves. For some providers, this could mean creating more advanced internal tracking systems and risk assessment tools, while for others it may mean relying more heavily on the data released by state and local governments.

For some companies, this change will require more effort than for others. However, like all items on this list, it's a case of a short-term problem turning into an avenue for a major long-term opportunity.

Specialty insurance underwriting plays a critical role in markets shaped by dislocation, heightened uncertainty, or generally greater complexity. Typically, higher margins are required to compensate for higher levels of volatility, but navigating this volatility is no easy task.

What Is Specialty?

Around 1000 BC, David defeated the larger, better-armed Goliath with a sling and a stone, highlighting that battles can be won through scale (Goliath) or skill (David). In insurance, neither is inherently superior, and many companies use both scale and skill across teams, business units, or subsidiaries.

Specialty risks are those excluded from standard insurance. Take inland marine, which covers property that is in transit, is under construction, has high values, or has other idiosyncratic traits. This could run the gamut from medical equipment to infrastructure to bitcoin mining to fine art. These are all excluded in common property coverage, and each requires a highly bespoke solution.

There are four types of specialty insurance risk:

Expertise. These risks require a deep understanding of the exposure and underlying loss drivers, along with prior experience and a healthy dose of battle scars. Classic liability examples would be grain elevators, snow-plow operators, or liquor liability. Inland marine is the quintessential property example. Tax liability is a niche professional lines class, focused on unintended tax liability associated with transactions or other changes in tax treatment.

Structure. These are property and liability coverages with unique structural characteristics. The classic example is excess & surplus, where freedom of rate and form gives underwriters flexibility on terms and pricing. Alternative risk transfer, often for larger clients, similarly varies retention, limits, caps, coverage options, and more. Channel relationships (binding authority, MGAs) may also include variable, loss-sensitive performance features. 

Dislocation. For these, the demand for insurance exceeds the supply, resulting in excess rate. Often, the lack of supply is due to loss-driven distress, leading to the pullback of capacity. Cat-exposed property generally represents this risk in any hard market point in the cycle.

Service. These risks require solutions in addition to risk transfer, which in turn requires non-insurance expertise. Examples include property engineering, cyber risk mitigation, or auto telematics. The intention could be to prevent or mitigate loss or provide some insight that allows an insurance carrier to have superior risk selection.

These archetypes aren't mutually exclusive, as some businesses can have several of these features. I tend to de-emphasize certain specialized risks, especially those with higher volatility across the insurance cycle such as terror or remote-return-period property, like earthquake or other non-peak zone perils. These can be profitable but (in my view) resemble picking up pennies in front of a steamroller. It works until it doesn't, and when it goes bad, the losses can be severe.

Specialty can also mean emerging risks with little track record and higher uncertainty, such as intellectual property or contingency, two of the more recent P&C market innovations – which also happen to be distressed insurance products where ultimate losses were underestimated.

Specialty Underwriting Requires Slingshot Precision

Specialty underwriting is about skill over scale. It requires more nimbleness, creativity, and precision than standard risk. There are six core pillars of great specialty underwriting:

1. Scale within the niche. Average line size needs to be balanced relative to the total portfolio. Losses inevitably will happen, and without scale there is less room for error. Balance is commonly measured by premium-to-limit ratios, to ensure there is enough depth to reasonably absorb loss when it happens.

2. Surgical underwriting thesis. Every specialty segment needs a clear rationale. The underwriter might have some unique edge or expertise. In any case, markets inevitably shift, and usually specialty niches become less attractive over time once the crowd catches on and there is more capital availability. Cycle management is a critical feature for any underwriting thesis.

3. Quantifying upside and downside. It's difficult to plan for precise outcomes, particularly in a short horizon. Underwriters need to understand the stochastic distribution of results – the probability of profit relative to the probability of loss. Underwriting and actuarial need to be deeply intertwined with underwriters who understand the quantification of the upside/downside, and actuaries needing business judgment, so the quantification is not mechanical and superficial.

4. Street smarts. It's critical to understand when math might be wrong and avoid over-reliance on models. This applies to any catastrophe model, any probable maximum loss (PML) calculation, and any return on capital model with diversified capital. Street smarts means appreciating that models are directional at best.

5. Exceptional talent. Great specialty portfolios are built by talented and passionate underwriters. Not just technically strong but with market followership across all stakeholders: brokers, reinsurers, and other underwriters. Great underwriters are humble, appreciating what is unknown. The best underwriters have passion, which they exude when they talk about their business.

6. Portfolio Balance. Given specialty's inherent volatility, it requires a portfolio of niches, ideally with non-stacking, non-correlating exposure. Diverse exposures will lower the standard deviation of results, meaning, the overall average performance should be less volatile. Portfolio breadth also allows more flexibility to dial up or dial down specific niches in response to the market cycle. There is a critical caveat, the need to avoid "de-worsification." Every niche needs to have a strong thesis and favorable outlook, or it risks dragging on results.

Conclusion

Like David defeating Goliath, specialty underwriting is all about precision and skill honed through practice. Success in specialty lines requires ensuring every line has a clear thesis for market success, a path to scale within the niche, and the right balance of risk and reward.

The way physicians are evaluated has profound consequences — not just for reimbursement but also for clinical practice, professional trust, and ultimately patient outcomes.

Yet too often, performance measurement relies on opaque or "black-box" analytics that lack transparency and fail to resonate with the clinicians whose behavior they are meant to influence. Evidence-based, transparent, and traceable methodologies are essential if health plans and providers are to find common ground and use performance data as a tool for genuine improvement and change.

Among the many friction points in payer–provider relationships, few are as consequential as performance evaluations, which — like prior authorization and reimbursement rates — directly affect both financial outcomes and professional identity.

Like the other two hot-button issues, evaluations affect income, but they also touch on the sensitive matters of clinical outcomes, practice habits, and professional judgment. Low evaluations can be viewed as criticism of a physician's performance, which strikes at the heart of their practice and their personal brand.

A longstanding lack of mutual payer-provider trust compounds this contentiousness. Plans suspect providers try to boost their income by performing as many procedures and ordering as many tests as possible, often with little thought to necessity or wasteful low-value care. Providers often perceive plans as focused primarily on financial outcomes rather than patient care.

This friction between payers and providers has been exacerbated by health plans' use of opaque methodologies – even AI – to analyze provider behavior. Health plans have long used analytics that providers consider obscure, unfair, or irrelevant. The lack of transparency and accurate attribution in these approaches has fueled the abrasion between these two crucial healthcare stakeholders.

Today's Typical Performance Reviews: Group Level and Aggregate

Health plans today primarily rely on claims data to evaluate provider performance. While clinical data would be ideal and clinical data interoperability is improving under TEFCA, it is not yet widely available at scale.

Most performance reviews occur at the medical group, practice, or health system level. Common approaches include:

• Cost-efficiency metrics such as total cost of care, usage, and readmission rates.
• Quality measures like HEDIS scores, chronic disease control, and hospital-level outcomes.
• Patient experience scores that are typically aggregated through Consumer Assessment of Healthcare Providers and Systems (CAHPS) surveys.
• AI-driven insights that are increasingly used to identify patterns and trends.

These methods provide a broad view of performance but do not identify or evaluate the wide performance variation that exists between individual clinicians. It's hard for a single physician to see himself or herself in this data — or to trust and act on it in meaningful ways.

For performance measurement to change behavior, physicians must trust it. That trust comes when systems have three essential attributes:

• Transparency – physicians can see precisely how results were derived, from evidence sources to algorithm design to data application.
• Traceability – every measure can be linked back to the clinical guidelines or research from which it was derived.
• Comprehensibility – physicians can understand the methodology and validate the logic themselves.

Evidence-Based Standards: The Foundation for Fair Measurement

The best sources for physician performance measures are evidence-based clinical practice guidelines published by medical societies and professional organizations. These guidelines are based on scientific findings, cumulative clinical experience, and the consensus judgment of practicing clinicians. They are stewarded by respected leaders in each specialty.

Another essential source is peer-reviewed research from leading medical journals such as The Lancet and The New England Journal of Medicine, which can provide convincing evidence that one clinical practice is safer or more effective than another.

Then there is the data that the measure is based on. Today, claims data is the largest and most widely available data set for measuring physician performance, and a great deal about clinician performance can be determined with claims data if it's applied correctly.

Equally important, evaluations should be applied at the individual physician level, not just at the group or system level. Aggregated metrics can mask unwarranted variation in care that lower quality and increase cost. Individually attributed measures ensure accountability, highlight clinical excellence, and surface opportunities for targeted improvement. Physicians who undergo individual reviews often report feeling empowered by evidence-based data specific to their own practice — and they are often more willing to make meaningful changes.

Of course, some clinicians, in spite of research and professional guidelines, may persist in doing things that are not aligned with evidence. In those cases, plans can apply pressure through mechanisms such as tiered or selective networks, limiting referrals, adjusting reimbursement incentives, or requiring prior authorization and more.

Finding Common Ground

Fee-for-service reimbursement fuels payer–provider mistrust by rewarding volume rather than outcomes. But even under value-based care, disagreements about performance measurement persist.

The path forward lies in performance analytics that are scientifically sound, mutually acceptable, evidence-based, and transparent to both parties. Only then can health plans and providers share a language that reduces friction, builds trust, and inspires clinicians to improve care delivery.

AI will continue to revolutionize healthcare in many ways. But when it comes to evaluating physician performance, black-box algorithms are not the answer. Evidence-based clinical analytics, grounded in transparency and traceability, remain the fairest and most effective approach — for plans, providers, and patients alike.

Only then can we engage and inspire physicians to change practice behaviors, reduce waste from unnecessary low-level care, enhance patient outcomes and truly arrive at value-based care.

The insurance market has been under increased pressure throughout 2025 from every direction. Litigation is becoming more aggressive, capacity is tightening, and regulations are changing fast. For brokers, MGAs, carriers, and capital providers, these forces aren't abstract — they're reshaping day-to-day decisions, from pricing and reserving to partner selection and tech investment.

Stitching it all together is the urgent need for real-time insight into data, operational agility, and underwriting accuracy. Insurance companies that respond quickly, make better-informed decisions, and provide great customer service are already pulling ahead of the competition.

Litigation: Less Predictable, More Costly

In Florida, tort reform has changed the rules, compressing claims timelines and shifting litigation incentives. However, elsewhere in the U.S., third-party litigation funding (TPLF) is making those same rules harder to follow.

In July 2025, Reuters reported that litigation financiers narrowly avoided a proposed 41% tax on their returns — a sign of how embedded and influential the sector has become. At the same time, Burford Capital is poised to collect $6 billion from its investment in a massive oil-and-gas arbitration case against Argentina.

Add in social inflation driven by mass tort advertising and shifting jury sentiment, and the result is a claims environment that's harder to predict, price, or reserve for.

Capacity Is Tightening — Especially in E&S

At the beginning of 2025, I predicted the continued boom of the E&S market. While this market has seen growth, it's no longer as wide open as it was in 2024 due to carriers getting more selective. Appetite is narrowing. Loss ratios are under pressure due to record losses from climate change. Across property and casualty (P&C) and professional lines, underwriting discipline is no longer optional; it's a threshold to even stay in the room.

And as margins tighten, speed matters. Launching products, testing appetite, and adjusting pricing dynamically is now a core advantage.

Reporting and Regulation Are Raising the Bar

The pressure isn't just coming from courts and carriers; it's also coming from regulators and capital partners.

For MGAs and hybrid fronting carriers, real-time bordereaux reporting, audit readiness, and live profitability tracking are now essential to maintaining trust and capacity, and the innovators in these markets are already rethinking their tech infrastructure to meet demand.

The old way — manually assembling spreadsheets to send weeks after the fact — just doesn't cut it any more.

Agents and Brokers Use AI to Stay Ahead

Insurance agencies and brokerages are not passengers in the AI journey—they're pilots. A recent Agents United report shows how independent agents leverage AI and predictive analytics to gain efficiency, improve client outcomes, and unlock new revenue streams. But getting there will be a massive undertaking. Data will need to be cleaned, unified, and stored in a single, dynamic repository that acts as a reliable source of truth across the organization. I described it previously as a secure container for information that the agent only needs to enter into the system once.

Why This Matters

• Personalized, Real-Time Client Proposals: AI synthesizes client data such as claim history, risk exposures, and market trends to craft tailored policy suggestions instantly, helping agents win trust and gain bandwidth. Personalization wins new policyholders and helps retain existing clients, as well.
• Efficiency Gains in Operations: Automating routine tasks, such as lead scoring, document generation, or renewal reminders, frees agents to focus more on advisory and client relationships rather than admin overhead.
• Regulatory and Risk Alignment: Predictive analytics help flag potential compliance or fraud concerns early in submissions or renewals, supporting agents in maintaining client integrity and agency credibility.
• Competitive Differentiation: With nearly 70% of brokerages adopting generative AI in some form, early adopters who integrate AI deeply into the sales and underwriting workflow gain a decisive edge.

By integrating AI insights, brokers and agents can operate more strategically, offering personalized, faster service without sacrificing quality. This allows them to stay relevant even as market turbulence increases.

The Bottom Line

From Florida's tort environment to tightening carrier appetite, the story is the same: Faster insight, stronger controls, and greater transparency are now table stakes.

For brokers, MGAs, and hybrid fronting carriers, this means:

• You need underwriting precision supported by real-time data — not just historical loss trends.
• You need agility to adapt, launch products, and adjust pricing as litigation and capacity trends shift.
• You need audit-ready compliance and accurate, transparent bordereaux to maintain relationships with fronting carriers and capital providers.
• And most of all, you need a tech stack that doesn't just record activity, but drives better decisions.

In a market that is this volatile, leadership depends on how fast you can adapt. The rules have changed, and your tech must keep up.

The U.S. insurance landscape is dynamic, driven by litigation, capacity issues, and regulation. The industry demands agility, precision, and transparency. Brokers, MGAs, and fronting carriers must leverage advanced technology for real-time insights, optimized underwriting, and compliance. Adapting swiftly with innovative tech stacks will ensure survival and leadership in this evolving market.

Customer experience (CX) has always been vital to the insurance industry, but fundamental aspects of it have changed. Historically, agents and customer service representatives were the main points of contact with consumers and clients, and they defined CX. But today, CX is distributed across a more complex, hybrid structure; customers interact with insurers through multiple digital channels as well as trusted intermediaries, meaning insurers must support both direct and agent-led experiences to ensure the client is receiving the best customer experience possible.

Many carriers fail to meet the demands of this multichannel CX environment due to outdated, batch-based processing, lack of access to real-time data, and aging or poorly designed systems that don't support digital-first engagement. A survey of 250 producers revealed that agents increasingly support multiple lines of business—life, annuity, and P&C—demonstrating the necessity of a unified view of customer experience without the current inefficiencies and disjointedness.

Improving customer experience starts with addressing one of the biggest obstacles in insurance: data complexity.

Insurance data is complex, inconsistent and often redundant.

A single carrier can have 35,000 different data attributes in their life products alone. In addition to the natural complexity of the industry, legacy systems and decades of product layering have created overlap between data structures, making them extremely inconsistent. In some cases, a single data attribute is replicated 10 to 18 times across various internal systems.

The result of this overlap and inconsistency is that insurers lack a single source of truth when it comes to their customers. Holistic views are hard to assemble because data is spread across many systems and, in many cases, inaccessible. Business users struggle to find what they need, often using shadow systems and workarounds to piece together elements of a fragmented customer picture. Although it feels more challenging to implement, data modernization is equally important as system modernization. Without a clean, unified data foundation, carriers struggle to deliver real-time transactions, enable intelligent automation, or personalize experiences in meaningful ways.

And, if the picture can't be fully drawn, then how can a carrier build customer personas, map customer journeys, or any of the other more advanced steps in optimizing CX?

Solving the data problem isn't optional — it's the foundation for modern CX.

Unified data is essential for omnichannel success.

A single source of truth is essential for analytics, AI implementation, and optimized client service, but it remains elusive for many insurers. Legacy platforms create data silos, and multiple generations of products cause data to be inconsistently transformed and stored — determining the authoritative source at any given moment becomes a challenge. Traditional approaches to centralizing data often backfire, resulting in rigid structures that restrict access. Instead, carriers should focus on data fabrics, governance models that support usability, and democratized access. If CX platforms rely on outdated or conflicting data, any improvements will be short-lived.

True omnichannel experiences require more than channel availability. Omnichannel experiences demand consistent, connected service across every touchpoint. Agents and customer service representatives need visibility into all prior interactions, whether through digital self-service, a call center, or an in-person meeting. Agents should be able to see online transactions, even if they're incomplete, to help clients pick up where they left off. They should be able to see the attempted transaction and how it can be completed to create total understanding. Data governance across all channels is vital to making holistic CX possible.

New PAS technology helps insurers meet CX expectations.

Full spectrum transparency requires modern policy administraton systems (PAS) with real-time application programming interfaces (APIs), common data services, and unified interaction histories. Only then can the entire ecosystem of clients, agents, and employees operate efficiently to deliver a cohesive experience.

The latest PAS technology helps insurers enhance CX with a focus on modularity — like API-first design, microservices, and event-driven architecture. Modern PAS solutions support the real-time data flow critical for creating smooth and responsive CX experiences, allowing changes to propagate instantly across systems without replication.

Carriers are also embracing cross-system product bundling, intelligent workflows, advanced analytics, and, increasingly, agentic AI. These technologies reduce manual intervention, accelerate underwriting and claims, and enable dynamic, personalized engagement. Ultimately, the new generation of PAS empowers insurers to evolve with customer expectations — not just react to them.

Successful CX requires rethinking core technology.

Insurers that treat digital transformation as a front-end exercise will continue to struggle. True CX gains come from rethinking the core — modernizing policy admin systems, untangling data complexity — and embracing omnichannel strategies built on real-time, API-driven infrastructure.

In an age of automated processes, customers' expectations for a fast and responsive customer experience are only rising. The carriers that succeed will be those that can deliver seamless, data-driven, omnichannel experiences by aligning the right technology with a clear, execution-focused strategy.

You work at an insurance company, in an industry where time plays a critical role in gaining a competitive edge. Your team has an idea and a vision for a new insurance product that answers real market needs. You take it to IT, and the response is: We can deliver in two to three months. Do you really have time to wait?

For decades, the process of introducing a new tariff, modifying terms and conditions, or updating underwriting rules resembled a slow, multi-stage cycle between business, IT, and legal. Every single change, even the smallest, required developers to translate business logic into code, followed by lengthy testing and deployment.

New technologies are changing this picture for good.

A configurator for change and innovation

More and more often, the industry is talking about product configurators that, powered by business rules engines (BRE), flip the traditional dynamics of product launches. Instead of waiting on IT, business teams can create and adjust product logic on their own. With intuitive, no-code or low-code graphical interfaces, users define every aspect of how a product works. They decide how pricing is calculated, which variants and options are available, who qualifies for a policy, and under what conditions. All those complex dependencies that used to be buried deep in the code are now transparent and fully configurable.

The fundamental shift is that these tools are designed with non-technical users in mind. Instead of writing complicated scripts, they define rules in decision tables, build calculation functions, or even model entire processes through visual diagrams.

What can product configurators be used for?

One of the key roles of product configurators is speeding up time-to-market for new products and modifications. Business teams can also set up pre-defined benefit packages or dynamically segment customers to offer personalized terms.

In underwriting, configurators become the central tool for defining and updating risk assessment rules. Instead of relying on static guidelines, underwriters can continuously adjust logic to support both manual and fully automated processes. The same applies to pricing – all aspects of rating logic, from simple validations and discount/markup conditions to complex premium calculation algorithms, can be managed centrally and in real time.

Configurators also bring order to managing policy terms and conditions and integrating with policy administration systems (PAS). Mapping products and their rules into the core system becomes a straightforward, configurable process, ensuring consistency throughout the policy lifecycle. In addition, these tools often serve as a central repository for reference data such as address dictionaries, transaction codes, or vehicle classifications, ensuring data consistency across the organization and boosting operational efficiency.

How can you be sure this will work?

Traditionally, the guarantee that a solution would function as expected came from IT. When business takes on the role of product creator, there's a natural fear that something might go wrong.

However, modern configurators have built-in testing mechanisms. For example, an analyst creating a discount rule doesn't need to wait for a deployment cycle to verify it. They can instantly run single test cases or entire regression test suites to see how the change affects the entire product portfolio.

Equally important are full version control and auditability. In insurance, being able to track, compare, and roll back changes when needed is essential. Configurators maintain a complete history of every modification, making it easy to manage multiple product versions - for instance, rolling out new terms on a specific date, tailoring offers to different sales channels or customer segments. Detailed audit logs ensure complete transparency and regulatory compliance.

More than just speed

Using a product configurator should be seen as an investment that quickly pays off. The first benefit you'll notice is a dramatic reduction in time-to-market - from months down to days. That allows you to respond faster to competitor moves or regulatory changes.

You'll also gain independence from IT.

When a new product idea or modification can be tested and rolled out quickly, the organization becomes more agile and responsive.

Finally, automating manual processes directly reduces operational costs and minimizes the risk of human error.

What's next?

Analysts agree that the next stage of evolution for these tools is the integration of rule-based logic with predictive models and artificial intelligence. Imagine a system where the configurator not only executes defined rules but also leverages AI recommendations to optimize pricing in real time, automate underwriting decisions based on predictive analytics, or flag potential fraud attempts.

Personally, I can't wait to see this future unfold.

The healthcare system in the U.S. processes more than a billion insurance claims each year. With this scale comes complexity, administrative cost, and inevitable risk: denials, billing errors, fraud, and compliance issues drain billions of dollars annually from payers and providers alike. For decades, insurers have relied on manual reviews, retrospective audits, and rigid rule-based systems to manage these risks. While effective to a point, these methods have not kept pace with the increasing volume of claims, the sophistication of fraud schemes, and the demand for faster reimbursements.

Artificial intelligence, particularly when paired with machine learning and advanced data analytics, is beginning to transform this space. By shifting from reactive oversight to proactive risk detection, AI offers insurers, providers, and patients the possibility of fewer denials, lower costs, and greater trust in the system.

At its core, the value of AI in healthcare claims lies in three practical applications: predicting denials, catching billing errors, and spotting fraud patterns. These are not speculative ideas; they are real-world use cases that are already being implemented by both providers and insurers today. Let's examine how these applications reduce risk across the claims lifecycle—and what the future may hold.

1. Predicting Denials Before They Happen

Denial management is one of the costliest pain points for providers. Industry estimates suggest that five to 10% of all submitted claims are denied on the first pass, with more than half of those denials being potentially preventable. Each denied claim not only delays reimbursement but also creates costly rework that clogs up revenue cycle operations.

AI can now predict the likelihood of a denial before a claim is ever submitted. By analyzing historical claims data—including payer rules, provider specialties, diagnosis/procedure combinations, and previous denial trends—AI models can assign a risk score to each claim in real time.

For example, if a claim has a high probability of being rejected for lack of medical necessity, the AI system can alert the provider's billing team to attach supporting documentation up front. Similarly, if prior authorization is likely required, the AI can flag it before submission.

For insurers, this predictive capability reduces the need for downstream appeals and resubmissions—streamlining operations and lowering administrative costs. For providers, it increases first-pass acceptance rates, which directly translates into healthier cash flow.

Looking ahead, we can expect predictive denial prevention to become more personalized. Models will adapt not only to payer rules but also to patient-level risk factors and provider-specific patterns, allowing a more dynamic and customized submission process.

2. Catching Billing Errors With Precision

Billing errors remain one of the largest sources of claims risk. Sometimes they are as simple as mismatched patient identifiers or incorrect coding; other times they involve systemic issues like upcoding, unbundling, or duplicate charges. Historically, insurers have relied on post-payment audits and claim edits to catch these problems—but by then, money has often changed hands, and clawbacks are difficult.

AI shifts this from retrospective correction to prospective prevention. Natural language processing (NLP) models can scan clinical documentation and compare it with coded claims in real time, ensuring that the story told in the medical record aligns with the claim being billed. Machine learning algorithms can also detect subtle inconsistencies that humans or rule-based engines might miss—for example, a high-cost procedure appearing in an outpatient setting where it is rarely performed.

The practical impact is twofold:

• For insurers: Reduced leakage due to overpayments and more consistent application of policy rules.
• For providers: Fewer costly audits and repayment demands, and improved compliance with payer contracts.

Soon, we can expect even greater integration between electronic health records (EHRs) and claims processing systems. Imagine a workflow where AI not only detects an error but automatically suggests the corrected code or documentation needed—turning error detection into real-time error resolution.

3. Spotting Fraud Patterns at Scale

Fraud remains the most complex and costly risk for insurers. Estimates from the National Health Care Anti-Fraud Association suggest that tens of billions of dollars are lost to healthcare fraud annually in the U.S. alone. Fraudulent schemes—phantom billing, kickbacks, medically unnecessary services—are constantly evolving, making it difficult for rule-based detection systems to keep up.

AI excels at pattern recognition across massive datasets. Unlike traditional systems that flag claims based on rigid rules (e.g., a certain dollar threshold), AI can learn the nuanced signatures of fraud: unusual billing frequencies, atypical provider-patient relationships, or geographic anomalies that don't fit established patterns.

For example, AI might detect that a small clinic is billing for a volume of complex procedures far above the specialty's norm, or that multiple patients are receiving identical services at suspiciously regular intervals. These are signals that often escape manual reviewers but are clear to machine learning models trained on millions of claims.

Importantly, AI can also reduce false positives, which are a major burden on insurers. Instead of flooding fraud investigators with thousands of "maybe suspicious" claims, AI can prioritize the highest-risk cases with supporting rationale, allowing investigators to work more effectively.

The future of fraud detection likely lies in collaborative AI ecosystems where payers, providers, and regulators share anonymized data, allowing algorithms to learn across broader datasets. This will make it harder for bad actors to exploit gaps between organizations.

The Broader Risk-Reduction Value

These three core applications—denial prediction, error detection, and fraud spotting—represent the immediate, tangible value of AI in healthcare claims. But their impact is broader when viewed through the lens of risk management:

• Financial Risk Reduction: By preventing denials and fraud, AI helps stabilize cash flow for providers and reduces payout leakage for insurers.
• Operational Efficiency: AI reduces the rework cycle, freeing human staff to focus on exceptions rather than routine processing.
• Regulatory Compliance: Proactive error detection helps organizations stay ahead of compliance audits and avoid costly penalties.
• Member and Provider Trust: Faster, more accurate claims processing builds confidence among patients, providers, and payers alike.

For insurance leaders, the adoption of AI in claims is not just a technology upgrade—it is a strategic imperative for maintaining competitiveness in a rapidly changing healthcare landscape.

Practical Considerations for Insurance Executives

While the benefits are clear, implementing AI in claims operations requires thoughtful planning. Insurance executives should consider:

1. Data Quality and Integration: AI is only as strong as the data feeding it. Insurers and providers must invest in cleaning and integrating data across claims, clinical, and operational systems.
2. Change Management: Staff must be trained to work alongside AI tools, interpreting insights and taking action on recommendations. This is less about replacing humans and more about augmenting their effectiveness.
3. Ethical and Regulatory Oversight: AI models must be transparent and explainable, particularly when they affect payment decisions. Regulators will increasingly demand evidence that AI tools are unbiased and compliant.
4. Scalability and Interoperability: Systems should be designed to scale across multiple lines of business and to integrate with both legacy systems and emerging digital health platforms.

Looking to the Future: A More Intelligent Claims Ecosystem

We are moving toward a future where claims processing becomes increasingly real-time, proactive, and intelligent. Instead of the current sequence—service rendered, claim submitted, denial issued, appeal filed—AI will help shift the paradigm toward "right-first-time" claims.

In practical terms, this could mean:

• Near-instant adjudication of routine claims, enabled by AI-driven validation at the point of submission.
• Continuous fraud monitoring that adapts to new schemes in real time.
• Dynamic contracts between payers and providers, where reimbursement models adjust automatically based on AI-driven insights into quality and efficiency.
• Greater patient transparency, with AI tools that explain in plain language why a claim was paid, denied, or adjusted—reducing frustration and building trust.

The promise of AI is not to eliminate human oversight but to make oversight smarter, faster, and more resilient. For insurance leaders focused on reducing risk while maintaining efficiency, the time to engage with these tools is now—not five years from now.

Conclusion

AI is no longer a futuristic buzzword in healthcare claims. It is a practical, proven tool that reduces risk by predicting denials, catching billing errors, and spotting fraud patterns at scale. For insurance leaders tasked with protecting financial performance and operational integrity, AI offers a rare combination of immediate cost savings and long-term strategic advantage.

The healthcare claims process will always carry some level of complexity and risk. But with AI, insurers and providers can move closer to a system that is not only more efficient and accurate, but also more trustworthy for all stakeholders.

Over the past decade, many insurers have automated key processes, such as document classification, policy issuance, and claims triaging. Many have also implemented rule-based workflows for claims, underwriting, policy servicing, and compliance monitoring. A results has been faster turnaround and reduced operational overhead. 

Still, insurers remain caught between legacy systems and growing expectations from customers, regulators, and the business itself.

Artificial intelligence (AI) is helping bridge that gap. From underwriting and claims to compliance and customer engagement, AI is quietly but smartly reshaping insurance operations. A poll conducted by Global Data in Q3 2025 found that 46% of respondents identified underwriting and risk profiling as the functions most improved by AI. This was followed by claims management at 20% and customer service at 18%.

Let's explore how AI is fueling transformation across key insurance functions.

Smarter, Real-Time Underwriting

Underwriting has long relied on historical data and static models. But today's risk environment is far more dynamic. From climate changes and emerging health risks to evolving customer behavior and stricter regulatory pressures, underwriters face growing complexity and faster change.

One way to deal with the challenges, without missing compliance or overlooking digital fraud, is a strategic adoption of AI tools. Leading U.S. insurers are now using AI to improve efficiency, access real-time insights, and make more accurate risk assessments. According to the Zipdo Education Report 2025, AI-driven underwriting can reduce policy issuance times by up to 50%.

Keeping predefined checklists aside, underwriters now leverage AI to analyze vast volumes of structured and unstructured data sets, many of which would be extremely tedious to process manually.

For example, AI assists underwriters by:

• Surfacing localized risks through geospatial data and NATCAT data
• Flagging inconsistencies in applications or documentation
• Recommending optimal pricing strategies for individual customer profiles

AI acts as a powerful assistant to the underwriters, helping them evaluate risks more precisely and confidently. For instance, the need for health insurance or occupational risk coverage for gig workers can also be fulfilled with customized plans crafted with the help of AI-powered insights. The outcome? Enhanced scope of scalability, better pricing, faster decision-making and reduced risk exposure.

Faster, Fairer, and More Efficient Claims

Claims have long been a pain point, often drawn out, paperwork-heavy, and emotionally taxing for policyholders. AI is helping insurers transform the entire claims lifecycle, from intake and validation to assessment, resolution, and follow-up. A 2025 BCG report stated that AI is enabling up to 50% faster claims processing, 20–50% cost reduction, and, in simple claim cases, real-time resolution for as many as 70% of claims.

Let's take a simple example of an auto insurance claim to understand the role of AI in streamlining claims:

• At intake, AI-powered chatbots can guide the customer to report the incident by capturing videos and photos via a mobile app.
• For validation, AI models can instantly cross-check the claim against policy details and detect inconsistencies or signs of potential fraud.
• During assessment, image recognition tools help to evaluate vehicle damage from uploaded photos and generate repair estimates.
• For resolution, the system can recommend a settlement or flag complex cases for review, ensuring fairness.
• Post-resolution, AI can trigger personalized updates and feedback requests, helping insurers close the loop and improve customer experience.

Automating repetitive steps and offering intelligent insights enables teams to handle claims faster and focus more on empathy, accuracy, and customer satisfaction.

Personalized Customer Engagement

Customer engagement in insurance has traditionally been reactive. Insurers typically contact customers at policy renewal time or respond only when a customer reports a claim. But this model is evolving.

Today's customers are bringing expectations shaped by digital-native companies like Amazon, Uber, and Netflix. Customers have grown accustomed to frictionless, personalized experiences that anticipate their needs and offer relevant recommendations. An industry report found that 75% of consumers say they are more likely to purchase insurance from a company that offers personalized experiences.

This shift is pushing insurers to rethink engagement beyond transactional touchpoints. AI makes that possible by integrating with CRM, policy, and service systems to deliver timely, omnichannel, and relevant communication across the policy lifecycle.

For instance, AI recommends coverage updates when customers move to high-risk areas or triggers reminders ahead of seasonal risks like flood protection during hurricane season. AI also ensures consistent experiences across channels.

This level of personalization helps insurers not only meet rising expectations but also build trust, drive loyalty, and deliver standout customer experiences.

The Road Ahead

The most successful insurers are no longer asking if they should use AI; they're asking where and how it can best support their people and processes.

According to a 2025 Statista report, nearly half of global insurers plan to integrate AI into their operations this year. And it's not just for experimental pilots. AI is deployed to modernize core functions, creating real, scalable value across the enterprise.

But AI adoption must be thoughtful. U.S. insurers value transparency, explainability, and control. That means selecting AI tools that offer clear business logic, allow for human oversight, and align with ethical governance frameworks.

Conclusion: Human Intelligence, Enhanced

AI is not here to replace the underwriter, adjuster, or compliance officer. Instead, it equips them with better data, deeper insights, and more time to focus on serving customers, managing risk, and driving growth.

The most powerful transformation in modern insurance will not come from technology alone but from the synergy between intelligent systems and human expertise. An AI-first core platform for insurance can boost ROI and reduce the complexity of transformation. To realize this, insurers must build an AI-first culture, invest in explainability and ethics, and establish governance frameworks that empower humans and machines to work harmoniously.

Insurance is entering the Agentic Age. Intelligent, autonomous systems that can perceive, reason, act, and learn are redefining how insurance stakeholders operate, compete, and grow. This is not simply automation taken a step further. It is a structural shift that changes the speed, precision, and economics of the entire industry.

Agentic AI consists of intelligent agents that can sense changing conditions, interpret context, make decisions, take action, and learn from results autonomously. These agents orchestrate complex processes, uniting data, enterprise logic, and contextual memory to improve continuously.

Across the industry, scaled deployments of Agentic AI are beginning to deliver measurable results. In P&C, underwriting expense ratios will decline by 15 to 20%, and claims expense ratios by more than 15%. In life, underwriting costs will drop by more than 25%, with benefit expenses reduced by nearly 20%. Claims resolutions that once took weeks will be shortened to hours or less, and payment error rates will fall by more than 30%. These are not incremental gains but step-change improvements.

Agentic AI moves beyond workflow automation and analytics. It empowers systems to combine historical, contextual, third-party, and synthetic data with connected platforms to coordinate complex processes and make informed decisions. The result:

• Faster cycle times: Underwriting processes cut by up to 75%
• Improved retention: Customer loyalty increases by 10 to 20%
• Higher productivity: Output per colleague more than doubles
• Enhanced economics: Marginal cost trends toward zero while precision improves

Agentic AI enables firms to optimize price, product, experience, and operating economics simultaneously, at scale. This is something that was previously beyond reach.

Why it matters now: Markets are moving toward real-time, predictive, and adaptive operations. Firms that deploy Agentic AI early can capture structural advantages such as lower marginal cost, faster execution, and stronger retention that compound over time. Late adopters will struggle to close the performance gap and forgo learning curve effects.

However, many firms are not ready to capitalize on Agentic AI. Legacy technology, disconnected data, manual workflows, and fragmented governance can slow execution and block leverage. This capability debt will further widen the gap between leaders and laggards.

To help overcome such challenges, consider the following strategies:

• Design connected systems: Modernize infrastructure with orchestration layers, application programming interfaces (APIs), and cloud extensions to connect legacy cores to agentic systems.
• Rethink your operating model: Redefine roles, governance, and incentives to support enterprise-wide AI adoption.
• Create consistency: Standardize workflows and embed business logic to enable intelligent orchestration from triage to resolution.

These strategies are supported by five enablers that ensure sustainable scale and impact:

• Strategic alignment
• Organizational readiness and performance management
• Governance and risk management
• Process and workflow design
• Data and technology enablement

Agentic AI is not a future concept: It is here. The question for industry firms is whether you will lead or follow. This is a strategic decision, not a tactical one. Acting now will unlock superior economics, faster execution, and durable competitive advantage. Waiting means falling behind in a market that is rapidly accelerating away from traditional operating models. The time for decisive action is now.

For the full white paper this article is based on, click here.

The insurance industry has undergone rapid innovation over the last decade, but not all sectors within the industry have evolved equally.

Property & casualty (P&C) insurers, for example, have made impressive strides in digitizing and optimizing claims. Life insurance has been slower to modernize. This disparity has resulted in a significant innovation gap between life and P&C claims processing. It stems largely from fundamental differences in claim volume, complexity, and customer expectations. But as the gap grows, it becomes increasingly unsustainable in today's fast-moving, digitally driven world.

Outlined below are the key lessons life insurers can learn from P&C's digital transformation, as well as a road map for how life insurance carriers can accelerate claims modernization while preserving trust, empathy, and compliance.

Why the Innovation Gap Exists

P&C insurers handle millions of claims annually, many of which are low-severity, high-frequency events like fender-benders or storm damage. Keeping pace with claims volumes is what provided incentives for early investment in automation, AI, and self-service to help optimize processes for both the insurer and the insured.

In contrast, life claims are often low-frequency but high-emotion, and manual processes were deemed the most acceptable approach for these emotionally charged events.

Consumer expectations have since shifted.

Wider industry pressures – demographic changes, labor shortages, the pervasiveness of AI, and more – are further catalyzing this transformation. Gartner predicts that by 2026, 30% of enterprises will automate more than half of their network activities.

The call to action is clear: Life insurers must digitize to meet modern expectations without compromising on a customer experience that balances empathy, accuracy, and compliance.

Leveraging AI for Automation

P&C insurers have already built a foundation of speed and efficiency by embracing digital-first operations. From first notice of loss (FNOL) to straight-through processing (STP) and digital documentation, many previously manual claims processes are now automated, rules-based, and augmented with AI. The result is better fraud detection and faster triage at scale.

Major auto and home insurance carriers already use automated workflows and proprietary mobile apps to resolve minor claims blazingly fast. Insurers use these platforms to submit photos of vehicle or home damage, which AI tools instantly assess, use to estimate repair costs, and process in real time. In some cases, claims are approved and paid within minutes.

Alternatively, life claims remain labor-intensive and complex, with paper death certificates, manual policy validation, and disconnected systems leading to long delays. These laggard operations no longer align with customer expectations or enable operational sustainability.

The AI models that are widely used in P&C to assess claim complexity, detect anomalies, and flag fraud in real time can similarly be applied to life insurer workflows – flagging incomplete claims, triaging straightforward cases for fast-track approval, even personalizing communication based on behavioral or demographic data.

McKinsey anticipates that by 2030, more than half of claims activities will be automated.

Digital With a Human Touch

From mobile-first claims submission to real-time status update chatbots, many P&C carriers now offer seamless self-service options that keep customers happy and informed. This has reshaped customer expectations across all lines of insurance – and life insurance is no exception.

But the inherently emotional and often painful nature of life insurance claims make clarity, transparency, and speed essential when adopting practices from P&C.

Rather than being a one-to-one template for life insurance innovation, P&C's use of customer journey mapping and design thinking offers life insurers a model for where to begin when modernizing their operations. By mapping the end-to-end life claims experience – from the beneficiary's first contact to final payout – insurers can uncover and address friction points such as multiple document requests, long silences, or poor communications.

The X-factor for implementing these changes is that, alongside automation and personalization techniques such as instant document upload and multi-channel status updates, life insurers must also create precedents for enabling swift human intervention at key moments. For life insurance, the human touch must never be far away.

Tech solutions must then strive to make the process easier without making it feel cold. Automation should never come at the expense of empathy.

Intelligence Through Data and Ecosystem Integration

Advanced data usage has long been a defining feature of P&C claims transformation. Carriers routinely use third-party data – weather reports, IoT and telematics data, government records – to populate claims automatically, assess risk, and identify anomalies in real time, resulting in faster decisions.

Life insurers can achieve the same effect by integrating with government databases, obituary application programming interfaces (APIs), health records, and even social media, to validate deaths quickly and securely.

A Matter of Life and Death

The innovation gap between P&C and life insurance claims has finally become a solvable challenge, with the barrier to entry for automation, more empathetic customer experiences, and smarter, more connected data ecosystems lower than ever before.

By adopting the automation tactics honed by P&C insurers and anchoring them in the empathy that life insurance demands, life insurers can modernize claims in a way that enhances trust, improves efficiency, and delivers lasting value.

But life insurers must act now. Because reimagining life claims through a digital lens isn't just possible: It's imperative for long-term competitiveness and customer loyalty.