The flash flooding in Texas has, tragically, killed more than 100 people, and much-deserved finger pointing has begun about who all is to blame. As responsibility gets sorted out over the coming weeks and months, I think we need to recognize another aspect of the problem, one that goes well beyond local, state and federal authorities. We humans just don't think and behave right.

The flood happened in what's known as the most dangerous river valley in the U.S., but local and state officials have for years decided not to install early warning systems because they didn't think voters would approve of the spending. Lots of people discounted the forecasts of heavy rain that the National Weather Service issued in the days leading up to the floods.

If we can't even prepare for flooding in what's known as Flash Flood Alley, where can we get it right?

I'm really quite discouraged. I'm beginning to think human behavior may be intractable.

To be clear, I'm happy to point fingers at lots of people in authority. There was a massive failure to prepare here, as this story in the Washington Post describes.

State and local authorities not only didn't invest in warning systems; they also underestimated the logistical difficulties of alerting people in remote areas, even though cellphone coverage is known to be spotty and severe weather can obviously cause power outages. At the federal level, the DOGE chainsaw led to some 600 people being cut at the National Weather Service or taking early retirement this spring, and many experts say the lack of personnel likely contributed to the inadequacy of warnings about the Texas flood. 

The problem will get worse at the federal level, too, because the Trump administration says it will scale the Federal Emergency Management Agency (FEMA) way back or even eliminate it as part of a plan to "empower states." Already, the Trump administration has said it will, at the end of this month, cut off access to a data source that is considered to be crucial for hurricane forecasting. 

But even beyond the (many) failures of authorities, I think we have to acknowledge that people really aren't built to prepare for disasters. We reason based on our personal experience, and we get complacent because we haven't experienced a flood, a wildfire, a hurricane, a tornado and so on.  

I realize money is scarce in many areas and can understand why voters would be reluctant to invest in warning systems. I also realize that this flood was unprecedented in this particular area.

But we live in a time of unprecedented weather, and the problems are only going to get worse. So we need to do far better at making ourselves and our properties more resilient in the face of impending natural disasters. 

Yet I'm not sure we will. 

I'm not sure we can, as humans.    

All I can think to do is the sort of thing I've recommended in the past, so we can at least minimized the catastrophes. Insurers have a major opportunity to guide policyholders on how to mitigate risk, including possibly offering incentives through discounts on premiums. Insurers can also help communities of people work together. 

We won't solve the problem. We humans are hard-wired against a real answer, it seems. But we can have a serious rethink and at least try to do better.

That's the best I've got, I'm afraid. 

Cheers,

Paul

In this era of digital transformation, the insurance industry is shifting from traditional models to intelligent, customer-centric systems. A key driver is agentic AI—a form of artificial intelligence that exhibits autonomous decision-making, goal-oriented behavior, and the ability to act on behalf of users or systems.

Unlike conventional AI, which typically reacts to predefined rules or inputs, agentic AI systems think, plan, and act proactively. In insurance, this means not just reacting to claims or policy requests but anticipating customer needs, identifying risks in real time, and optimizing operations for better results. Let's explore how agentic AI is redefining the insurance value chain across three pivotal domains: risk, relationships, and results.

1. Rethinking Risk: From Reactive to Predictive

Risk assessment has always been the bedrock of insurance. Traditionally, this meant using actuarial tables, historical claims data, and statistical models. Agentic AI brings a new dimension to this process by analyzing real-time data and continuously learning from patterns to predict risks with greater accuracy.

Examples in action:

• Dynamic underwriting: Agentic AI can use live IoT data from smart homes, wearables, and connected vehicles to personalize risk scores. A home insurance policy might adjust coverage dynamically if the system detects that a home is unoccupied for a prolonged period, increasing fire or theft risk.
• Climate-aware pricing: AI agents can continuously monitor weather patterns, satellite imagery, and environmental reports to assess climate risks. Insurers can use this insight to offer targeted protection products or adjust premiums in high-risk zones.
• Fraud detection: Traditional fraud detection is often rule-based and static. Agentic AI can evolve its fraud detection logic, identifying suspicious activity by comparing claims across demographics, behaviors, and locations in real-time.

2. Transforming Relationships: From Policyholder to Partner

Today's insurance customers expect seamless digital experiences, personalized communication, and empathy in interactions. Agentic AI is enabling insurers to move beyond customer service chatbots and offer human-like, proactive engagement.

How this looks in the real world:

• Personalized coverage advisors: AI agents can analyze a customer's lifestyle, income, dependents, and goals to recommend the most suitable policy mix—even adjusting recommendations as life circumstances change.
• Proactive claims guidance: In the event of an incident, an agentic AI system can automatically initiate a claim, guide the customer through the process, schedule inspections, and communicate updates—creating a frictionless experience.
• Wellness and prevention: For health and life insurers, AI agents can engage policyholders with personalized wellness tips, nudge them to take preventive screenings, or offer rewards for healthy habits—all aimed at lowering long-term risk.

By moving from transactional to advisory roles, agentic AI helps insurers build stronger, longer-lasting relationships with their customers.

3. Driving Results: Efficiency and Innovation at Scale

Beyond customer engagement and risk prediction, agentic AI offers powerful opportunities to transform operational efficiency and drive business results.

Here's how it's making an impact:

• Automated policy administration: From issuance to renewal and policy updates, AI agents can manage back-end tasks with minimal human intervention, reducing errors and turnaround times.
• Claims automation: Agentic systems can gather documentation, assess damages via image recognition, validate policy coverage, and even authorize payments—often within minutes. This reduces costs and significantly improves customer satisfaction.
• Smart portfolio optimization: By continuously analyzing market trends, customer behaviors, and product performance, agentic AI can recommend changes to product pricing, coverage tiers, or distribution strategies—helping insurers stay competitive and profitable.

The result is a more agile, responsive, and customer-focused business model—a far cry from the legacy systems and slow processes that have plagued the industry for decades.

A Word of Caution: Ethics, Oversight, and Human Touch

While agentic AI offers immense potential, it also raises important considerations. These systems must operate within ethical and regulatory boundaries, especially when making decisions about pricing, claims denial, or coverage eligibility.

Transparency, accountability, and human oversight remain essential. Insurers should implement explainable AI frameworks and ensure that final decision authority rests with trained professionals in sensitive or complex cases.

The Future of Insurance Is Agentic

As agentic AI matures, it will become the co-pilot for underwriters, claims handlers, actuaries, and customer experience teams—augmenting human intelligence rather than replacing it. Insurers that embrace this shift early will unlock new levels of speed, precision, and personalization that traditional systems can't match.

Whether it's preventing risk, improving customer loyalty, or scaling operations, agentic AI is no longer a futuristic concept—it's a competitive advantage today.

Insurance is no longer just about protecting what's valuable—it's about predicting, preventing, and proactively managing the journey. And with agentic AI, that journey just got a lot smarter.

In an industry built on long-term relationships and brand trust, insurers are facing a generational shift that's impossible to ignore. Gen Z is stepping into adulthood with a new set of expectations, behaviors, and decision-making patterns that many insurers simply aren't prepared for.

This is a digitally native generation that grew up on mobile-first experiences, real-time feedback, and hyper-personalized content. It's no wonder that more than half of this cohort feels anxious or overwhelmed at the thought of dealing with insurance. It seems well out of their wheelhouse.

That's a problem—but it's also an opportunity.

Why early engagement matters with Gen Z

Most 20-somethings aren't thinking about insurance in a holistic way. But they are driving, renting, starting jobs, and forming financial habits. These life moments come with insurance needs—auto, renters, life, and beyond—and represent key opportunities for engagement.

Reaching younger consumers early with products that feel relevant and easy to understand helps establish trust and sets the stage for long-term relationships. Younger customers who buy into policies earlier tend to be more profitable and loyal.

Yet many carriers are still relying on outreach strategies that haven't evolved in decades. The first step in pinpointing the right way to connect with this important generation is gathering insights that are timely, accurate, robust, and built to reflect how Gen Z actually engages with brands and content.

Why traditional consumer insights research falls flat

Our research shows that Gen Z's purchase habits are incredibly complex, using social media, for example, to discover products and services, but then turning to other channels for purchase. In fact, only 18% complete the purchase directly through social channels, while 88% buy via online marketplaces (Amazon, Etsy, etc.) and 75% through brand websites. That's just one small example of how hard it can be to find that sweet spot with Gen Z.

Traditional survey methods and clinical, outdated feedback tools don't resonate with this group: they expect, at minimum, real-time interactions, seamless UX, and hyper-personalized content. In many cases, the problem isn't the product, it's the way insurers are trying to understand and engage their audiences.

To reach them effectively, insurers need to reframe how they approach research. That starts by engaging them where they are: on mobile devices, in the moment, and on their own terms. Static, 30-minute surveys won't cut it. Instead, agile approaches that mimic the way young people already communicate, via text, voice, and mobile-first platforms, are far more likely to spark real dialogue.

It's also about more than just the channel. Gen Z craves authenticity. Research must be designed to create a conversation, not an interrogation, and to build trust through transparency. When young consumers feel heard and respected, they're more willing to share meaningful, thoughtful feedback that insurers can actually act on.

Quick wins for insurers doing market research

You can benefit from:

● Text-first surveys: Reach Gen Z where they already are—on their phones.

● Continuing engagement: Regular check-ins build loyalty and provide real-time insight into customer needs.

● In-the-moment claims feedback: Moments of vulnerability can provide opportunities for empathy, rather than just data collection.

● Tailored incentives: Offer rewards that feel immediate, personal, and worth their time.

Making insurance more relevant

When guided by the right insights, insurers can design offerings that feel tailored to Gen Z's lifestyle and mindset. Some companies are already experimenting with new ways to make insurance feel more relevant, such as:

● Life insurance with wellness perks: Bundling policies with benefits like cancer screenings or fitness discounts makes life insurance feel like a proactive health move, not a grim obligation.

● Meaningful perks and rewards: Offer benefits that align with Gen Z's values and lifestyle, like partner discounts, sustainability incentives, or access to exclusive experiences, and make sure they actually know about them. Visibility is just as important as the perk itself.

● Empathetic claim follow-ups: A traumatic experience shouldn't be met with a sterile, 30-minute survey. A quick, thoughtful check-in can go a long way in showing care and building trust.

Ultimately, relevance starts with understanding. Modern research methods help insurers uncover what matters most in the day-to-day lives of young consumers. With the right insights, every decision, perk, or touchpoint can carry more weight and meaning.

Don't overlook the influencers

Insurance decisions are rarely made in isolation. Gen Z often relies on parents, friends, or agents/brokers for guidance. That means insurers need to look beyond the individual and understand the broader decision-making landscape.

There's also a major opportunity in engaging agents and brokers. These professionals are often the bridge between the company and the customer. Building research communities around these professionals can surface valuable feedback on tools, messaging, and processes that directly impact both the agent and customer experience.

Better research leads to stronger connections

Insurers are under pressure from rising rates, increased climate risk, and new competitors in the insurtech space. To stand out, carriers need more than clever messaging. They need a clear, current understanding of how different audiences make decisions, especially the next generation of policyholders.

That understanding doesn't come from outdated surveys or one-time touchpoints. It comes from continuing, human-centered research that's designed for how young people actually communicate today. By investing in more modern methods, insurers can build credibility with the next generation, uncover actionable insights, and move from transactional interactions to lasting relationships built on relevance, trust, and mutual value.

The commercial auto insurance landscape is facing an inflection point. While fleets rapidly embrace telematics technology and generate unprecedented amounts of driving data, a surprising disconnect persists between insurers that desperately need this information and fleet operators that possess it. According to SambaSafety's 2024 Telematics Report, this gap represents the industry's greatest challenge and its most significant opportunity.

Perception Versus Reality

One of the report's more notable findings reveals a fundamental misunderstanding that's stalling progress: 75% of commercial insurers believe convincing fleets to share telematics data is their biggest hurdle, while 74% of fleets that don't share data say it's simply because they were never asked. This communication breakdown prevents meaningful partnerships from forming.

The issue becomes even odder when considering fleet readiness. Currently, 80% of fleet respondents monitor a large portion of their vehicles, and satisfaction scores average four out of five for their telematics providers. These fleets aren't resistant to technology—they're already deeply invested in it. What's missing is the bridge between their data and insurers' analytical capabilities.

Emerging Technologies at the Forefront

So much more than GPS tracking, the telematics landscape is rapidly evolving beyond GPS basics. The telematics report from SambaSafety shows that while 77% of fleets use GPS tracking, over 50% have adopted camera systems—a significant shift toward more refined risk assessment tools. These cameras aren't just operational aids; they're becoming critical legal defense mechanisms against nuclear verdicts, which peaked at a median of $23.8 million in 2023, according to the Institute of Legal Reform (ILR).

More tellingly, 51% of fleets plan to add new telematics devices or providers in the next year, creating an expanding universe of data sources. For insurers, this presents itself as an opportunity and hurdle to undertake. The challenge lies in accessing this data and developing the infrastructure to ingest, normalize and analyze information from multiple providers and device types.

In recent conversations, artificial intelligence and advanced analytics have become key differentiators for competitively assessing risk. As the report notes, "The growing capabilities of AI and its ability to gather insights will drive commercial lines insurers to prepare their data infrastructure and expand their telematics experience." Insurers leveraging AI to transform raw telematics data into actionable risk insights will gain a significant competitive advantage.

Infrastructure Reality Check

There's a growing infrastructure gap today that is daunting for many insurers. Only 25% of commercial insurers categorize themselves as fully capable of handling large amounts of telematics data, while over 33% acknowledge their infrastructure needs enhancement. This technical readiness challenge is compounded by resource constraints—58% of insurers cite lack of resources as a primary barrier, up dramatically from 32% in 2023.

The solution for insurers involves building strategic partnerships, which are becoming increasingly popular. Carriers recognize they can't produce everything in-house, regardless of capabilities and size. Whether partnering with data aggregation, risk scoring, benchmarking or training content, successful insurers use external partnerships as building blocks for capability expansion.

The Path Forward to Transformation

One of the most encouraging trends is the rise of dedicated telematics teams. The percentage of commercial insurers with dedicated telematics teams jumped from 27% in 2023 to 60% in 2024. These teams are taking a multi-disciplinary approach, with loss control leading the charge (47%), followed by underwriting (23%) and business line units (23%).

This organizational evolution reflects telematics' expanding role beyond simple data collection. Modern telematics teams handle vendor management, business function training, data preparation, risk pricing and segmentation—essentially becoming the central nervous system for data-driven insurance operations.

To capitalize on the telematics opportunity, insurers must focus on four key areas:

Share: Move beyond simply requesting data. Explain how, as an insurer, you will use the data and what benefits fleets will receive. Create feedback loops that provide fleets with actionable insights from their data.

Provide incentives: Develop financial incentives that align broker and policyholder interests with telematics adoption. Current programs often lack sufficient motivation for widespread adoption.

Prepare: Invest in data infrastructure, analytical capabilities and strategic partnerships— the volume and variety of telematics data will only increase.

Communicate: Foster transparent dialogue between insurers, brokers and fleets. Many adoption barriers stem from misunderstanding rather than fundamental resistance.

The Strategic Advantage

Commercial auto profitability continues to decline, with increased litigation, distracted driving and claim severity threatening sustainability. Telematics offers a proven path to risk reduction—72% of fleets report reduced crashes and claims when combining telematics with training programs.

The question isn't whether telematics will transform commercial auto insurance but rather which carriers will emerge as leaders and which will struggle to catch up. With 82% of commercial insurers already having some level of telematics adoption, the race is on to convert experimental programs into competitive advantages.

The data makes it clear: Fleets are ready, technology is maturing and the benefits are proven. Industry leadership is needed to bridge the communication gap and unlock telematics' full potential. The insurers that act decisively today will shape tomorrow's commercial auto landscape.

SambaSafety and the IoT Insurance Observatory are gathering insights for the 2025 Telematics Report. You can participate in the 2025 survey here.

The National Health Care Anti-Fraud Association (NHCAA) and the Coalition Against Insurance Fraud (CAIF) have repeatedly named provider fraud the most acute pain for the health insurance industry. The sector's annual losses from dishonest medical providers are alarming: NHCAA's estimate is $54 billion, CAIF places it as high as $105 billion, and some government agencies suggest it could be more than $300 billion.

The enormous costs of provider fraud result from its multiple devastating impacts. From the financial standpoint, illegitimate and inflated payouts break the health payer's claim reserves and lead to skewed cost forecasts, with growing reserves commonly leading to a rise in premiums and the loss of competitive positions. Operationally, fraud distorts the picture of care usage, making it harder for payers to fairly assess health risks and manage chronic conditions — but you know all that already.

Notably, when I discussed provider fraud concerns with my clients in health insurance, many cited the loss of trust between the payer and providers as the worst outcome. Having once faced a previously unknown scheme from a certain provider, the payer has to institute stricter controls across the entire network. Every new incident escalates administrative and IT costs (again, leading to higher premiums) and slows claim processing due to extra oversight. As fraud schemes evolve constantly, the payer's investments in protective measures are growing exponentially.

What's So Challenging About Detecting Health Provider Fraud

So far, most of the health payers' conventional methods of combating provider fraud have been fairly ineffective. The cost of fraud, when adjusted for inflation, has nearly doubled over the last 30 years. And with the fraud rates in insurance showing the greatest rise among all industries, it's clear that fraud safeguards that have worked for other domains failed to aid health payers specifically.

Why so?

The primary challenge is constantly evolving fraud schemes. This is by far the biggest concern among my clients, and I came across the same sentiment in multiple studies: For example, FRISS, in its 2022 Insurance Fraud Report, names keeping up with new trends in insurance fraud as the payers' top hindrance. Indeed, even if a robust solution to some scheme comes quickly, it loses its edge as soon as new tricks appear. As providers are becoming increasingly creative, payers are forced to respond with better, more versatile fraud detection tools.

Plus, we have the inherent complexity and specificity of medical data, which holds back the development of effective health fraud detection algorithms. Interpreting and verifying health claim evidence like medical images and lab test results has traditionally required deep professional expertise. But even for investigators with strong medical background, it's too easy to overlook visual and technical inconsistencies in highly specific imagery, especially when inspecting large-volume, multi-format submissions. These are the ideal conditions for misrepresenting diagnoses and falsifying medical necessity.

The decentralized nature of insurance-relevant data further complicates the story. With open APIs and automated reconciliations, it became easier for payers to cross-reference claims with federal databases and collaboratively detect schemes like double billing. However, no traditional analytics tools can recognize intricate, multi-tier schemes like referral collusions and kickbacks.

AI to Put an End to Provider Fraud — Or Will It?

Rule-based fraud detection tools have been here for decades to help health payers spot known types of provider fraud, like billing for the ineligible and unbundling. But rule-based tools can't address syndicated provider offenses and tech-supported schemes like medical image tampering, so by design, they are unfeasible for sufficient protection.

Advanced tools powered by artificial intelligence (AI) brought health insurers something old-school stuff could never offer. With AI's ability to continuously match millions of diverse medical data points, recognize hidden patterns, and instantly flag suspicious outliers, payers can now address many of the previously untamable types of fraud, including media forgery and organized collusions. Intelligent algorithms can study complex healthcare concepts, reason on the necessity and relevance of medical procedures, and spot inconsistencies in claims as humans do. More importantly, AI engines can continually learn from fraud patterns and get smarter over time, meaning payers can expect a steady growth in fraud detection accuracy.

But does this mean AI solutions keep up with the evolving pace of health insurance fraud?

Alas, they can't.

Just like traditional tools, intelligent systems can't foresee emerging types of fraud. Take a recent, frustrating example. Advanced claim analytics powered by machine learning (ML) have been in use for roughly a decade and have proven to be effective in detecting sophisticated fraud schemes. However, the algorithms behind these tools weren't designed to capture brand-new frauds like believable medical image fakes and convincing abusive narrations enabled by generative AI (GenAI). So, with GenAI at their fingertips, providers are once again miles ahead of payers in 2025.

AI Tool Stack for Efficient Provider Fraud Detection in 2025

At this point, my health insurance clients usually ask:

"OK, we can't respond to what's behind the horizon. But what technology do we need to address what's already here?"

Below, I share a minimal tool stack that will help health payers establish viable protection against healthcare provider fraud in 2025. Predictably, we have to fight fire with fire — you'll need AI to recognize AI fakes, but it is also effective for battling old-school fraud schemes.

Based on my estimates, implementing this multimodal toolset can bring health payers up to a 3x increase in provider fraud detection rates and a 20% to 90% reduction in fraud-associated losses. McKinsey analysts suggest that large health insurers can expect $380–$970 million savings in total claim payouts for every $10 billion of revenue with the current AI capacity.

ML-powered behavioral analytics to detect fraudulent provider actions

Machine learning models can identify anomalies in billing patterns, deviations in medical service frequencies, and non-obvious patient visit overlaps. This works great for exposing the most frequent types of provider fraud, such as upcoding, unbundling, phantom billing, and repeated charges for unnecessary or non-performed services. The purpose of behavioral intelligence tools is simple: understand how legitimate providers act and flag those who don't play by the rules.

Behind the scenes, ML algorithms study historical claim data, provider actions, and "normal" behavioral patterns across specific geographies and clinical specializations. Over time, they self-construct individual behavioral baselines that are unique for each provider. Once the baselines are set, the models can accurately recognize and classify any outstanding events. Early adopters of ML-powered behavioral analytics systems report a 60%+ increase in fraud detection rates with a twofold decline in false positives.

To automate the behavior diagnostic cycle end to end, such solutions need a broad stack of smart components. My colleagues from the data science team at ScienceSoft suggest unsupervised and supervised ML models for clustering and detecting natural groupings, outlier detection models for surfacing deviations, diffusion models for capturing time-based changes in provider conduct, and smart notification engines for issue reporting to fraud investigators. Investigator dashboards should provide a real-time overview of the raised flags with traceability to source provider data.

I know this may sound like a multimillion-dollar investment, but based on my experience, building anomaly detection solutions is one of the most affordable insurance AI initiatives. At ScienceSoft, we have managed to deliver the entire stacks of tailored models within the budget of $100,000–$250,000. Off-the-shelf behavioral intelligence tools like Provider Prepay FWA Detection by Shift Technology can be quicker and cheaper to implement, but they come with accuracy and integration tradeoffs.

Intelligent image analysis tools to recognize forged claim evidence

Medical image intelligence tools will help you catch edited, reused, staged, and entirely fabricated visual evidence. These tools are valuable for their ability to reveal technical forgery that even human claim reviewers with deep medical expertise might overlook.

Such tools serve a range of specific purposes. First, smart algorithms (at ScienceSoft, we use convolutional neural networks or transformer-based neural networks) inspect image metadata like device signatures, editing trails, and timestamps to verify image authenticity and expose suspicious manipulations. Next, they compare submitted images against the payer’s claim archives to find duplicates used in other cases. Finally, they analyze visual noise patterns, compression artifacts, and pixel anomalies that signal tampering. When an image contains any inconsistencies (e.g., a supposedly “new” MRI scan has the same shadows as one from last year’s claim or an X-ray has mismatched anatomy or signs of cut-and-paste), the solution flags it for manual inspection.

In ScienceSoft’s recent dental image analysis software project for medical insurance, we went a step further and combined CNNs with machine learning algorithms for autonomous claim validation. This way, the system could cross-reference image parameters and embedded text with provider filings and decide on claim eligibility outright. Remember that you may also need dedicated background algorithms to unify image file formats and establish standardized image processing flows.

When it comes to the accuracy of such engines, it largely depends on how rich and representative your model training dataset is and how deeply the model is tailored to your review workflows. Well-developed models can show up to 95% accuracy in detecting image falsifications – a rate that's not attainable with any commercial models.

LLM-supported document review to spot abusive provider narratives

Health payers know firsthand how tricky are providers' words — long, tangled justifications buried in a sea of medical jargon. One way to uncover abuse in complex provider narratives at scale is to apply a medical document review tool powered by large language models (LLMs). In simple terms, LLMs are a sub-type of GenAI that power tools like ChatGPT — the AI algorithms that can process natural language requests and form human-sounding responses.

In our case, LLM models can quickly parse massive volumes of provider notes, medical records, appeal letters, and other textual data and detect inconsistencies and subtle lingual tricks that may indicate fraud. For instance, they can pick up vague and medically incoherent documentation, contradictions in treatment timelines, and mismatches between diagnoses and procedures. Such tools can also highlight clinical term misuse, which could be used to justify higher-cost billing codes.

The best thing about LLMs is arguably their ability to explain their output in regular human language. For example, a health claim reviewer can ask an LLM to summarize high-volume documentation for complex surgery and explain suspicious abstracts in simple words. Early adopters of LLM-supported tools for detecting fraudulent claims report dramatc gains: 90% quicker claim reviews, up to a 400% increase in reviewer capacity, and a 5–20% reduction in illegitimate payouts.

You don't need to build your own LLMs from scratch. Applying retrieval augmented generation (RAG) and prompt engineering to commercial LLMs is usually enough to obtain an accurate solution tailored to the payer's business-specific data. For provider fraud detection specifically, I recommend opting for a healthcare-specific LLM like Med-PaLM or BioBERT. Such models are trained on specialized medical corpora and care delivery examples, meaning you can roll them out without costly "upskilling." Implementation costs may vary from $150,000 to $500,000+, depending on the chosen approach to LLM enhancement.

Network intelligence solutions to reveal provider collusions

Intelligent network analytics help uncover a less frequent but highly damaging type of medical fraud — organized healthcare provider collusions. AI engines can automatically map relationships between providers, spot factors like shared addresses, financial ties, coordinated referrals, and circular service flows, and identify groups of bad actors working together.

To find suspicious care provider hubs across multi-layer networks, such systems use network science models, including graphs and community detection algorithms. Like with medical image analysis tools, you can incorporate prescriptive machine learning into these solutions to automate decision-making. For instance, ML algorithms could auto-classify the revealed network patterns as legitimate, borderline, or clearly fraudulent and trigger relevant follow-ups.

The success of network intelligence systems has always depended mainly on the breadth of provider data they access. Ideally, such software should be able to monitor dynamic changes in provider profile info, patient encounter logs, referral trails, claims data, and external feeds (think provider registry info and corporate activity details from public databases). A smart move is to prioritize integrating the solution with diverse internal and third-party systems. Some health insurance providers ultimately choose custom software engineering due to limited integrations in ready-made products, which results in lower efficiency.

Also, network intelligence systems rely on visual representation way more than other tools in the stack. Such solutions should have interactive charts reflecting multi-tier network connections, scatterplots depicting concentrated provider links, and temporal graphs showing how provider relationships evolve over time. Investigators should also be able to drill down to granular details.

Maximizing AI Accuracy and Compliance While Reducing the Costs

Among the latest insurance AI stats, the following numbers seem the most representative to me: While 84% of health insurers currently use AI/ML in some form, only 14% trust machines in the actual decision-making, and of those who do, 97% encounter challenges related to AI accuracy. The infamous case of UnitedHealth, which was legally pursued for using low-precision AI models to deny care, undermined member trust in the entire sector, pushed regulators to institute stricter oversight, and taught payers to prioritize algorithm accuracy. Many of the health insurers I've talked to cited concerns about AI model precision — and sufficient proofs of that precision — as their biggest barriers to adopting intelligent fraud detection tools.

That being said, from the technical angle, both concerns are far-fetched. The solutions for maximizing AI accuracy and transparency are here; they just require extra investments, which not every health payer wants to and can afford to bear. Unfortunately, as I always tell my clients, any attempts to go without these will inevitably cost you the efficiency of the entire AI system.

There are still ways to optimize expenses, though. Here are some proven tactics:

• As I mentioned, the volume and versatility of data used for AI model training are the key drivers for fraud detection accuracy. Still, in some cases, the available data is just too scarce for meaningful representation. Let's say you only have a few examples of claims for rare back surgeries. One way to give your algorithms more patterns to learn from is to train a generative AI model on real claims and apply it to synthesize realistic data for similar scenarios. For example, GenAI can produce claim files with the same procedure code but different treatment programs and billing scenarios. GenAI-supported data synthesis comes 30% cheaper than acquiring, standardizing, and labeling real data.
• Synthetic data augmentation can also be a budget-friendly method of source data debiasing. Synthetic data for underrepresented member groups, providers, and fraud types should reflect hypothetically accurate care paths and charges. This way, intelligent algorithms don't replicate historical bias and can learn to better distinguish legitimate differences from fraud. For you, this means sharper detection of provider fraud and minimized risks of unintended discrimination.
• In my recent paper on AI for health insurance underwriting, I elaborated on cost-effective ways to achieve AI transparency sufficient for regulatory compliance. The key point is that big tech players are aware of regulatory scrutiny, so major AI platforms like Azure Machine Learning and Amazon SageMaker come with built-in explainability toolkits. By using their go-to AI development frameworks, you avoid costly model engineering from scratch and get the fully interpretable fraud detection logic from the onset.

Contributors: Stacy Dubovik, financial technology researcher, ScienceSoft; Alex Savanovich, senior data scientist, ScienceSoft

Over the past decade, carriers have modernized their core systems, stitched together integration layers, and deployed business process management (BPM) tools. These efforts brought efficiency and scalability, but they weren't built to support what may be the most significant shift in enterprise technology since the microprocessor: AI.

As AI moves from pilot to enterprise-wide production, regulators like the NAIC are focusing on governance and explainability, and many insurers are discovering that their current architecture simply wasn't designed for this new world.

Insurers don't need to throw away what they've built. But they do need a new layer of architecture, one that enables orchestration of automation, AI, and digital servicing independent of the core. This is driving a shift toward coreless modernization.

What Is Coreless Modernization?

To be clear, coreless doesn't mean going without a core system. It means liberating the enterprise from the limitations of the core. This virtualized layer acts like a semantic graph, allowing systems and applications to operate on real-time data across the enterprise without duplication or disruption.

While traditional legacy transformation often relies on expensive projects involving replacing and retiring core systems, coreless takes a different approach. It uses an event-driven orchestration layer, data fabric, and modular AI services to externalize business logic, workflows, and decision-making to a more flexible, intelligent layer while leaving the core system intact.

Core systems remain the system of record, but orchestration of servicing, underwriting, distribution, and engagement are able to be moved to the abstracted layer that's decoupled from the legacy constraints that typically slow innovation.

In effect, this creates a hollowed-out legacy environment, one where modern capabilities operate in sync with the core system, extending its utility without overloading it or requiring that it be replaced.

Why Now?

Three fundamental shifts are making coreless possible:

• Data Fabric Maturity: Insurers now have the tools to build a unified data layer across systems, without duplicating or displacing source systems. This makes it possible to expose business state and automate real-time workflows without overwhelming the core.
• AI-Driven Decision-Making: With agentic AI, intelligent automation can now handle more than just simple tasks. Complex underwriting, fraud detection, and case routing can run outside the core with full lineage, audibility and traceability.
• Composable Architecture: Agentic orchestration allows new journeys and products to be assembled in weeks, not years, without being bottlenecked by monolithic legacy dependencies.

These shifts aren't abstract trends. They are direct responses to mounting pressure across the insurance enterprise. Distribution leaders want faster partner onboarding. Product teams need to launch offerings in weeks, not quarters. Compliance officers and regulators demand auditability. CIOs are expected to scale AI safely without triggering full system rework. Traditional architectures can't keep up. Coreless gives insurers a way to break through without breaking what already works.

What Makes Coreless Different?

The key distinction is architectural: Coreless reinvention introduces a unified substrate for orchestrating AI, automation, and digital workflows without replatforming. Where BPM tools and application programming interface (API) middleware attempt to route tasks across siloed systems, coreless provides an explainable orchestration substrate that can:

• Ingest real-time business events to trigger AI and automation flows
• Log every decision for audit and NAIC compliance
• Scale horizontally without relying on BPM or synchronous APIs

A Blueprint for Reinvention Without Disruption

Every decade or so, enterprise technology brings a defining architectural shift. Mainframes gave way to client-server architectures. Legacy policy administration systems (PAS) evolved into cloud-based cores. This next shift is being shaped by AI.

Some insurers are already applying coreless principles in practice. One insurance carrier began orchestrating new workflows around its existing core systems, rather than within them. This shift allowed the company to significantly reduce policy issuance times and achieve sub-400 millisecond response times, all without rewriting foundational infrastructure or disrupting their core.

Composable. Co-existent. Designed for Flexibility.

The concept of coreless is built on the principle of separation of duties in every module, from decision-making engine to data fabric and AI orchestration. These can operate independently or together, depending on your needs. This means insurers can retain existing investments, whether that's a PAS, customer relationship management (CRM), or claims system, and still adopt AI capabilities to modernize intelligently.

With agentic AI, embedded experiences, and real-time orchestration on the rise, coreless modernization is becoming the new path forward. It's a pragmatic response to the realities of building, scaling, and governing AI-powered insurance operations in today's landscape. For insurers navigating legacy complexity while pushing toward digital agility, Coreless offers a viable alternative for modernization, one that complements what exists, while enabling what's next.

If you are trying to scale AI inside a legacy-bound stack, you're already behind. Coreless isn't the future. It's today. Start embracing it now.

As digitization reshapes every link along the insurance value chain, one essential component still lags: the payment experience. 

For policyholders, the payment process is one of the most frequent and tangible touchpoints with their insurance carrier. But outdated payment systems and non-specialized call center representatives often result in a frustrating experience for policyholders seeking an accurate understanding of their payments.

Modernizing the payment experience presents an opportunity to foster customer goodwill in the insurance industry. However, regulatory nuance and capital demands of financing premium payments make this an area where insurance carriers, managing general agents (MGAs), and insurance agents benefit from innovative technology and strategic partnerships. Insurance premium finance companies have evolved from an industry utility into allies helping insurers meet customer expectations and build competitive advantage.

The traditional, narrow view of premium finance has been purely functional, missing the broader strategic potential. Today, while premium finance companies deliver fully integrated, digital-first payment experiences, only a few are forward-thinking enough to incorporate the latest cutting-edge technology. Some carriers have explored in-house financing models, but most find partnering with the right third-party premium finance company delivers quantifiable results, including delivering payment innovation reliably and expediting the cash cycle.

Speed and Flexibility Without the Capital Burden

Financing premiums in-house requires considerable capital reserves. It also necessitates loan servicing capabilities, regulatory and financing expertise, and significant exposure to credit risk. For many insurers and MGAs, this is simply not a core element of their business model.

Leading premium finance companies provide a deep specialization in both financing and customer service. These firms are built specifically to handle the complexity and expectations of the premium finance process, from precise billing calculations and cancellation workflows to high-touch borrower support. Their service teams are trained to work with policyholders who may not be familiar with financing mechanics. This level of customer service is difficult to replicate internally without additional cost burdens and ensures that policyholders receive timely, expert support that reflects positively on the insurer's brand. In many instances, the premium finance company's customer support team becomes a main contact for the insurer's agents and customers. For commercial policies, their service specialization can be the difference between a closed sale and a missed opportunity.

Elevating the Customer Experience With Innovative Payment Solutions

Customer experience is a key differentiator in an increasingly competitive insurance market. Policyholders want to manage their policies and payments the same way they manage many financial aspects of their lives: online and on mobile.

Forward-thinking premium finance companies have responded with platforms that integrate seamlessly into the quote-to-bind process and policyholder portals. They incorporate technologies that have become expected in payment processing, such as electronic signatures, auto-pay and online account services.

Some premium finance companies further streamline the payment process by incorporating innovative solutions into antiquated methods. For example, they deploy secure, single-use QR codes on printed and emailed payment notices. These codes directly link to the customer's personalized, secure payment portal, thus eliminating the need to log in or manually enter account details. This noticeably reduces friction for customers who still receive paper correspondence or who prefer traditional billing formats, while maintaining security and compliance.

Another innovation is incorporating opt-in text message payment reminders with shortened, secure URLs. These messages allow policyholders to access their payment portals with a single tap, improving on-time payment rates while reducing cancellations due to missed installments. The convenience of mobile-first communications reflects how today's consumers prefer to interact with service providers of all kinds.

Building these cutting-edge, compliant financing capabilities internally is a resource-intensive project for insurance carriers, which distracts from an insurer's core objectives. Premium finance companies have already made these investments, including API-based integration with agency management systems, co-branded borrower portals, and automated document generation.

Final Thoughts: Rethinking Payment Processing as a Strategic Advantage

In an era where customer expectations are rising and digital transformation defines competitiveness, the payment and financing experience has become a strategic opportunity. Historically overlooked, this metaphorical "last mile" of the insurance process can be a key differentiator for carriers, MGAs, and agencies willing to modernize their payment processes.

insurance organizations gain more than capital support by affiliating with specialized premium finance companies. They gain access to turnkey technology, compliance expertise, and customer service infrastructure built specifically for the unique demands of insurance financing. These partners enable insurers to deliver an innovative payment experience without the financial and operational burden.

As the industry continues to evolve, those who rethink payment and financing as a core component of the customer journey will be best positioned to drive loyalty and compete at the speed of today's market.

In this ever-changing technological landscape, it's important to continuously reevaluate consumer payment options. Modernizing the payment experience benefits both insurers and their customers.

Across the insurance industry, carriers are quietly losing ground—not to market shifts or rising risks but to their legacy policy administration systems (PAS). These aging platforms aren't just inefficient; they hinder innovation, frustrate employees, and limit insurers' ability to respond to customer needs, regulatory change, and competitive threats.

Complex, Costly, and Inflexible

Legacy policy administration systems are often built on proprietary frameworks developed on top of traditional platforms. These custom-built architectures are typically rigid and complex, requiring specialized—and often costly—expertise to maintain or enhance. This makes adapting or evolving the system challenging without deep knowledge of the underlying custom framework. Every implementation, enhancement, or product launch adds cost, complexity, and reliance on niche skills. The result is spiraling budgets, rigid workflows, and delays in going to market.

Compounding these issues, many older systems lack standardized debugging tools. Logs are fragmented, and troubleshooting often requires manual searches across multiple components and microservices.

Another challenge is PAS providers that offer only partial modules. This pushes insurers to adopt multiple systems written in different languages, which rarely integrate cleanly and then require additional data lakes, middleware, and maintenance layers. As a result, transactions can't be easily traced across systems. This blocks efficiency and limits technologies like AI and predictive modeling.

The true cost isn't just technical debt—it's missed opportunities. As one insurance executive put it, "Every enhancement or product rollout feels like a battle, sapping both budget and morale." The impact extends across underwriting, claims, billing, and service—dragging down the customer experience and hampering growth.

The Competitive Divide Is Widening

The industry is at an inflection point: Modernization is no longer optional, it's a competitive necessity. Insurers that modernize gain real-time access to data, faster product deployment, and greater agility to respond to regulation and market shifts. Cloud scalability, API-first design, and embedded analytics enable them to tailor experiences and drive operational excellence.

Consider a mid-sized carrier running a heavily customized legacy PAS. When new regulations demanded fast product adjustments, rigid workflows and hard-coded rules made a timely response impossible. Product timelines stretched into quarters. Competitors with modern platforms capitalized.

This scenario is common. Carriers without modern systems face costly delays, limited insight, and reduced responsiveness. The fallout: missed revenue, agent frustration, and customer churn—all of which undermine competitiveness.

Capabilities Insurers Need to Stay Agile and Compliant

While policy administration systems have long been "sticky" due to high replacement costs and the risk of operational disruption, today's pressures from artificial intelligence (AI), regulatory complexity, and speed to market are forcing insurers to reconsider the efficacy of their legacy systems.

A modern PAS must enable seamless communication across all core insurance functions—from rating and underwriting to broker and client portals, reinsurance, actuarial reserving, billing, claims, and regulatory filings. The key to achieving this is an open, configurable platform that unifies these disparate components into a single, integrated system.

Such platforms should be built on industry-standard programming languages and frameworks. This broadens the developer pool, accelerates development cycles, reduces maintenance complexity, and future-proofs the system for ongoing innovation. Configurability and scalability become essential, enabling insurers to adapt quickly in a landscape marked by rising claim costs, workforce shortages, and shifting regulatory requirements.

Auditability and governance are equally crucial. Modern PAS solutions embed version control and traceability into every system change—from rating rules to workflow configurations. This ensures transparency and simplifies compliance management with built-in audit trails.

Integration readiness is another vital attribute. API-first architectures allow smooth, real-time connectivity to essential services such as payment gateways, agent portals, reinsurance systems, and AI-driven engines. This design supports rapid deployment and flexible plug-and-play capabilities.

Finally, a truly modern PAS delivers unified workflows that provide a 360-degree view of the policyholder. With real-time analytics available at both macro and micro levels, underwriters, claims teams, and operations can make faster, smarter decisions, streamline processes, and improve customer experiences.

Overcoming Barriers to Modernization

Despite the clear benefits, some insurers still hesitate—wary of cost, time, and complexity. Historically, PAS upgrades were multi-year projects with big budgets. But that's changing. Newer market entrants offering end-to-end platforms are dialing down the risk by eliminating implementation fees and reducing the reliance on niche developers. With modern tech stacks and prebuilt integrations, carriers can launch faster and cheaper than ever before.

Modernizing PAS is no longer just a technology upgrade. It's essential to business growth, customer retention, and long-term survival in the rapidly evolving insurance landscape. Ultimately, the question isn't if insurers must modernize—it's how quickly they can act. The competitive divide is real, and despite the time and capital outlay, those who invest now will lead while those who delay risk being left behind.

Recent global research has highlighted a huge talent issue in the insurance industry. Just 9% of the insurance workforce is in digital and tech roles, compared with 47% in sales and marketing. This imbalance leaves the sector overly reliant on manual processes, hindering the adoption of crucial capabilities such as data and analytics.

As data, AI, and climate risk continue to transform the landscape, this lag in digital capability is likely to have serious, far-reaching consequences.

Meanwhile, insurance employers benefit from strong career loyalty, with low turnover and high satisfaction—which is great for long-term planning, but now creates urgency to upskill long-serving staff in digital, product design, AI, and other emerging areas.

Using a combination of quantitative and qualitative analysis, research has identified "pacesetters"— industry leaders that excel in their financial, innovation and talent outcomes and achieve sustained success.

After analyzing millions of data points from the top 200 global insurance companies, we can see how this is playing out in insurance. The research unearthed clear data that some insurers aren't just rising to this talent challenge—they're lighting the way forward for the rest of the industry.

What differentiates these leaders is their embrace of intelligent adaptability—the capacity to evolve quickly and strategically.

But what does that look like in practice?

A late, but strong, embrace of advanced tech

While many insurers remain cautious about AI investment, the industry's pacesetters are forging ahead—recognizing that lagging on adaptability is a risk they can't afford. For these leaders, adopting new technologies isn't optional; it's foundational to staying competitive in the 2020s and beyond.

That marks a significant shift from even a year ago, when most of the top-performing firms had only vague notions about "doing something with ChatGPT." Today, those same leaders are mandating its integration across the board—from enhancing customer experience to transforming back-office training.

A new approach to job definitions and pathways

Training is a crucial focus because the demographic of those committed to long-term careers in insurance is primarily made up of Gen Z, who have grown up with the internet as a default part of their lives. In response, pacesetters are redefining what a career in insurance looks like in the 2020s and 2030s, creating more flexible, adaptable pathways for talent.

At the heart of this new career proposition is a shift in how progression is framed—emphasizing continuing support for developing technological familiarity and proficiency. Importantly, pacesetters aren't aiming to replace large portions of their experienced workforce. Instead, they're carefully designing diverse career paths that make transitioning into tech-focused roles not only manageable but genuinely attractive and motivating.

The aging out and specialized employee problem

It might not seem like a problem, and the highly skilled veteran might not see it that way either, but when critical knowledge is locked away in a single team or even just one person's head, it becomes non-transferable—and runs the risk of being lost when that individual leaves.

Research shows that insurer pacesetters are tackling this challenge in two innovative ways. To prevent the scenario of "Only Janice knows how to do this, go ask her," some employers are experimenting with tech-enhanced onboarding. This includes using virtual reality headsets to provide immersive, step-by-step walkthroughs of core processes early on, allowing new hires—or those moving between roles—to organically build their skill sets and understand how they can contribute beyond their immediate job description.

Some pacesetters are going a step further by capturing the unique specialist knowledge of key or aging staff in "digital twins"—AI-driven models designed to reason and respond with expert-level insight in niche, highly valuable areas of market-specific insurance solutions.

New customer acquisition solutions

Finally, the data reveals how insurance pacesetters are transforming product access by embedding insurance offerings into adjacent industries such as automotive, mortgages, and other B2C products.

What's driving this shift? Not only pacesetters but the entire market have experienced significant declines in renewals over recent years, largely due to consumer pushback against price increases. In response, insurers are striving to present attractive offers as early as possible and intensify efforts to reduce churn and retain customers. These new channels and tactics have only become viable by recognizing that traditional approaches no longer suffice, necessitating fresh, data-driven strategies.

Six steps to intelligent adaption

For insurers looking to become intelligently adaptable, six key steps emerge from this research that should be put into practice:

1. Establish a cross-functional talent intelligence center of excellence (COE). Unite people analytics, HR, digital transformation, and business leadership under one roof. This COE will serve as the foundation for navigating disruption, forecasting future capability needs, and embedding talent strategy into enterprise-wide planning.
2. Identify your most critical talent challenges. Whether it's modernizing underwriting teams, scaling AI expertise, or building pipelines for innovation in product development and customer experience, use AI-driven talent intelligence to analyze roles, skills, and organizational patterns to uncover gaps and opportunities.
3. Develop solutions using the four "R" framework: Recruit, Retain, Reskill, and Redesign. Tailor these approaches to your organization's unique priorities, ensuring that planning and execution involve close collaboration across HR, operations, IT, and business leaders.
4. Embrace cross-functional planning and execution. Align efforts across all relevant teams to ensure a cohesive, integrated approach that supports business goals and drives transformation.
5. Measure, iterate, and improve continuously. Track progress against clear benchmarks, adjust strategies in real time based on data, and create a feedback loop that fosters learning and refinement.
6. Act with urgency and learn from the pacesetters. The insurance workforce is rapidly evolving, and traditional roles and skills are quickly becoming obsolete. Follow the lead of the industry's pacesetters who are finding, cultivating, and retaining the emerging skills they need to thrive in a changing world.

As property insurance markets continue to harden, brokers and agents face an increasingly challenging landscape when seeking coverage for clients with water damage history or aging infrastructure. Some properties have experienced such severe claims that they've become what some in the industry refer to as uninsurable or severely impaired, where the challenge is not about getting an affordable premium but simply being able to get insurance coverage in the first place.

However, innovative technology solutions are emerging that can help transform these high-risk properties into insurable assets, providing brokers with powerful tools to secure coverage and favorable terms for their most challenging clients.

The Scale of the Water Damage Problem

Non-weather-related water damage accounts for approximately $13 billion in annual property insurance losses in the U.S., making it one of the most significant drivers of property insurance claims, with claims over $50,000 doubling since 2015. In commercial and multi-unit residential buildings, the effects are even more severe. The average water leak in New York City costs between $65,000 and $95,000, with damage often cascading through multiple floors before detection.

A 26-story mid-town condominium experienced this firsthand when multiple water incidents led to their insurance premiums skyrocketing from $150,000 annually to $850,000. Properties with such claim histories find themselves in the excess insurance market, where top-tier insurers simply won't provide coverage, forcing owners to alternative carriers with significantly higher costs and reduced service levels.

Point-of-Leak Detection as Market Leverage

The emergence of reliable point-of-leak detection technology has created opportunities for brokers to present compelling risk mitigation cases to underwriters. Unlike flow-based systems that attempt to detect unusual water usage patterns across entire buildings, point-of-leak sensors are placed directly at high-risk locations where leaks typically originate.

These systems can detect water within seconds and initiate live operator calls to building management within a minute, providing specific location information such as, "There's a leak under the washing machine in Apartment 4J." This immediate response capability fundamentally changes the risk profile of a property.

The proof is in the performance data. For example, in 2024, ProSentry systems caught 3,610 leaks with zero false alarms and zero insurance claims. This track record demonstrates to underwriters that properly implemented detection systems can virtually eliminate water damage claims.

Implementation Blueprint for Brokers

When presenting smart leak detection solutions to clients and underwriters, brokers should focus on three key elements:

Comprehensive Coverage: Effective systems must provide building-wide protection, not just individual unit monitoring. Leaks can originate in neighboring units or several floors above, and standalone detectors are often unable to detect or alert to those risks. Insurers increasingly recognize that comprehensive monitoring that adapts to real-world building dynamics is essential for meaningful risk reduction.

Professional Monitoring: While consumer-grade products from big box retailers may offer basic detection, insurers increasingly prioritize full-building solutions that go far beyond app notifications. Systems with 24/7 monitoring services and live operator calls promote immediate response, often supported by building staff.

Automatic Response Capabilities: Advanced systems include automatic shutoff valves that can isolate water sources when leaks are detected. While complete building shutoffs aren't practical for some multi-tenant properties, strategic valve placement allows for isolated response - if a leak occurs, water to that specific area can be shut off while building staff responds.

Quantifiable Insurance Benefits

The insurance industry has begun formally recognizing these risk mitigation investments. Some major insurers now offer premium discounts or lower deductibles for buildings with comprehensive leak detection systems, representing industry acknowledgment that properly implemented detection systems significantly reduce claim risk.

Beyond premium discounts, these systems help properties escape the uninsurable or severely impaired designation. For example, the 26-story mid-town condominium, after implementing comprehensive detection, saw their next insurance premium reduced by $300,000, with their broker successfully arguing to carriers that "We've done our best to mitigate any kind of risk."

Comprehensive Risk Management Approach

Another recent example of implementing risk management to lower property insurance premiums is from a leading Atlantic City casino hotel. Facing a challenging $5 million premium increase over five years due to water damage claims, the hotel casino invested less than $70,000 in comprehensive monitoring systems and ultimately saved close to $5 million through increased carrier competition and improved terms.

The most effective approach combines water detection with broader building monitoring capabilities. Modern platforms can monitor for gas leaks, oil leaks, temperature fluctuations, humidity levels, mechanical malfunctions, and even unauthorized smoking or vaping. This comprehensive approach demonstrates to underwriters a commitment to holistic risk management and property protection.

Moving Forward in a Challenging Market

As insurance markets continue to tighten, brokers who can present clients with concrete risk mitigation strategies gain significant competitive advantages. What's more, some in the industry are already anticipating that we'll soon get to the point where insurance companies will require these systems whether or not a building has a history of water leaks.

Smart leak detection technology provides brokers with a powerful tool to transform previously uninsurable properties into attractive risks. By demonstrating risk management through quantifiable protection measures, brokers can secure coverage for challenging clients while positioning themselves as innovative solution providers in an increasingly difficult market.

The key is presenting these systems not as additional costs but as insurance enablement tools that open doors to coverage and favorable terms that would otherwise be impossible to achieve.