A fake injury, staged slip, trip and fall accidents, double-dipping. Workers' compensation fraud has been a persistent issue for our industry since the U.S. implemented workers' compensation laws in the early 20th century.

Fast forward 114 years. According to a Forbes Advisor report, workers' compensation fraud causes about $34 billion in yearly insurance losses – $9 billion from fraudulent workers' compensation claims and another $25 billion due to workers' compensation premium losses.

The National Insurance Crime Bureau (NICB) and the Coalition Against Insurance Fraud (CAIF) have reported that roughly 10% of these claims are estimated to be fraudulent. The study points out that small businesses are especially vulnerable to workers' compensation fraud due to limited resources for thorough investigations.

Some recently emerging types of workers' compensation fraud were not widely recognized a decade ago. These include claims from remote workers, fraudulent claims resulting from targeted data breaches and other issues associated with the increasing use of technology, as well as sophisticated medical billing fraud, among others.

So, the multibillion-dollar question: How can we turn workers' compensation fraud detection into a risk management advantage?

Let's examine some key tools agents, brokers and insurers can encourage employers to use to reduce, if not eliminate, this costly issue. No single tool is a cure-all. Instead, they should all be integrated into a comprehensive fraud prevention strategy.

Early Identification of Fraud

This is where workers' compensation fraud mitigation truly starts. Both insurers and insured employers need to create a strong and complete reporting and investigation system, which includes the obligation to report all workplace injuries right away.

Furthermore, creating protocols for detailed investigations of any suspicious claims can help confirm whether each claim is legitimate or, importantly, identify those that seem suspicious. Prompt claims reporting by both the insured and injured employee can help stop fraudulent attempts to collect false benefits. Insurance professionals can support their insureds by emphasizing the importance of honesty and accuracy when reporting injuries.

It's far easier to gather evidence at the time of the incident, rather than after time has passed, because important findings or key witnesses might no longer be available. The sooner a claim is filed and reviewed, the less chance there is for false documentation or manipulation.

Strong Documentation and Compliance

While early detection is crucial, maintaining current and thorough documentation of records, including workplace incidents, injury reports, medical assessments and communications, can serve as evidence in potential disputes.

Accurate documentation is fundamental to a workers' compensation claim and requires careful attention to detail. The process begins immediately following an injury or diagnosis, when it is crucial to record all relevant information about the incident and the subsequent medical assessment to support a legitimate claim.

This documentation also includes reports from initial emergency responders, subsequent treatment strategies and pharmacy records, all of which can greatly affect the determination of compensation for lost wages and medical expenses.

Additionally, while not every state requires employers to carry workers' compensation insurance, it is crucial for all parties to stay informed about any requirements and penalties to ensure their coverage complies with state laws. Moreover, staying updated on industry changes, legal updates and new best practices for detecting and preventing workers' compensation fraud is also important.

Sharing this information with potential claimants can build a culture based on accountability and integrity, which is vital in a comprehensive workers' compensation fraud prevention environment.

Embracing a Collaborative Approach

Insurers can educate their policyholders about maintaining regular and close communication with insurers, medical professionals, attorneys and all necessary parties when managing a claim. Knowing how to navigate the claim process among all involved is crucial.

To stay ahead of the claim's outcome, those employers should also familiarize themselves with the policy and benefits available, as well as communicate clearly and concisely—always sticking strictly to the facts.

At the heart of preventing workers' compensation fraud is building a strong culture of integrity in the workplace. Both insurers and insureds play a crucial role in this by setting clear standards for honesty and transparency and demonstrating these values themselves.

This includes not only following ethical guidelines in their financial transactions and reporting, but also creating a supportive environment where employees feel valued and appreciated. When injured employees are treated with respect and fairness, they are less likely to participate in fraudulent activities against their employer or exploit the workers' compensation system.

Robust, Hands-on Training Programs

Insurance professionals should encourage insureds to provide continuing and comprehensive training. It is best practice to inform potential claimants about the workers' compensation process, their entitlements and obligations, as well as the repercussions of fraudulent actions. A thorough program will enable insureds to:

• Know all aspects of workers' compensation fraud
• Better understand reporting procedures and how to best collaborate with regulatory agencies
• Acquire skills for investigating and recording suspected fraudulent behavior
• Identify signs of fraudulent activity among insureds, injured employees and insurance professionals
• Mitigate risk by adopting proactive claims management practices

Additionally, it's important to stay updated on industry changes, legal updates and effective methods for preventing and detecting workers' compensation fraud. Sharing this information will help foster a culture based on accountability and integrity.

Embracing Technology

Thanks to advanced claims software, artificial intelligence (AI) and other surveillance methods, we can streamline review processes and detect potential red flags early, giving us the strongest set of tools ever to fight workers' compensation fraud.

• A strong predictive fraud model analyzes and assesses data from various internal and external sources, including claims history, medical billing information, public records, databases of medical providers, industry standards and specialized investigative and geographical data.
• AI models can analyze and assess thousands of incoming claims in near real-time, quickly pinpointing those most likely to be fraudulent.
• Technology-based strategies like video monitoring, social media oversight and field investigations can help detect patterns, verify claims and identify fraudulent behavior.

These and other capabilities enable investigative teams to concentrate on the most suspicious cases early on, instead of waiting weeks or months.

By recognizing the divisive effects of workers' compensation fraud and dedicating ourselves to joint preventive measures, we can protect the integrity of the system. This approach is crucial not only for supporting injured employees but also for upholding the equity and confidence that are fundamental to our wider social and economic frameworks.

Insurance fraud is a growing concern for insurers worldwide, leading to significant financial losses and increasing premiums for customers. Fraudulent claims, including staged damage, false theft reports, and counterfeit device schemes, challenge traditional fraud detection methods. As primary insurers increasingly adopt artificial intelligence (AI) to identify and mitigate fraud, reinsurers must understand how these technologies affect loss ratios, pricing models, and risk assessment strategies. This article examines how AI-powered fraud detection is transforming the insurance landscape and what it means for reinsurance underwriting.

The Scope of Insurance Fraud

Insurance fraud encompasses a variety of schemes that strain insurers' resources and undermine trust. Common fraud patterns include:

• Staged Incidents: Claiming damages for devices or property intentionally destroyed or pre-damaged
• False Theft Claims: Reporting non-existent thefts to receive compensation
• Device Swapping: Submitting claims for counterfeit or older devices while keeping the insured item
• Fake Documents: Providing forged receipts, police reports, or repair invoices

Such activities not only affect primary insurers but also affect reinsurers who share in these losses through treaty arrangements. Understanding how AI reduces these fraud rates is critical for accurate reinsurance pricing.

How AI Tackles Insurance Fraud

AI leverages advanced technologies such as machine learning (ML), natural language processing (NLP), and computer vision to detect fraud patterns and anomalies. Below are the key applications of AI in combating insurance fraud:

1. Pattern Recognition

AI systems analyze historical claims data to identify patterns indicative of fraudulent behavior. For example:

• Unusual claim frequencies from the same individual or geographical area
• Inconsistent information provided across multiple claims
• Behavioral patterns that correlate with confirmed fraud cases

For reinsurers, understanding the effectiveness of these pattern recognition systems helps assess whether primary insurers are reducing their loss ratios through better fraud detection.

2. Image and Video Analysis

Using computer vision, AI can scrutinize submitted photos and videos for signs of manipulation or forgery. For example:

• Detecting inconsistencies in photos of damaged devices or property
• Verifying timestamps and metadata to confirm the authenticity of media files
• Identifying previously submitted images being reused for new claims

These capabilities significantly reduce fraudulent payouts, directly affecting the loss experience that reinsurers cover.

3. Natural Language Processing (NLP)

NLP tools can analyze written statements or phone conversations for inconsistencies and red flags. For instance:

• Identifying discrepancies in customer narratives across multiple interactions
• Detecting keywords or phrases commonly associated with fraudulent claims
• Analyzing linguistic patterns that indicate deceptive behavior

4. Behavioral Analytics

AI tracks policyholders' digital behavior during the claims process to identify anomalies, such as:

• Sudden changes in location or device usage patterns
• Repeated login attempts from unusual IP addresses
• Inconsistent data across digital touchpoints

5. Real-Time Fraud Detection

AI-powered systems can flag suspicious claims in real time by:

• Cross-referencing claims with external databases, such as police reports or repair shop records
• Using predictive models to assign a fraud risk score to each claim
• Enabling immediate investigation of high-risk claims while processing legitimate claims faster

6. Automation and Efficiency

AI streamlines the investigation process by automating repetitive tasks, such as document verification and data entry, enabling human investigators to focus on complex cases.

Benefits for Insurers

Enhanced Accuracy

AI minimizes false positives and negatives, ensuring genuine claims are processed quickly while fraudulent ones are flagged. For reinsurers, this means more predictable loss ratios from cedents using advanced AI systems.

Cost Savings

By preventing fraudulent payouts, insurers can save millions and reduce administrative overheads. These savings improve the profitability of primary insurers, which can lead to better retention rates and affect reinsurance treaty structures.

Improved Loss Ratios

Faster claim processing and reduced fraud result in lower overall losses. Reinsurers evaluating potential partners should consider the maturity and effectiveness of their AI fraud detection systems when pricing treaties.

Scalability

AI systems can handle large volumes of claims efficiently, making them ideal for high-demand scenarios. This scalability is particularly relevant for reinsurers covering high-frequency lines of business.

Reinsurance Underwriting Considerations

As AI adoption spreads across primary insurance markets, reinsurers must adapt their underwriting approaches:

Evaluating AI Implementation

Reinsurers should assess:

• The type and sophistication of AI systems deployed by cedents
• Historical data showing fraud reduction rates since implementation
• Integration quality with existing claims systems
• Training data quality and model performance metrics

Pricing Differentiation

Insurers with proven AI fraud detection capabilities may warrant more favorable reinsurance pricing. Reinsurers can create competitive advantages by developing frameworks that credit effective AI implementation.

Adverse Selection Risk

As some insurers adopt AI while others lag, reinsurers face potential adverse selection where insurers with weaker fraud detection disproportionately seek reinsurance coverage.

Treaty Structuring

Performance-based treaty adjustments tied to fraud detection metrics can align incentives and account for the improving loss experience from AI implementations.

Challenges and Ethical Considerations

While AI offers immense potential, it is not without challenges:

• Data Privacy: Handling sensitive customer information requires strict adherence to data protection regulations, which can vary across jurisdictions relevant to reinsurance treaties
• Bias in AI Models: Ensuring fairness in fraud detection models is critical to avoid discriminating against specific groups
• Transparency: Explaining AI decisions to customers, regulators, and reinsurance partners can be complex
• Model Validation: Reinsurers need assurance that AI systems are properly validated and produce reliable results

The Future of AI in Insurance and Reinsurance

As AI technology advances, it will become even more adept at detecting sophisticated fraud schemes. Emerging trends include:

Deep Learning Models

More nuanced fraud detection capabilities that can identify complex patterns invisible to traditional machine learning approaches.

Integration with IoT

Leveraging device data and telematics for real-time fraud monitoring, providing objective evidence that reduces information asymmetry between insurers and reinsurers.

Collaboration Platforms

Sharing anonymized fraud data among insurers to identify repeat offenders across the industry. Reinsurers may play a role in facilitating these networks to improve overall market loss experience.

Parametric Trigger Evolution

AI fraud detection reduces moral hazard in traditional indemnity products, similar to how parametric triggers reduce claims adjustment uncertainty in catastrophe coverage.

Conclusion

AI is revolutionizing the fight against insurance fraud by providing insurers with sophisticated tools to detect and prevent fraudulent activities. For reinsurers, this technological transformation presents both opportunities and challenges. By understanding how AI systems work and developing frameworks to evaluate their effectiveness, reinsurers can more accurately price risk and structure treaties that account for improved loss ratios. As primary insurers continue to embrace AI, reinsurers who build expertise in assessing these technologies will gain competitive advantages in underwriting and pricing. The future of reinsurance will increasingly require technical due diligence on fraud detection capabilities as a core component of risk assessment.

Changes to the tariff environment over the course of this year have presented challenges to businesses and dealmakers. Since Jan. 20 of this year, there have been over 100 changes to trade policy in the United States. Numerous other countries have imposed reciprocal tariffs on products from the United States. Significant challenges in the M&A market have resulted, and these challenges have trickled into RWI underwriting. Buyers and underwriters need to quickly adapt to these changes and prepare for additional changes in trade policy.

What challenges do tariffs pose for deals? While there are indirect impacts, such as a potential economic downturn and increased inflation, buyers and underwriters are focused primarily on direct impacts. Direct impacts primarily relate to increased costs of a target's products, which can result in reduced margins and reduced demand for the target's products caused by increased prices. These impacts vary depending on the exact nature of what is being imported. Consumer businesses with international supply chains will likely be affected; businesses whose supply chains cross U.S. borders multiple times are likely to be severely affected. Other businesses, such as "software as a service" providers or many healthcare businesses, will face very limited, if any, impact from tariffs.

Consider a steel mill located along the U.S. border with Canada. While there are tariffs imposed on imported steel, given that the business appears to fabricate steel in the United States, a buyer would reasonably assume that there would be little tariff impact on this business. However, as diligence progresses, the buyer discovers that the target's steel production process has several steps in Canada. These steps must be conducted in a specific order and, unfortunately for both the buyer and the target, these steps necessitate crossing the U.S. and Canadian border several times. Each crossing requires a tariff to be paid either to the U.S. or Canadian government, as Canada has threatened and implemented reciprocal tariffs on U.S. exports. Very quickly, the target's costs skyrocket. The target is forced to attempt to increase its prices; however, it is not certain that they will be able to do so. What is a buyer to do in this situation? There are three potential options: (i) walk away from the transaction; (ii) revise the valuation of the business to reflect reduced cash flow resulting from materially higher costs; or (iii) trust that the target will be able to offset tariff costs and not revise its valuation. Each approach has different levels of risk; however, we will focus on (ii) and (iii) in the context of a RWI transaction.

Before discussing strategies underwriters have been implementing to mitigate tariff risk, we must first discuss how tariffs affect an RWI policy. The specific impact is entirely dependent on the language of the representations in the purchase agreement. In a fairly negotiated transaction, it is likely that the impact of tariffs on the target will breach at least one of the representations. Which representations will be implicated varies depending on the specific facts and circumstances; however, given the far-reaching impact of tariffs, there are many potential breaches. These potential breaches include breaches of the customer and supplier representations, material contracts representations, the no undisclosed liabilities representation, the absence of material changes representation, and the financial statement representations.

How are underwriters addressing tariff risk? Generally, underwriters are working to understand how these potential impacts have been factored into the buyer's valuation of the target. If a buyer has underwritten a transaction at a discounted level of EBITDA resulting from increased costs, the likelihood of a loss resulting from tariffs is substantially lessened. This is because buyers may not suffer a "loss" if tariffs do in fact discount EBITDA, as the impact has been fully accounted for in the purchase price. There is still potential for a loss if the buyer does not fully discount EBITDA for purposes of its valuation; however, the magnitude of the loss is lessened by any discount included in the buyer's valuation. Sellers are reticent to accept a lower valuation for their business as a result of tariffs; consequently, not all buyers are able to fully reduce purchase price for the expected tariff impact. Sellers often suggest various tariff mitigation strategies and will argue that these address any material impact from tariffs. These strategies can vary from passing along price increases to customers, to negotiating with the target's international suppliers to split the tariff costs. To accommodate sellers, buyers will assess these strategies, determine which they believe are likely to be effective, and revise their valuations to reflect successful tariff mitigation. Whether or not an RWI underwriter will underwrite the risk depends on a number of factors, including the underwriter's individual risk tolerance and how successful the target's tariff mitigation strategies have been to date.

If the buyer has not factored any tariff impact into their valuation, underwriters have been digging in further. Initially, underwriters will ascertain what percentage of the target's products are subject to tariffs. They will also seek to ascertain the imported items. If the target is importing raw materials or components of its products, the impact of tariffs on the overall price of the target's product may be limited. For example, if imported items constitute a small portion of the cost of the target's products, underwriters are more likely to view tariff price increases as immaterial. If the target imports finished goods, or the increase in the cost of components/raw materials is material, underwriters will seek to ascertain the likelihood of tariff price increases being passed along to the target's customers. If the target has already increased its prices to pass along tariff costs and customers have been paying increased prices, underwriters are likely to view tariff impact as low risk. Underwriters will also seek to understand whether these increased prices will result in reduced demand for the target's products. If the target's products are "non-discretionary," underwriters are likely to view the risk of reduced demand as low. To the extent that the target's products are discretionary, underwriters will want to understand how increased prices will reduce the demand for the target's products. Customer calls are likely to be a key point for underwriters in measuring the risk of reduced demand.

If an underwriter views tariff impact as material, underwriters have been primarily addressing tariff risk in two ways. The first is an exclusion related to tariffs and the second is a deemed disclosure of the tariff impacts on the target. A majority of markets have avoided using exclusions given both buyers' and brokers' preferences. There is also concern regarding the effectiveness of any exclusion related to tariffs, as underwriters have concerns about being able to prove that any breach of the representations is tied to tariffs. For example, it may be difficult to prove that the loss of one of the target's customers is tied specifically to tariffs. The more common approach has been to put together a relatively broad deemed disclosure, which describes the specific tariff impact. This approach is often more palatable to the buyer, as the language of the disclosure is often heavily negotiated. Underwriters also attempt to avoid limiting the disclosure to a specific representation; however, they will often accept limiting a disclosure to a specific representation both parties agree is the most likely to be breached. In such circumstances, underwriters will rely on customary cross-referencing language for the disclosure schedules, which provides that a disclosure shall be deemed disclosed to any other representation to which the applicability of the disclosure is reasonably apparent on its face, to mitigate the risk of breaches of other representations.

While the approaches discussed above do not entirely preclude a buyer from bringing a claim relating to tariff impacts, they generally lessen the risk to an acceptable level. It is important to note that every transaction is unique, and that the approaches discussed above are common but not ubiquitous. Every underwriter has different risk tolerances, and every transaction involves a different level of tariff risk. Underwriters will be as commercial as possible in addressing tariff issues; however, it is important that buyers recognize tariff risks in their transactions. Changes to the tariff environment have shown the adaptability of buyers, sellers, and underwriters. While there are likely other potential changes to tariff policy, using the strategies described above, tariff risks can be mitigated in an RWI transaction.

Paul Carroll

What are the main challenges in underwriting today, and how might emerging technologies address these issues?

Balázs Kaman

One of the biggest challenges in underwriting today is simply getting the right data into the system. Many of our MGA customers underwrite highly specialized risks such as crypto exchanges, mining rigs submerged in the ocean, or electric vehicle chargers. The prerequisite for accurate underwriting is having high-quality data available for rating, but collecting and structuring that data is still painful and time-consuming.

Over the last decade, the industry tried to solve this by pushing data entry downstream and asking agents or insureds to fill out information directly in portals. With AI, we now have a better path forward. Instead of changing long-standing submission behaviors like sending emails, AI can extract and structure that data automatically and trigger the necessary workflow steps. That allows underwriters to spend less time rekeying information and more time focusing on evaluating risk.

Paul Carroll

I’d bet that the speed enabled by AI creates benefits beyond efficiency. In the consulting world, the concept of "time to value" has really taken hold over the past 15 years. In other words, don’t just tell me you’ll double my investment; tell me whether you’ll do that in two years or 10. What value does this acceleration bring to the underwriting field? 

Balázs Kaman

Absolutely. Tools that integrate via APIs [application programming interfaces], enriching the data that you have available, using AI to do some of the underwriting and also scoring the incoming requests let you focus on what really matters: These tools enable underwriters to cut processing times dramatically.

What used to take maybe days can now be handled immediately or can at least surface a preliminary price or rate, so you can then come back with a more polished rate after all the underwriting was taken into consideration. 

Paul Carroll

I imagine faster quote turnarounds provide a competitive advantage in the highly competitive MGA market? 

Balázs Kaman

Speed is the name of the game. We see that in all the customer types we serve. Wholesale brokers who don't necessarily do the underwriting themselves but focus on finding the markets that need a specific application—speed is very, very important for them. Also for MGAs who rate their own risk and do their own underwriting and who might have binding authority.

In the past, they used platforms where they needed to log in and rekey all the information. Modern systems like BindHQ allow integration with their APIs directly and massively reduce the time it takes to get quotes back.

Paul Carroll

How has insurance evolved in the six or seven years since BindHQ was founded? 

Balázs Kaman

I can say that many of the big frustrations—issues like duplicate data entry, disconnected systems, and long response times to customers—can all be addressed with today's technology.

Previously, only a handful of forward-thinking carrier markets had APIs, and even those weren't very modern or helpful. They typically only allowed for quoting, not binding or endorsing policies.

Our industry is slow to adapt. Still not every carrier has those capabilities. But we are getting there.

Seven years ago, I saw a lot of handwritten, scanned paper documents being uploaded into agency management systems. Then someone, mostly offshore, would rekey that information. Today, modern OCR or AI-assisted tools can read and process that information, which saves time, reduces cost, and creates a much better user experience.

Seven years ago, people simply sent ACORD forms in emails. This practice is still fairly common because people resist change. They wonder, “What's in it for me?” You really need to provide incentives to agents to start using new technology or platforms, or they’ll just email the expiring policy or a filled-in ACORD form from two years ago.

If you tell agents to come to your platform and rekey everything, that won't work. But with the new tools using OCR and GenAI to extract that information, you can save them tremendous time. As an MGA, if you can save time for retail agents and quickly provide accurate quotes, they're more likely to send business to you. I find it amazing how long forms have remained relevant despite technological advances. 

Paul Carroll

I hear all the time about problems with data standardization in the insurance industry. How do we address that, given that data is expressed in different formats across different systems?

Balázs Kaman

The question is tricky because insurance is complex. I joke that specialty insurance is anything about anything. It's a written contract about literally anything. So there are either no standards or there are too many standards.

People have tried coming up with standards, but specialty MGA program administrators come up with creative and innovative products, so how they capture data might change. And depending on who’s viewing the report, they might want to see things differently. So we provide access to the data and allow you to really build your own report.

There, again, generative AI can be hugely helpful because the tools can really democratize the data engineers' work. You can, in plain English, explain what reports you want to see. And then if the data is available, you can more easily build those reports.
But GenAI is unfortunately not a silver bullet. You cannot just put ChatGPT on top of a database and expect it will solve all the problems, because insurance is very complex. Depending on how you ask the questions, you might get different answers. Like, are you thinking about the term premium, the billed premium, the annual term premium, the pro rata premium? Even just "premium" has so many meanings that you need to be very careful when you are creating a report. 

Paul Carroll

Where do you see underwriting heading over the next two or three years?

Balázs Kaman

That is a great question. I've read a quote that people usually overestimate change in the short term and underestimate change in the long term. I think the GenAI hype has maybe settled down a bit. Everyone started using it, and they burned themselves once or twice. Now, some people are saying it's not the revolutionary thing we thought it would be.

But even if we just implement everywhere in all the tools, in all the workflows, the technology that is available today, it would already mean a huge change for the entire industry. Automating the busy work will be huge.

Also, I think concepts that are not even considered today will become more prevalent. Using AI agents and building custom agents to do underwriting and enrich data will be huge. Accessibility and the interconnected nature of our industry will be better.
I think the trick with GenAI is that you don't need to change human behavior. You can just put smart tools on top to get huge results.

I still think insurance is a relationship business. There will still be a huge role for the relationship part and the human element. Technology will not solve all parts of the problem, like securing capacity. 

Paul Carroll

How accurate is AI, and how accurate does it need to be?

Balázs Kaman

97% accuracy is sometimes great, but sometimes it's not good enough. If you need to report on your financials, for instance, 97% accuracy is not sufficient. However, if you want to provide speedy responses and a ballpark estimate is acceptable, then 97% can be good enough.

I think that's where the difficulty lies for many technology providers. Getting from zero to 95% accuracy is pretty easy with these new technologies. But going from 95% to 100%, where you can totally trust the system and take the human out of the process, is difficult. 

Paul Carroll

We’ve published numerous articles on “continuous underwriting,” where companies adjust policies in real time when conditions change rather than waiting for renewal periods. How do you view that concept? 

Balázs Kaman

The technology would allow that, and really forward-thinking companies are doing it. In business auto, it's very common that you continuously underwrite. Based on the mileage that was driven, you fine tune the policy and dynamically do the rating. I believe this trend will intensify, particularly with the proliferation of IoT, as ubiquitous connectivity becomes the norm.

I also see embedded insurance as a trend, building on that connectivity through APIs. With AI, the integration part can be much simpler. 

Paul Carroll

Any final thoughts? 

Balázs Kaman

I think this is a very exciting time. In the past, humans were afraid when there was change, thinking they would be out of a job. But the coming years will help underwriters reduce the busy work, the boring stuff—keying in information, sending emails, responding to emails—so they can focus on what requires their expertise and on the art part of underwriting. The boring stuff will be taken care of by technology.

About Balázs Kaman

As Head of Product at BindHQ, Balázs leads the company’s product strategy and innovation roadmap, shaping how MGAs, wholesalers, and retailers connect through BindHQ’s modern insurance distribution platform. He champions customer-centric design and scalable architecture, ensuring products deliver measurable value for underwriters, brokers, and partners. With over 12 years of experience spanning product management, software engineering, and business operations across multiple indusries, Balázs combines deep technical expertise with a strong commercial mindset to drive digital transformation across the insurance value chain.

The oddest invitation I received at the recent InsureTech Connect was from the vice chair of Duck Creek, who suggested I attend a session it sponsored that... barely mentioned Duck Creek. Instead, the session was a celebration of a Duck Creek customer, Hagerty Inc. — and it was a revelation. While I wasn't familiar with Hagerty, it turns out to be a model for what insurance can (and should) be.

Having opened a small agency 40 years ago because they couldn't find insurance for their wooden boats, Frank and Louise Hagerty expanded into classic cars and then kept following their customers until Hagerty Inc. served just about any need a car enthusiast could have. That little agency now carries a market value of nearly $4 billion.

The journey shows how others can also wrap services around their core insurance products and do more for their customers, while turning them into loyal customers, if not fans. 

I know, I know, nobody is going to get as excited about directors & officers insurance as many do about classic cars, but it's possible for all sorts of lines of insurance to demonstrate broader understanding of, and care for, customers.

Some agencies and carriers are already doing so — and winning.

Don't you wish your company had videos as cool as Hagerty's "Driveway Find" about the immaculate restoration of a 1964 Chevrolet Impala that two car nuts did for the original owner or this "Redline Rebuild" of a Stovebolt 6 engine from an ancient Chevy pickup truck? (Fair warning: If you click through to either of those videos or to the media section of their website, you may be there a while. I'm not at all a car guy but got sucked in for a good half-hour.)

Hagerty got to this point by moving beyond wooden boats and into insurance for classic cars in 1991, then progressively expanding to take on more of car enthusiasts' needs. The company launched a price guide in 2008 — it had to have the information for insurance purposes, so why not provide it to customers and prospective customers? Information on price is valuable for just about any used item, but especially for a category like old cars where comparables are hard to find. In 2017, Hagerty began offering membership in a drivers club, which offers automotive discounts, roadside service and more. Hagerty has also set up a marketplace where people can buy and sell classic cars online. Hagerty charges no fees; it benefits just from being the center of attention. Last year, it bought a small carrier so it can serve customers directly.

Along the way, the company made some smart marketing moves, too. It launched a magazine in 2000, bought well-known events such as the Greenwich Concours d'Elegance and even worked its way into a presence in the Gran Turismo video game.

While owners of classic cars and motorcycles are a breed apart, perhaps matched in their enthusiasm only by certain groups of boat owners, agencies and carriers can fulfill all sorts of other needs, even if they're far lower on the excitement scale. 

I'm enthusiastic, for instance, about Empathy. While life insurers pay the death benefit and agents facilitate the bureaucracy associated with getting the claim, lots of the beneficiaries could use much more, well, empathy. They're facing a daunting series of processes — dealing with a funeral home, perhaps arranging a memorial service, notifying friends and relatives, and on and on and on. Many are going through the unfamiliar, intimidating process for the first time, while dealing with waves of emotion. Why not use Empathy or set up a similar service that goes beyond the insurance piece and helps people navigate the first month following a death? Why not say: "We've been here before. We know what you're going to deal with. Let us help."?

I'm likewise delighted by some of the innovations in P&C, where carriers are telling clients that they'll help protect their homes, not just pay to repair them after a loss. Whisker Labs is my poster child, with its Ting device that plugs into a wall and detects electrical faults that could lead to home fires. Some 34 carriers now provide the device and service free to customers, and I love the message that sends. I'm sure the carriers are finding that customers do, too. Water leak sensors aren't quite as far along in terms of cost-effectiveness, but they're getting there, and I hope carriers will start providing those for free, too, before long. 

Workers' comp, where huge progress has been made in preventing injuries, has also demonstrated the benefit of adding service that takes great care of the individual. If an injured worker feels ignored, they may take longer to recover and may even seek legal help. If an advocate calls them shortly after an injury, expresses concern and helps walk them through the whole recovery process, the results have proved to be better for everyone. 

There are surely other areas, too, where carriers and agents and brokers are going well beyond their contractual obligations. I just wanted to call attention to Hagerty as an example of how lucrative it can be to expand beyond the basic insurance product and tackle the full needs of a client. 

$4 billion is a rather nice market value for a small insurance agency to grow into.

Cheers,

Paul

Cybersecurity has fought a long, hard battle with alert overload.

Most companies throw money and workforce at the problem until they reach a point where they can't throw any more. Attrition and layoffs often follow as unjustified costs with no ROI are cut. As each member of the IT team departs, the organizational knowledge and context become increasingly elusive. Managed service security providers (MSSPs) and managed detection and response firms (MDRs) may come in as a saving grace, but that leaves the problem outsourced and still unresolved.

It's no surprise that forward-thinking organizations are turning to artificial intelligence to revolutionize their security operations centers (SOCs). But what might be surprising beyond the technological benefits is a compelling ROI use case: AI SOC agents deliver measurable returns that can transform an organization's security budgets from a cost center into a strategic investment.

The Rising Costs of Traditional SOCs

Staffing a typical enterprise SOC requires a staggering investment in human resources that extends far beyond analyst salaries. The total operational expenditure balloons when factoring in benefits, continuous training, and the high costs of employee turnover, creating a massive and perpetual financial burden for organizations.

Beyond financial impact, SOC teams are wasting 25% of their time chasing false positives. All this while the average security incident costs $4.4 million in real dollars when considering downtime, data loss, and remediation efforts (while not factoring in lost business, negative publicity, and reputation damage).

Traditional SOCs also struggle with coverage gaps since human analysts can't maintain consistent vigilance across three shifts. This results in detection misses, as well as delays during off hours, creating inconsistent coverage and opening vulnerable windows that sophisticated attackers know to exploit.

The ROI AI SOC Can Deliver

Organizations implementing AI SOC agents report dramatic improvements across numerous financial metrics, some reducing costs by up to 80% in their security operations budgets, all while simultaneously improving threat detection accuracy.

The primary ROI drivers include reduced response times since AI agents investigate alerts in minutes rather than hours, reducing Mean Time to Resolution (MTTR) by 3x. This acceleration directly translates to reduced incident costs, with faster containment limiting the reach of security breaches.

AI SOCs also eliminate alert fatigue by automatically triaging and filtering false positives. As a result, AI agents enable human analysts to focus on actual threats. For example, organizations using AI SOC solutions report that analysts spend 90% of their time on high-value activities rather than mundane alert handling. In addition, AI agents provide uninterrupted monitoring without degradation in performance, preventing coverage gaps that attackers can exploit during off-hours.

Realizing Annual Savings

By implementing an AI SOC, enterprises can significantly reduce costs while enhancing efficiency. Instead of continually expanding analyst headcount to keep up with rising alert volumes, organizations can streamline existing teams into smaller, more specialized units. This shift not only cuts substantial operational expenses but also improves job satisfaction for security staff, who can now focus on higher-value work rather than being buried in routine alerts.

AI agents process alerts at a speed no human team can match—consistently outperforming human analysts in battle-like environments nearly 95% of the time. Their precision, consistency, and ability to scale make them unmatched when it comes to rapid detection and response. Yet, the future of security operations won't belong to machines alone. True resilience will come from the balance between relentless AI-driven execution and human strategic oversight. AI handles the grunt work—sifting through noise, prioritizing threats, and executing playbooks—while humans focus on what they do best—critical thinking, creative strategy, and adapting to the unexpected. Together, this hybrid force redefines how security teams win against evolving adversaries.

Additional Cost Savings With AI SOC

Organizations also realize additional financial benefits of AI SOC agents beyond immediate cost savings. A critical benefit of AI SOC is an improved security posture that can enable business growth initiatives previously unreachable. For example, threat hunting capabilities identify vulnerabilities before they're exploited, preventing costly breaches and regulatory penalties. The average data breach fine in many jurisdictions now exceeds $2 million.

In addition, when it comes to the competition, AI SOC-powered organizations can respond to threats faster than competitors, protecting market position and customer trust. This competitive advantage can preserve revenue streams and enhance brand value.

Maximizing AI SOC Implementation ROI

To maximize ROI from an AI SOC implementation, organizations should follow some essential guidelines.

To start, successful deployments integrate AI agents with existing security infrastructure rather than replacing entire systems. After that, the transition from manual to AI-assisted workflows requires careful planning, so organizations should invest in analyst training and gradual responsibility transfer. Finally, AI agents improve over time through continuous machine learning, but organizations must actively participate in this optimization process to maximize returns.

Security Investment for the Future

AI SOC agents represent more than technological security investments; they're a fundamental shift in how organizations approach cybersecurity economics. By moving security operations from reactive cost centers into proactive value generators, AI enables the strategic security posture that modern businesses require.

The annual savings discussed are not just about cutting costs; they also allow for reinvestment of AI-generated savings into strategic security initiatives that drive business growth. As cyber threats continue to evolve, organizations that embrace the AI SOC advantages today will be better equipped to handle tomorrow's challenges while maintaining the financial flexibility to invest in future innovations.

For organizations evaluating AI SOC implementation, the question isn't whether they can afford to invest; it's whether they can afford not to. In an era where cyber threats grow more sophisticated daily, AI SOC agents are the only way to keep up. They provide a scalable, cost-effective solution that transforms security from a necessary expense into a competitive advantage.

For years, CISOs have relied on a defense-in-depth strategy built with layers of security to protect the physical perimeter, the endpoint, the applications, and the data that flows between them. While a best practice in its day, this approach has left many organizations in a state of entrenched "product sprawl," coping with a patchwork of disparate tools and consoles, each designed to do its job but not necessarily to work well together. The inherent shortcomings in this legacy architectural approach are being exposed at a moment when the volume of data flowing through enterprise environments is exploding and the number of hybrid and remote users has surged, leading to visibility gaps, alert overload, slow response times, conflicting policies, rising costs, and reduced security effectiveness.

Fundamentally, the attack surface has changed beyond recognition, and it's clear that traditional approaches cannot address the increased complexity of modern networks. The concepts behind Secure Access Service Edge (SASE) represent a needed paradigm shift in how we think about security architecture by converging security and networking into a single, integrated, cloud-delivered platform that vastly simplifies how we connect and manage on-premises and remote entities.

In recognition of the just-concluded Cybersecurity Awareness Month, let's look at the top five ways SASE transforms enterprise security:

1. Security and networking convergence

In legacy architectures, networking and security are built and operated separately. Security solutions such as NGFWs, SWGs, VPNs, CASBs, etc., sit apart from networking components like routers, SD-WAN controllers, and WAN optimizers. Each tool has its own policy engine and controls its own data flow, making it complex to stitch them together to work in concert.

Advanced SASE solutions eliminate this divide by unifying these functions, not just yoking them together. Instead of hop-by-hop inspection service-chained across multiple appliances, security is applied natively within the traffic flow, providing seamless network and policy enforcement that streamlines operations, reduces latency, and closes gaps.

2. Single-pane-of-glass visibility

With traditional tools, security teams must pivot from one interface to another, trying to manually identify indicators of compromise with delayed or contradictory data.

In contrast, SASE gives networking and security teams a unified control plane. They gain full visibility into users, devices, applications, and threats across the entire infrastructure – from branch to cloud to remote endpoints. As a result, log correlation becomes faster, enriching data and allowing responses in real time.

3. Modernized defense-in-depth

Defense-in-depth isn't dead as a concept, it's just evolved. SASE provides all the core pillars of layered security (NGFW, intrusion prevention, DLP, ZTNA, CASB, SWG, etc.), but as coordinated capabilities in a single architecture. Policies apply equally everywhere, unlike with legacy tools, where policies may apply only in certain locations, leading to inconsistent enforcement in a hybrid world where users are constantly moving between corporate networks and connecting from anywhere.

The value of delivering defense-in-depth capabilities within a single architecture can be found in cohesive, layered protection without the operational burden of stitching together multiple point solutions, thus providing inline control for real-time defense. This enables immediate, coordinated action, and allows security functions such as ZTNA, NGFW, SWG, IPS, and threat intelligence to share context and enforce unified policies. This approach reduces gaps, eliminates redundancy, and simplifies management to strengthen security posture while improving performance and efficiency.

4. Zero Trust built in

The Zero Trust philosophy of "never trust, always verify" is critical in today's evolving threat landscape. Yet many organizations limit Zero Trust Network Access (ZTNA) to remote users, while sticking with traditional perimeter security and network access control solutions for in-office authentication. This creates uneven security coverage and leaves gaps where implicit trust is persistent after initial access. Advanced SASE solutions embed Zero Trust principles across all entities regardless of their location. A device's posture is continuously evaluated, least-privilege access is dynamically enforced, and identity-aware security policies allow for microsegmentation to restrict lateral movement. All policies are centrally managed and auditable to ensure consistent, adaptive protection everywhere.

5. AI made effective

Advanced SASE platforms also lay the foundation for AI-driven security by providing enriched data for all entities that can be parsed and correlated via a single system, enhancing the Zero Trust model by eliminating blind spots and enabling deeper, more accurate analysis for faster remediation. AI poses a problem for traditional solutions, which use their own built-in AI and therefore know how to enrich only their own data. When it comes to working with other solutions' enriched data, a third-party solution such as a SIEM is needed that can take this data, parse and correlate it as needed, and display it in a way that showcases indicators of compromise and real and potential threats.

Security leaders find themselves with an incredible challenge. The threat landscape is evolving faster than ever, and legacy tools are failing to keep up. The pressure to consolidate, simplify, and modernize has never been greater. SASE offers a way forward with a new architecture that's faster and smarter and meets the reality of how businesses operate today.

The banking, financial services and insurance sector (BFSI) has a problem. While nearly every industry leader agrees that digital transformation is business-critical, new research from Iron Mountain and HFS Research uncovers a stark disconnect between aspiration and action, with 78% of BFSI executives globally warning that failing to digitize could result in permanent competitive irrelevance.

The Back Office: No Longer Just Support

The back office has evolved from a support function to the backbone of operational resilience, regulatory compliance and differentiated customer service. Despite this, most organizations are struggling to move beyond legacy, paper-driven processes. While 81% of BFSI executives globally believe artificial intelligence will soon handle the vast majority of routine back-office tasks, only 13% have deployed AI at any meaningful scale. And while 77% believe the traditional back office will disappear within three years, only 21% are "radical transformers"—the organizations making bold moves to get there.

This leaves the BFSI industry to face an uncomfortable truth: The opportunity to achieve compliance, resilience and efficiency through digital and AI-powered operations is within reach, but only for those willing to move beyond incremental change. In today's BFSI sector, transforming the back office isn't just a lofty goal, it's become a business necessity. While many leaders are making ambitious plans to overhaul their core operations, turning that vision into reality remains a challenge.

The following seven hard truths highlight the disconnect between digital aspirations and the persistent realities of legacy systems, underscoring the struggle between commitment and true readiness for change.

Seven Hard Truths Reshaping the BFSI Back Office

1. Failing to digitize means falling behind permanently: Digitization has become a critical business success factor, with the overwhelming majority of leaders recognizing that organizations that do not act now could fade into irrelevance. Back-office transformation has shifted from a technological luxury to a strategic necessity.
2. Ambition outpaces action: While many organizations have expressed commitment to digitization and AI integration, only a small minority have successfully implemented AI-powered tools and systems on a large scale.
3. AI readiness is a workforce challenge: AI is poised to handle the majority of routine back-office work, with 81% of executives expecting AI agents to manage at least 75% of these tasks. Yet only 27% of organizations feel their teams are ready for this shift, representing a critical skills gap that could impede digital transformation efforts.
4. The "zero office" is still aspirational: While 77% of leaders believe the traditional back office will vanish within three years, replaced by an automated "zero office," only 21% are taking the bold steps needed to realize that vision. Most are hesitant, caught between legacy and opportunity.
5. Compliance drives change, but capabilities lag: Regulatory compliance is a top driver for back-office transformation. However, only 31% of BFSI firms have predictive, real-time compliance capabilities. As regulations accelerate, organizations must move from reactive to proactive compliance.
6. Investment and expectation are both high: BFSI firms plan to invest an average of $25 million each in back-office transformation over the next two years, with most demanding a return on investment in less than 24 months. This urgency requires clear priorities and a willingness to break from business as usual.
7. Only radical transformation yields true results: The research also spotlights a group of radical transformers—just 21% of respondents—who are investing in enterprise-wide reinvention. These organizations are already reporting higher revenue growth. In contrast, 79% remain stuck in incremental or limited transformation, risking long-term stagnation.

Radical transformers stand out not just in confidence but in results. These organizations treat back-office transformation as a growth engine, not a cost-cutting exercise, and they are seeing stronger revenue growth than their peers as a result. Their intent is matched by action: They invest more aggressively and target transformation that delivers enterprise-wide impact, not just incremental improvements.

For these leaders, customer experience is the guiding star. They view the back office as a direct driver of competitive differentiation, ensuring that every process ultimately supports better digital interfaces and client outcomes. Radical transformers also move quickly to adopt emerging technologies, building fluency in AI, automation and compliance tools that others are still piloting.

Crucially, they recognize that people are at the heart of transformation. Investment in upskilling and workforce readiness is central to their strategy, enabling teams to thrive in AI-driven, prompt-led environments. And while others focus on reducing risks or costs, radical transformers measure success by growth-oriented metrics, including innovation, customer experience and new value creation.

From Incrementalism to Bold Action: The Path Forward

The message from the research is unmistakable: Incremental change is no longer enough. Piecemeal digitization will not bridge the digital ambition gap or deliver the resilience, compliance and customer focus the industry demands. Only bold, enterprise-wide actions such as rethinking processes, investing in talent and scaling AI will separate tomorrow's leaders from the rest.

With BFSI firms accelerating investment and demanding rapid ROI, there is no room for hesitation. Those who move decisively now will shape the future of the industry. Those who delay will be left behind.

AI agents aren't just another layer of automation—they mark a fundamental shift in how insurers can scale decision-making and operations. Unlike traditional tools, they can interpret context, provide recommendations and carry out tasks across multiple systems. For insurers, this isn't about just answering questions—it means executing real work and driving measurable outcomes.

For example, an underwriter can ask an AI agent to review broker submissions, extract risk data and suggest pricing tiers based on historical patterns. A business analyst can use an AI agent to analyze customer lifetime value and identify new retention strategies. A product manager can even have an AI agent configure new insurance products based on specific business requirements. These agents accelerate operations and improve efficiency while leaving judgment and final decisions in human hands.

AI Experimentation Is Not Enough

The potential is clear, but the reality is more complicated. According to Boston Consulting Group, 67% of insurers have experimented with AI, but only 7% have scaled it across their organizations. That means the vast majority remain in pilot mode, running isolated experiments that rarely expand into enterprise-wide capabilities.

That gap between promise and practice is where insurers risk falling behind. AI agents can deliver real value, but not if they remain trapped in proofs of concept. Scaling requires more than one-off pilots—it demands modern infrastructure, aligned leadership and interoperable systems that can evolve alongside the technology itself.

AI Agents Require Modern Infrastructure and Interoperability

Several technological obstacles keep insurers from deploying AI agents at scale. Legacy systems still dominate many organizations, making it difficult to connect AI to core functions like policy administration, billing and claims. Data is often fragmented, inconsistent and locked in silos, limiting the usefulness of even the most advanced models.

Even when insurers modernize their infrastructure, interoperability quickly becomes the new barrier. Today's AI ecosystem is highly fragmented, with each platform requiring custom development to connect with insurance workflows. The result is a patchwork of brittle integrations that are expensive to maintain and risky under real-world demands. Technical debt and compliance pressures only add to the complexity.

This creates vendor lock-in. Carriers often stay with a platform not because it's the best fit but because switching would mean rebuilding their entire AI infrastructure from scratch. The consequences are serious: Innovation slows, costs rise and insurers lose access to emerging capabilities that could deliver better results.

Enter the Model Context Protocol (MCP)

There have been several attempts to solve the AI interoperability challenge. Solutions like LangChain provided some help but locked organizations into specific frameworks, while function calling still required custom glue code for each connection. These early frameworks proved the demand for better connectivity but also exposed the limits of proprietary approaches.

In contrast, the MCP, introduced in 2024 by Anthropic, establishes a universal, open protocol—similar to USB for hardware—that lets organizations write a connector once and use it across different AI models and providers. This standardization eliminates redundant work, enables clean separation between data sources and AI applications, and creates a true plug-and-play ecosystem for AI agent connectivity.

For the insurance industry, the implications are significant. MCP allows AI agents to execute workflows securely, with auditability and governance built in. It enables portability, so organizations can switch AI providers without rebuilding their integration layer. And it accelerates innovation, since new AI tools can be adopted faster and with less friction.

MCP isn't perfect, but it's the most widely adopted solution so far—and a major leap forward in enabling open, interoperable AI systems. That's why it has quickly gained traction among enterprise software leaders including Salesforce, Snowflake, Atlassian, Hubspot and many more.

How Core Platforms Can Deliver AI Connectivity

MCP solves the interoperability challenge, but it does not address the underlying data problem. AI agents are only as effective as the data they can access. If insurers rely on legacy systems with siloed or inconsistent data, even the most advanced AI deployments will underperform.

This is why insurers need modern core platforms built for data fluency—the ability to access accurate and complete data whenever it's needed, in whatever form the business requires. A data-fluent core platform provides:

• Cloud-native data availability and performance to support real-time workflows
• Flexible data access, such as open APIs, data lakes and event streams
• Complete and governed data with metadata, lineage and auditability for compliance

When paired with MCP, a data-fluent core creates the ideal foundation for agentic AI. It ensures that AI agents can connect seamlessly to critical workflows while reasoning across high-quality data. Together, they unlock not just isolated efficiency gains but the potential for enterprise-wide transformation.

The Path Forward

Even with interoperable systems and data-fluent cores, AI agents cannot operate in a vacuum. In a regulated industry like insurance, transparency and accountability remain nonnegotiable. Human-in-the-loop governance—reviewing recommendations, validating outputs, and ensuring fairness—will be essential to earning trust and meeting regulatory requirements.

The insurance industry has reached an inflection point. AI agents are powerful enough to reshape underwriting, analytics, and product design, but scaling them requires both standards like MCP and modern core systems designed for AI connectivity. By embracing open, interoperable architectures, insurers can avoid vendor lock-in, reduce complexity and accelerate innovation.

The winners will be those who understand that AI is not just about smarter models—it's about building the infrastructure that allows those models to thrive. With the right foundation in place, insurers can finally move beyond pilots and unlock AI as a true engine of innovation and growth.

Congress's sweeping reductions to Medicare and Medicaid funding have set the stage for a decade of disruption in the U.S. healthcare system. The headline numbers are staggering: nearly $1.4 trillion in combined cuts over 10 years, reshaping the safety net programs that tens of millions of Americans rely on.

For insurers, the issue goes deeper than dollar amounts. It's about communication. Every coverage adjustment, every eligibility requirement, and every treatment approval must be explained and confirmed, often across multiple parties, before care can proceed. CIOs are suddenly staring down a future where communication infrastructure is the backbone of the entire business, rather than just IT.

Imagine a patient awaiting a time-sensitive surgery, only to have it postponed when a pre-authorization notice is delayed or lost in the system. Follow-up care could be delayed if the insurer never receives a discharge summary. Even something as routine as a coverage update arriving late can cause panic and confusion for a family already under stress.

Rural providers face even more formidable challenges. Small hospitals and clinics are already battling staff shortages and financial strain, and with $137 billion in cuts looming, those pressures are expected to intensify. Many of these facilities still rely heavily on fax because inconsistent broadband access in rural America and limited budgets for digital transformation mean older technologies remain a lifeline. As more patients move from public to private coverage, insurers must prepare for a surge in fax-based communication, not a decline.

This dual reality of modern digital channels and legacy tools means insurers need communication strategies that bridge both worlds. CIOs can't afford to let legacy systems create bottlenecks, nor can they risk patient trust by relying on generic digital tools.

One technology stands out: cloud fax. Unlike traditional fax servers, cloud-based faxing is scalable, transparent, and compliant with industry standards like HIPAA. It integrates smoothly with on-premise, hybrid, and cloud environments, reducing ineffective workflows and ensuring sensitive documents move quickly and securely between providers and insurers. Costs decrease, visibility improves, and compliance boxes get checked without slowing operations.

So where should insurers start? A practical roadmap for CIOs includes three steps:

1. Assess. Identify the customer segments and workflows most at risk. How many policyholders will be affected? Which communication channels will see the heaviest surges?
2. Evaluate. Test your current systems under pressure. Can they scale? Do they deliver consistently? Are compliance and redundancy baked in?
3. Act. Move decisively toward modern, cloud-native platforms that can flex with demand. Partner with providers that understand the stakes in healthcare — not just IT vendors but specialists in secure, regulated communication.

It's tempting to view these challenges as purely technical. But at their core, they're about people. Patients who don't know whether they're covered, doctors waiting for a green light before treating someone in pain, or families making decisions under enormous stress. Every communication failure ripples outward into real lives.

That's why, in the post-cuts era, insurers must treat every message as a lifeline. Those who invest in resilient, secure communication workflows today will be the organizations patients and providers trust tomorrow. Those who hesitate risk finding themselves overwhelmed at best, irrelevant at worst, in a healthcare landscape that isn't slowing down for anyone.