Let's say you're reviewing a claim file. You see a medical record. Then, a few pages later, you see it again. Same doctor, same date, same content. But wait, this second one has a fax stamp in the corner. Or maybe a scribbled set of initials. Or a different date in the footer. Is that a duplicate?

Your gut might say yes. Your system might say no.

Welcome to the strange world of workers' comp duplicity, a world where two records can be 99.9% identical and still be treated as unique by the very software designed to detect sameness.

What Most People Don't Know About Duplicates

We tend to think of duplicates as obvious. Copy and paste. Carbon copies. But in the land of medical records, things get trickier.

Did you know that only 38% of duplicates in workers' comp are exact matches? The other 62% are "soft duplicates" or "near duplicates" — records that look the same to the human eye but fly under the radar of most third party administrator (TPA) and carrier systems because of minor formatting or metadata differences.

Some of the most common disguises include:

• Fax headers
• Page numbers that shift
• Highlighting or handwriting
• Updated logos
• Minor changes to margins, headers, or timestamps

To a human reviewer, these differences are irrelevant. To most legacy systems, they're enough to confuse or cause them to be missed entirely.

Why This Matters

Every time a duplicate sneaks into a claim file, it costs time. Adjusters scroll, reread, and second-guess. Defense attorneys over-prepare. Bill reviewers re-review. And in MLPRR-reimbursed cases, carriers can end up footing a much larger bill than necessary.

In one real California case, duplicate medical records caused a $58,000 MLPRR overcharge for a well-known TPA that claimed to have a "duplicate removal system" in place. No one caught the error until after reimbursement.

It's not just an efficiency problem. It's a clarity problem. It's a cost problem. It's a "why is this case so hard to close?" problem.

The worst part? Most teams don't even know it's happening.

The Philosophical Side of the Problem

This isn't just a technical issue. It's an identity crisis.

In workers' comp, there's no need to include duplicates. The reason carriers and TPAs spend such a significant amount on redundant records is because their current systems aren't removing them efficiently.

Most systems default to pixel-by-pixel comparisons or simple hash-matching, which means that one extra date stamp on a medical note can mean the difference between a clean file and a bloated one.

Judges at the Division of Workers' Compensation (DWC) have taken notice. They're looking for carriers, TPAs and law firms that address this wasteful spending. The problem has become so central to efficiency and fairness that industry experts are developing more sophisticated solutions.

It's time we evolve our understanding of what counts as a duplicate. And more importantly, it's time we stop letting outdated tools decide for us.

So What's the Fix?

Advanced tools are emerging that study the problem deeply, not just what duplicates are, but what duplicity looks like in the real world. These solutions don't just match PDFs. They evaluate semantic meaning, formatting shifts, and intent. They understand that sameness in workers' comp is a spectrum and address the inherently computing-intensive challenge of document comparison, where each page must be compared against all others. For context, a 1,000-page document demands a staggering 499,500 comparisons to identify all forms of variation across text and images.

These advanced tools can find all of the 62% that get missed. They can restore clarity to chaotic files. They can save hours of adjuster time and weeks of attorney time and eliminate unnecessary review work.

And yes, such solutions exist.

Hyper-volatility refers to a state of extreme and unpredictable fluctuations in global systems, such as financial markets, energy prices and insurance markets. In insurance terms, hyper-volatility involves events typically in the "fat tail" of the distribution, beyond the 95th percentile, driven by simultaneous or cascading effects, including extreme weather combined with geopolitical instability.

Geopolitical risk, like climate risk, includes both short-term shocks that lead to one-off losses that demand crisis management and persistent issues requiring strategic shifts and a change to longer-term risk management practices.

While risk managers often model risks independently – meaning they look at risks in isolation – climate change is a risk multiplier. It can increase the correlation between different risks and, in particular, between natural catastrophe and geopolitical risks.

Hyper-volatility-driven and connected risks challenge risk managers' and insurers' ability to predict outcomes. WTW's latest research points to both increasing connectivity between risks and the challenges organizations face in managing the unpredictability this generates. Managing risks individually using only traditional modeling methods could prove increasingly inadequate.

For organizations to shield themselves from the impacts of hyper-volatility and address the insurance gaps being created, risk managers need to adopt a modernized approach. This is about coupling traditional modeling approaches with analytical insight and scenario stress-testing that incorporates the connected nature of risks.

Understanding the challenge

Where one risk in a global system amplifies another, it tests the effectiveness of traditional risk management approaches. Traditional modeling techniques, such as pure reliance on probabilistic model outputs on a siloed, risk-by-risk basis are often falling short. They fail to capture key aspects of the real world, including the combined effects of acute physical risk, politics and policy, unemployment, finance, asset prices, volatility, tipping points, path dependency and complex feedback loops, according to research from Green Futures Solutions, to which WTW's Thinking Ahead Institute contributed.

We've seen climate change be a threat multiplier for geopolitical risk, and vice-versa, providing examples of complex feedback loops not reflected in standard risk models.

Consider climate change. It can increase the frequency and severity of extreme weather events like floods and droughts, which can not only disrupt local communities but also have far-reaching impacts on global supply chains. Geopolitical tensions, meanwhile, such as trade disputes and conflicts over natural resources or access to water, can exacerbate climate-related disruptions, leading to greater political instability and economic uncertainty.

Developments in the Arctic bring this complexity to life. The reduction of sea ice due to global warming is opening up new shipping routes. These are prompting disputes over which nations can control the new seaways and benefit from vast undiscovered natural resource deposits. Geopolitical tensions among the five Arctic coastal states — Canada, Denmark, Norway, Russia and the U.S. — as well as players with an interest in the region, including China, will no doubt affect supply chains. The situation shows connectedness, complexity and the conditions for wide-ranging unpredictability driven by cascading effects.

Managing hyper-volatility

Managing hyper-volatility requires more than isolated risk assessments. It asks for a connected view of how multiple threats interact and evolve. Scenario analysis offers a powerful way to address the unpredictability of hyper-volatility by capturing how connected risks – such as extreme weather, geopolitical tensions and supply chain disruptions – can cascade and amplify one another.

Unlike traditional models that often treat risks in isolation, scenario analysis enables risk managers to explore fat-tail events and test the resilience of assets, operations and business models under severe but plausible conditions.

However, to translate these narrative scenarios into actionable insights, they must be grounded in data. That's where multi-peril indices come in. By combining diverse risk indicators – climate, conflict, supply chain stress – into a single quantitative measure, these indices provide a real-time view of systemic vulnerability.

Together, scenarios and multi-peril indices can enable your organization to simulate future shocks, monitor current risk build-up and make faster, more informed decisions as conditions change. This approach can also work to reveal the optimal combinations of risk transfer, retention and physical adaptation in the face of hyper-volatility.

Putting theory into practice

By factoring in correlations between different risks, an organization can avoid viewing risks in silo, which is particularly crucial to avoid when carrying out due diligence and investment planning.

Consider a manufacturing site investment. Rather than assessing property, climate, geopolitical and supply chain risks separately, scenario analysis can model how these risks might interact under a plausible event or cascading set of disruptions. A multi-peril index framework can then quantify the combined exposure at specific locations, enabling you to compare sites and validate or reprioritize projects based on overall risk levels.

Supply chains are another area where this approach is essential. Imagine a food retailer assessing the impact of climate change on fish supplies. Scenario analysis can map how rising temperatures might affect stock availability and quality, while also exploring how geopolitical instability, such as trade restrictions or regional conflict, could disrupt fishing zones or export routes. A multi-peril index can then track these combined pressures across geographies, helping identify critical vulnerabilities and timing thresholds.

This insight allows risk managers to build a risk register and develop adaptive strategies to manage hyper-volatility, such as diversifying suppliers, investing in sustainable practices or strengthening infrastructure.

Cyber risk doesn't stop at the firewall. From cloud platforms and payroll processors to customer support software and data analytics tools, the average organization now relies on a complex ecosystem of third-party vendors. This growing web of digital interdependence has created a new frontier of exposure, one that traditional cyber insurance models are not equipped to handle. It's a new frontier of exposure for buyers of cyber insurance, too, as to date they have been underwritten primarily based on a carrier's understanding of their cyber controls, rather than concern for the cyber posture of their third-party vendors.

While cyber insurers have made meaningful progress in maturing their underwriting models, supply chain risk remains a persistent blind spot. Despite rising awareness, the industry continues to underestimate the operational and financial exposure introduced by third-party vendors. As the frequency and severity of vendor-related incidents grow, insurers and enterprises alike must rethink how they assess, measure and mitigate this form of connected risk.

Assumptions That Fall Short

The challenge is not a lack of concern. It's a lack of clarity. Many underwriting models today rely on assumptions and heuristics to estimate vendor exposure. For example, some insurers approximate concentration risk by applying vendor market share estimates to their book of business. This approach misses the nuance of actual enterprise dependency. A software vendor with a small market share may be a critical integration partner for dozens of policyholders. Conversely, a widely used vendor might have minimal operational importance in certain segments. Without visibility into these relationships, insurers are flying blind.

Recent incidents have underscored this problem. High-profile breaches traced back to third-party vendors have caught insurers and policyholders off guard, not because those vendors were unknown but because their risk wasn't understood. One example is the breach of CDK Global, a widely used vendor serving U.S. auto dealerships. The incident triggered cascading disruptions across hundreds of businesses. An Eastern European and Russian hacker group, thought by security researchers to be BlackSuit, claimed responsibility and demanded tens of millions of dollars in ransom. 

Despite insuring many affected policyholders, carriers were unaware of the shared dependency or the magnitude of its potential impact. At least eight lawsuits alleging negligence were filed against CDK by dealerships whose operations were affected by the outage. Within the first two weeks, the dealers recorded financial losses amounting to approximately $605 million. 

The implications of a network interruption resulting from a third-party vendor having a network outage became only too clear with this event. Events like the one that affected CDK are not exclusive to technology vendors. Organizations need to consider the risks associated with all types of vendors they work with.

Flawed Inputs, Flawed Outcomes

Why does this keep happening? Part of the problem lies in how enterprises classify and evaluate their own vendors. Traditional procurement processes may assess vendor "fit" and financial stability but often overlook cybersecurity control posture or fail to quantify how critical a vendor truly is to business operations. Even when vendor risk assessments are conducted, they're rarely shared upstream to inform insurers' portfolio-level analysis.

To solve this, the industry needs a new model, one that accounts for both technical controls and operational dependency. A vendor with weak cybersecurity hygiene may not pose significant exposure if they are loosely integrated and easily replaceable. Conversely, a vendor with strong controls may still introduce high systemic risk if their service is deeply embedded into business-critical workflows.

A Blueprint Already Exists

This dual-lens approach is already in use by leading enterprises, especially in financial services, where vendor risk oversight is a decades-old discipline. These organizations combine third-party cyber risk insights with internal assessments of vendor criticality to make more informed decisions. Insurers can follow suit by encouraging greater transparency, standardizing reporting frameworks and adopting technologies that can scale risk evaluation across thousands of policyholders.

Just as the requirement for multi-factor authentication has become standard in underwriting, we now need to expand expectations to include vendor risk transparency and supply chain assessment. The industry must evolve beyond evaluating the insured in isolation.

Opportunity for Industry Leadership

The good news? We're not starting from scratch. Emerging data sources, improved telemetry and advances in automation make it increasingly possible to map vendor dependencies and evaluate cyber posture at scale. But technology alone isn't enough. New ways of quantifying risk, incorporating a company's third-party vendor risk alongside historical elements of risk are being developed. Insurers, brokers, security professionals and enterprise leaders must work together to close the supply chain visibility gap.

This isn't just an underwriting challenge. It's a systemic risk to the broader digital economy. Addressing it will require more collaboration, shared standards and a willingness to evolve outdated models. The cyber insurance industry has an opportunity to lead the way. Let's not wait for the next breach to prove how urgently that leadership is needed.

Modern insurance agencies serve a diverse clientele across regions where multiple languages are commonly spoken. Consider the difference between a client struggling to understand policy details in their second language versus receiving explanations in their native tongue. The latter creates confidence and clarity during critical decision-making moments.

Language barriers affect policyholder acquisition and retention rates for insurers. When policyholders cannot understand their coverage or communicate their concerns effectively, they seek alternatives. This challenge becomes more pronounced during claims processing, a time when clients are under stress and need clear guidance in their preferred language.

To overcome such consequences, insurance agencies should consider leveraging CRM software equipped with multilingual capabilities. These systems enable insurance agents to document interactions accurately while ensuring nothing gets lost in translation. Multilingual CRM for insurance agents provides substantial advantages in both client acquisition and retention.

Delivering Exceptional Customer Service

Modern insurance agencies now adopt multilingual functionality in their CRM systems as a competitive edge. CRM software integrated with multilingual functionalities eliminates the language obstacles for policyholders and agents. This creates new growth opportunities and delivers exceptional service.

1. Expanded Market Reach

Multilingual CRM enables insurance agencies to move beyond their usual boundaries. Agents can serve regional Internet users more effectively by speaking their native language. In many markets, these users outnumber English speakers significantly. This approach helps agencies discover the potential of previously underserved communities.

Prospects trust you more when they hear about coverage options in their preferred language. This local connection becomes the foundation for successful market growth. Brokers can build authentic relationships with prospects from different linguistic backgrounds.

2. Improved Customer Experience

CRM software with multilingual capabilities turns everyday interactions into meaningful connections for insurance brokers. Speaking the customer's language creates a personal touch that strikes a chord deeply. This applies to everything from initial policy explanations to continuing service.

The emotional effect runs deep – clients feel genuinely understood when they discuss complex financial products in their native language. Picture a localized onboarding call where know your customer (KYC) processes, details, and questions happen in the client's preferred language. This shows respect for their identity and cultural background, which encourages stronger relationships.

3. Uninterrupted Claim Support and Increased Policyholder Loyalty

The claims process marks a crucial moment in insurance relationships. Multilingual CRM aids two-way communication during these sensitive times. Claimants feel immense relief when they find someone who speaks their language during stressful situations.

Speaking the same language speeds the entire claims process by removing communication barriers. Policyholders who get support in their native language show higher retention rates and loyalty. They appreciate their provider's extra effort to meet their needs.

4. Prevention of Costly Communication Errors

According to a recent survey, around 82% of policyholders interact with insurers via subprime communication channels. Complex insurance terminology challenges even native speakers, making precise communication crucial for policy accuracy. Multilingual insurance agent CRM software prevents misunderstandings about coverage terms, exclusions, and policy conditions. Clear communication ensures policyholders understand their protections completely.

Core Technological Components

Multilingual insurance CRM systems rely on sophisticated technological frameworks designed specifically for insurance management complexity. Several integrated components in CRM software for insurance brokers work together to create seamless language experiences for agents and policyholders.

● Language Management Systems - These systems control content display across multiple languages within insurance CRM platforms. They manage dictionaries, translation memories, and language-specific formatting requirements. Policy details appear correctly regardless of the selected language, ensuring accuracy in complex insurance documentation.

● Dynamic Content Translation Engines - Translation engines are equipped with natural language processing algorithms to understand insurance terminology and maintain nuanced meanings critical in policy documents. Unlike simple word-for-word translation tools, these specialized engines preserve the precise legal meaning of complex terms like "subrogation" or "indemnity" across different languages. This precision protects both agencies and clients from costly misunderstandings.

● Database Architecture – An architecture supporting multilingual CRM for insurance agencies employs metadata tagging that allows core information presentation in multiple languages without duplication. This architecture maintains a single source of truth while enabling flexible language presentation—essential for consistent policy management across diverse client bases.

● Multilanguage Search Functionality - This functionality allows insurance agents to search and discover client documents, policies, and records irrespective of creation language. This cross-language search capability proves invaluable in international insurance operations where client information might exist in various languages.

These technological components work together to remove language barriers from the insurance industry. Brokers can now offer tailored services to clients from diverse backgrounds.

Challenges

Insurance companies face multiple hurdles while adding multilingual features to their CRM systems. The challenges go well beyond simple translation. Many insurance firms struggle with integrating language support into their customer relationship platforms, even though the advantages are clear.

I. Complexity in Language Localization and Handling Regional Dialects

Insurance terminology necessitates accuracy that primary translation tools cannot offer. Words like "franchise" in French-speaking regions versus "deductible" in English-speaking areas illustrate how specialized terms vary across markets. Insurance CRM systems must account for regional dialects where the term "carro" means "car" in most Spanish-speaking countries but can mean "cart" in certain regions, potentially causing significant confusion during claim discussions.

Insurance tech service providers address these challenges through specialized linguists with deep insurance industry expertise. These experts understand the nuanced meanings critical to accurate policy representation across languages, ensuring that technical terms maintain their precise legal and financial implications.

II. Data Synchronization Across Language Variants

Maintaining consistent information across multiple language versions presents substantial technical obstacles for insurance agencies. Without proper synchronization, agencies risk presenting contradictory information to clients depending on their language preferences. Insurance tech providers overcome this through sophisticated database architectures that employ metadata tagging, maintaining a single source of truth while enabling flexible language presentation across all client touchpoints.

III. Multilingual Customer Communication Automation

Automating personalized communications across languages creates unique challenges, especially during claim processing scenarios where precision becomes paramount. Advanced insurance tech providers integrate contextual communication systems in multilingual CRM for insurance agencies that identify client language preferences from profiles and automatically generate appropriate correspondence. These systems maintain consistent branding while ensuring regulatory compliance across different jurisdictions.

IV. Compliance With Legal and Regulatory Requirements

The biggest challenge lies in navigating different regulatory frameworks across jurisdictions. Each country has its own rules for insurance disclosures, reporting, and transactions. Dedicated insurance tech providers handle this with compliance frameworks that stay updated. These frameworks automatically enforce regulatory requirements like KYC procedures and data retention policies without manual labor.

Insurance tech service providers with specialized expertise help insurance agencies navigate these complex challenges. They enable effective multilingual customer support that stays precise, culturally sensitive, and compliant with regulations.

Final Words

Language-enabled CRM systems give insurance agencies new ways to build stronger client relationships. Language hindrances make it challenging to establish trust and improve client relationships. Companies that use multilingual systems have a clear edge over those stuck with single-language operations.

Multilingual CRM systems tackle insurance's biggest problem -- its complexity. Clients understand their policies better when information comes in their native language, which builds confidence at key decision points. These systems also keep detailed records of client conversations, so nothing slips through the cracks during claims or policy changes. Insurance tech service providers offer specialized solutions that address the complex challenges of multilingual CRM implementation. Their expertise enables insurance agencies to overcome technical hurdles while maintaining precision in policy documentation and client communications.

Mid-August is supposed to be slow in the world of business, as people get their final bits of vacation in and prep for sending the kids back to school, but quite a number of items caught my eye this week. 

I'll start with an analysis of the costs of third-party litigation funding, which projects that insurers could pay as much as $5 billion a year directly to those who are investing in lawsuits against insurers. Including all the indirect costs associated with fighting those lawsuits, insurers could be out as much as $10 billion a year, the research finds. Those numbers are scary—much higher than I, at least, would have guessed.

Then I'll share an item on how parents are increasingly getting some control over their teen drivers and their at-times-ill-considered behavior, which could bring down claims while advancing everyone's goal: fewer accidents.

Finally, I'll reflect on the end of AOL's dial-up internet service and what it says about technology life cycles, such as the generative AI revolution that is just moving out of its Wild West phase and into what I think of as the land grab era. That reflection will also let me tell my story about how an inability to find a modular phone jack in the Berkshires in the early 1990s almost kept me from filing an exclusive on IBM that led the Wall Street Journal the next morning. 

Third-Party Litigation Funding

An article in Claims Management quotes "an actuary speaking at the Casualty Actuarial Society’s Seminar on Reinsurance [as saying] the top end of a range of estimates of direct costs that will be paid to funders by casualty insurers is $25 billion over a five-year period (2024-2028)."

The article also quotes another actuary who came up with a smaller, but still frightening, number. He ran 720 scenarios and found that "the five-year cost is most likely to fall between $13 billion and $18 billion (the 25th to 75th percentile), with a mid-range average coming in at around $15.6 billion for the five years from 2024-2028."

This actuary noted that the payments to those funding the litigation could snowball: The funds could let law firms advertise more, bring in more cases, and fight claims longer.

He also cited a study that "puts total costs, including indirect costs, at roughly double the amount of direct costs.... If this were true, the high end of the range—now $50 billion—could add 7.8 points to the commercial liability industry loss ratios for each of the next five years, with the most likely scenario (50th percentile) falling between 4.5 and 5.5 loss ratio points."

Those are scary numbers—that not only hurt insurers directly but will filter into much higher rates for everyone. 

(If you're interested in how the industry can combat third-party litigation funding, you might check out the work being done by our colleagues at the Triple-I, including this piece on the need to increase transparency about who's putting up the money and about what suits they're funding.)

Safer Teen Drivers

When my older daughter, Shannon, was 16, my wife couldn't sleep one night and went off to watch some TV. CNN ran a long piece about dangerous rain, concluding with footage of a car driving through a stream of water crossing a road. The announcer intoned, "Whatever you do, don't do this." My wife was suddenly wide awake. "That's my car!" she said to herself.

It was, too. She ran the video back and confirmed that her low-set sports car was, in fact, being driven through a flash flood. Shannon made it fine, but our new driver was caught.

She explained the next day that she'd been driving to her early-morning horseback-riding lesson and desperately didn't want to be late. She only knew the backroads route to the barn, so she decided to press on, somehow not wondering why a camera crew was set up by the side of the road. 

She made it, but her mom and I delivered a stern lecture with all kinds of threats attached. And at age 31, Shannon has not only not had an accident but hasn't even had a moving violation. The same goes for her 29-year-old sister.

I've joked for years that teaching a teen to drive is easy. You just need to have a CNN crew follow them around.

That's sort of what's happening through telematics, such as dashboard cameras, as this survey from Nationwide highlights. 

There is a long way to go: The survey finds that 96% of parents think dashcams are valuable but that only 26% of teens currently use them. Still, I see encouraging signs. A recent report by Cambridge Mobile Telematics found that the use of games and social media while driving—a problem especially acute among young drivers—has plunged. And traffic deaths on U.S. highways have now fallen for 12 consecutive quarters and were at their lowest in six years in the first quarter of 2025. The number of deaths—8,055—was still ghastly, but was down 6.3% from a year earlier.

Here's to progress.

The End of AOL Dial-up

To most people, the news about AOL was probably that it still offered dial-up, not that it was finally ending the service. But technology has a "long tail," which is why 163,000 Americans were still using dial-up in 2023, why insurers and other business are still having to deal with programs written in COBOL (a language designed in 1959), etc.

The surprise about the long tail made me think it's worth spending a minute on the various stages of a technology megatrend like internet access or, say, generative AI, because there are some other surprises, too, that matter today.

The main one is the overinvestment that frequently happens. People wonder how companies building large language models can justify the hundreds of billions of dollars A YEAR that they're spending just on AI architecture. And the answer is... they can't. Not in the aggregate. 

But each of the big players can justify its spending individually because the potential win is mind-boggling. Yes, Meta may be getting carried away by offering $100 million signing bonuses to individual AI researchers and by spending some $70 billion on AI infrastructure this year to overcome what's generally seen as a lagging position, but Meta will generate trillions of dollars in market cap if the bet pays off.

In general, a tech megatrend goes like this: 

• The Wild West
• The land grab
• The near-monopoly
• The long tail

With AOL, the Wild West was the mid-1990s, when everyone wanted to get to the internet but wasn't quite sure how to do it. Dial-up was a known way to connect to a computer, but there were loads of competitors for AOL. Meanwhile, cable modems were becoming a thing, while DSL was also claiming to be the high-speed solution. WiFi was in its infancy, and Bluetooth was being positioned as a better wireless solution. 

Dial-up was pretty quickly outpaced by cable modems as a technology but still won a mass audience, setting AOL up nicely for the land grab.

The land grab was when AOL blanketed the Earth with CDs that gave people immediate access to AOL's service (and played fast-and-loose enough with the accounting for its marketing expenses that it eventually paid a fine to the Securities and Exchange Commission). AOL won the land grab—and was fortunate enough to merge with TimeWarner at a widely inflated stock market valuation for AOL before internet access moved to its next phase, where AOL lost big time.

That next phase, the quasi-monopoly, actually didn't happen as quickly or decisively as it did with, say, IBM-compatible personal computers, Google's search engine or Facebook in the early days of social media. AT&T and Verizon to needed many years to emerge as the dominant players. But it did become clear quickly that AOL's "walled garden" approach was a loser. AOL wanted users to sign in through its dial-up and then never leave its site; users were to do all their banking, shopping, etc. through AOL. That approach has worked for Apple, but it became clear in the early 2000s that users were going to branch out across the internet as companies figured out how to make their sites more accessible. Whatever was going to emerge as the quasi-monopoly, AOL wasn't going to be part of it.

AOL had a market cap of $164 billion when it merged with TimeWarner in 2000, but fell so far that it was sold to private equity in 2021 for just $5 billion, even though stock market indices had roughly tripled in the interim—and that price included Yahoo, another former high flyer, and Verizon's ad tech business. 

That sale just left the long tail, some 35 years after AOL was founded. 

When you apply my model to AI, I'd say we're toward the end of the Wild West phase—we're not likely to again see something like Sam Altman getting fired as CEO of OpenAI, then almost immediately rehired. 

We're starting to move into the land grab, even though the technology isn't fully sorted out. Depending on how quickly a new battleground takes shape for agentic AI, we might see the technology sorting out in the next couple of years. The sorting out is when you'll see the big shakeout in the stock market valuations as companies that spent many tens of billions of dollars on AI are identified as losers. (My bet is that Tesla will be the first to lose the hundreds of billions of dollars of market cap linked to its AI aspirations, but we'll see.)

I realize this piece has run far longer than normal, but here's my story about a near-catastrophe with dial-up:

Some friends had recently bought a house in the Berkshires, and I joined them for a weekend there in the early '90s. I had reported a scoop on IBM that I knew would lead the WSJ on Monday and wrote it Sunday afternoon on the crummy little TRS-80s that we still used in those days. We then realized that their home's phones were hard-wired into the walls, so there was no way to plug in a cord.

With deadline approaching, we drove to the nearest town, Huntington, Mass., but couldn't immediately find a solution. We finally went into a store so tiny that we could see the curtain that separated the store from the room in the back where the proprietor lived. She had a modular phone. I said I'd pay her $20 if she let me make a one-minute long-distance call to New York. She looked at me like I had two heads, but she agreed.

The problem wasn't over. Her phone was set rather high in the wall, and the cord that connected the wall jack to the phone was only about four inches long. To plug the cord into my computer, I had to hold it above my head. That meant feeling my way around the keyboard as I dialed the number for the computer in New York, waited for the exchange of tones, and then typing blind as I inputted the code that gave my computer access to the WSJ system. 

It took a few tries -- as the proprietor watched anxiously, wondering what I was doing to her long-distance bill -- but it finally worked.

I hated dial-up. I'm glad it's finally on its last legs.

Cheers,

Paul

With mobile ordering, digital banking, and payments, technology has moved life into hyperdrive, and this expectancy trickles down to all other areas, including more traditional services like insurance.

According to LIMRA, the U.S. individual life insurance premium set a record last year, with total new annualized premium increasing by 3%, but the sector is often criticized for its slow pace and limited digital presence. Manual and lengthy processes add to the natural complexity of underwriting. To gain a competitive edge, build customer loyalty and trust, and reach underinsured demographics, insurers must recognize one truth – underwriting is in urgent need of transformation.

Meeting modern expectations with not-so-modern technology

Too many insurance companies still rely on legacy technology to complete core functions. This technology cannot keep up with the demands of a modern industry, as it lacks the flexibility and scalability that are essential for growth. As insurtech continues to raise the bar with modern solutions, traditional insurance organizations are falling behind. This gap is monumental and will continue to grow if left unchecked. Legacy workflows make underwriting an arduous process and limit the speed of customer support and policy fulfillment. This may force customers to abandon their transactions altogether or to look elsewhere, damaging a firm's bottom line.

Technology to support insurance enters an efficient, client-focused era

According to the 2025 Insurance Barometer, 74 million Americans need life insurance, while 22% of individuals who do not own insurance stated that they were not sure how much they needed or had a clear understanding of what to buy. 41% of U.S. adults stated they are "somewhat or not at all knowledgeable about life insurance."

With the current market uncertainty, consumers are feeling pressure to do more to protect not only their own financial security but also thinking of their loved ones and shielding them from future financial struggles. Yet, many may be unsure of how to move forward.

This is where agents, advisors, and distributors play a crucial role in educating prospects and customers. However, the science of underwriting is not straightforward, and finding and securing the right policy for a customer can be complex. Various factors go into play, and the lengthy back and forth process can leave end customers more perplexed and, even worse, cause them to abandon the process. Digitized solutions can support intermediaries in navigating the complexities of the underwriting process by helping them inform customers early in the cycle what policies they are likely to be approved for, taking the process from weeks to minutes. AI-driven predictive models can also help distributors and agents identify and provide policy recommendations best suited to each individual's needs, which can further streamline the process and meet customer demands for greater personalization. AI also enhances transparency in underwriting by evaluating decisions in real time as data is gathered. This accelerates approvals while making the process feel more personalized and intuitive, helping build trust and improve customer satisfaction.

To enable AI's full potential, robust, structured, and unified data plays a critical role. While carriers and distributors host a mountain of client data, it is often not used strategically to help drive business growth. For example, customers looking to adjust life insurance or annuity products to meet their evolving needs often face the frustrating task of starting from scratch and being required to re-enter their information, a tedious and off-putting task. This pain point can be alleviated by leveraging digitally led solutions that facilitate automation. Automation provides the ability for forms and documents to be auto-filled based on a customer's past profile and stored data, ultimately saving individuals' time and avoiding any dissatisfaction due to repetitive tasks.

Mind the gap – personalized approach to reach underinsured demographics

Data can also play a key role in helping the L&A sector to reach new customers. According to the 2025 Barometer survey, 43% of women indicate "they need (or need more) life insurance." The gender disparity in life insurance coverage persists, and while there has been progress over the years, closing the gap should remain a priority.

With no two financial journeys the same, each consumer's financial decisions and trigger points on what they need and why they need it are going to differ. The L&A sector has the opportunity to meet the diverse needs of various demographics. By using predictive analytics, firms can leverage new insights to help create tailored products as well as personalized distribution strategies that align with the specific needs and expectations of new market segments.

Beyond customer expectations – the talent crunch

It's no secret that the insurance and annuities industry is currently facing a challenge due to an aging workforce, and this is promoting executives to concentrate on attracting new talent to fill this gap. However, this task is more challenging than it might seem, as the sector struggles with a branding issue and is often perceived as outdated, primarily due to the underuse of innovative technology solutions. It is crucial to move away from this stereotype and modernize the industry to avoid falling behind, especially considering that there are approximately 70 million members of Generation Z in the U.S. alone. As they enter the workforce, this cohort has a strong demand for technology.

As digital natives, they expect modern solutions and are unlikely to compromise on these requirements. Recognizing this reality and making a concerted effort to advance digital initiatives should be a priority for executive leaders. Equipping current teams with digital tools not only enhances efficiency but also serves as a strong selling point for attracting talent that is focused on modern solutions.

Technology to accelerate underwriting needs

Overall, traditional processes, including underwriting and customer service interactions, are going to inhibit growth if they are not improved to fit the demands of today's speed-driven world. However, technology is quickly reshaping this space by accelerating workflows such as underwriting, distribution, and customer service using predictive analysis and automation.

Digital transformation is going to be the central factor affecting insurance firms' success in the coming years. And much like the expectations of today's end customers, the key word is speed, as those that embrace technology the quickest will take the lead.

Not long ago, insurers' principal interest in tracking climate-related and environmental, social, and governance (ESG) metrics was in satisfying compliance-related reporting requirements. Insurers relied on historical data from limited numbers of sources to do so.

While regulatory reporting remains a driver, that's changing fast as predictive analytics evolve from a compliance exercise to a strategic risk-management tool. Climate disasters will cost an estimated $328 billion this year, of which about 40% will be insured, and those numbers are expected to rise at about the same 6% annual clip as in the recent past.

So it's no surprise that 83% of insurance executives view predictive analytics as "very critical" for the future of underwriting. And it's cause for concern that just 27% of property and casualty (P&C) carriers say they have the ability to leverage predictive analytics in their underwriting models.

Predictive-analytics models can reduce risk exposure, identify insurable risks, and sharpen pricing. That combination can help boost profitability by avoiding losses and insuring what might otherwise have been avoided in a less-sophisticated era.

Investment-side benefits are at least as important as underwriting gains

The benefits of advanced analytics in assessing climate and risks related to environmental, social and governance (ESG) issues extend to insurers' investment portfolios. Without analytics touching investing as well as underwriting, insurers can find themselves exposed on both the claims and investment-portfolio fronts. For instance, as we enter hurricane season, an insurer with P&C liability as well as municipal bond holdings in coastal Florida could end up suffering a double hit after a storm sweeps through.

The question the roughly three-quarters of insurers who still lack climate and ESG-related analytics should be asking is not whether it makes sense to establish such capabilities but rather how to go about it. The playbook will differ depending on an insurer's scale, market distribution, and underwriting and investment portfolios. But there are three fundamental steps to consider.

First, predictive analytics is about data, and while generative AI may be able to work from the unstructured masses, predictive analytics and the emerging agentic AI that delves into the numbers need clean, high-quality data. In both cases, developing cloud-based repositories of rationalized data is essential. The data-analysis process typically leads back to applications, many of which can be trimmed down and consolidated – a bonus.

Second, predictive analytics needs tons of data, and from many sources. In the climate-risk realm, external weather and geospatial data may need to be merged with internal geographic risk factors, claims and payment data, economic data, demographic data, and so on.

Querying such combinations enables hyperlocal predictive analysis and individualized risk scores for property-tailored pricing – for example, based on the age, location, and materials of a structure that's prone to storm surge or wildfire or based on a farm's crop selection, water usage and, by extension, its resilience against drought. There's a customer-service benefit here also, because the insurer can demonstrate precisely why a policy has been priced as it is, boosting transparency and trust.

Getting there takes data assimilation into data lakes, ideally incorporating systems integrated with enterprise resource planning (ERP) that funnel third-party as well as an insurer's business data into repositories powering predictive-analytics capabilities in both the underwriting and investment sides of the house – in addition to providing for detailed sustainability tracking and reporting.

Third, predictive analytics is also about people. Given the power of predictive-analytics models, underwriters in particular may feel threatened by these models' introduction and proliferation. The maturation and increasing sophistication of AI in predictive analytics will only exacerbate that. So, involve underwriters early. Foster a rapport between analytics specialists and underwriters to make sure analytics enhances rather than hinders underwriter workflow. Show underwriters how predictive analytics can help them improve portfolio profitability, then monitor and encourage their use of these new tools.

Predictive analytics for climate and ESG risks are already out there

Some of the world's biggest insurers are leading the way with predictive analytics for climate and ESG risks. Aon incorporates chronic as well as acute risks in climate modeling to assess commercial customers' risks down to the asset level, covering freeze risk, extreme precipitation, flooding, extreme heat, drought, and more.

Allianz's Climate Adaptation and Resilience Service (CARes) platform includes a self-service tool to translate climate risks into financial and physical loss metrics at portfolio and location levels. On the investment side, its Sustainability Insights Engine (SusIE) embeds climate-relevant data into its portfolio decision-making process.

Also on the investment side, AXA IM analytics provides ESG scores across its asset classes for use by portfolio managers and analysts companywide, and AXA XL's in-house specialists bring in data from catastrophe modeling firms to understand and predict climate risks on both the underwriting and investment sides of the house.

Swiss Re's ESG risk assessment tool ranks potential transactions based on risks and even gives a direct recommendation to abstain. It uses both proprietary data based on country, sector, and a company and project watchlist, and brings in external data from Rystad, SBTi, and others.

These giants are among the pioneers of new approaches to bringing climate and ESG advanced analytics into the cores of their businesses. Others must now follow. Given the stakes of foggy risk assessments in a world where climate disasters are increasingly common, what was once a question of reporting is now one of survival. The first step is to gain command of your data, and there's no time to waste.

Health plans today are under pressure to deliver on behavioral health parity, not just in theory, but in practice. Yet ask any payer executive what area causes the most administrative friction, and behavioral health will almost certainly top the list. From opaque admission justifications to inconsistent treatment documentation, psychiatric care continues to be an operational outlier.

That mismatch between need and efficiency is becoming a crisis. Behavioral health units are closing at an alarming rate, not because demand is down but because operating them has become too difficult. At the same time, health plans face escalating costs and rising complaints from members who struggle to access timely, high-quality mental health care.

It's easy to assume this friction stems from stigma or lack of will. But the truth is more structural. Behavioral health lacks the operational scaffolding that underpins other areas of medicine, namely, standardized ways to measure patient acuity and track outcomes. Without that foundation, it's nearly impossible to make the behavioral health ecosystem function smoothly for payers, providers, or patients.

Why Behavioral Health Lags Behind

In cardiology, oncology, and orthopedics, providers can point to lab results, imaging, or a consistent scale to justify their clinical decisions. A patient with a certain ejection fraction or lesion size will almost universally qualify for a given procedure or medication. This data-driven standardization enables payers to make faster, more consistent determinations about coverage and necessity.

Psychiatry, by contrast, operates in a far more subjective realm. Clinicians rely on clinical judgment, observations, and interviews to determine whether a patient meets criteria for inpatient care or continuing treatment. But without shared acuity benchmarks or universally accepted scoring tools, the same patient might receive very different assessments depending on who's evaluating them.

This subjectivity creates a perfect storm for prior authorization disputes. Payers aren't necessarily denying care out of bias. They simply don't have the tools they need to confidently approve it. A recent study from the U.S. Government Accountability Office found that commercial insurers are more likely to deny inpatient behavioral health stays than comparable medical ones, in large part due to documentation gaps and ambiguity around clinical justification.

The Cost of Operational Friction

This ambiguity ripples downstream in expensive and disruptive ways. First, it drives up administrative costs for both payers and providers, as clinical teams go back and forth submitting new notes, clarifying documentation, or appealing denials. 

Second, it damages member experience. Patients and families often don't understand why behavioral health claims take longer to process, or why care is harder to access, and end up frustrated with both the insurer and the healthcare system as a whole.

Third, the lack of standardized data undermines care quality. Without consistent acuity scoring and outcome tracking, providers can't easily benchmark performance or spot systemic issues. Payers, in turn, struggle to evaluate network adequacy or support high-performing facilities. This makes it harder to intervene early in cases of treatment-resistant conditions or to prevent readmissions, which are key drivers of both cost and patient harm.

Over time, these inefficiencies erode the financial viability of inpatient psychiatric care. Hospitals and behavioral health units, especially those operating on thin margins, face pressure to cut beds or shut down altogether. This shrinking of the network only compounds access problems for patients and headaches for payers trying to maintain parity compliance.

A Better Way Forward

The good news is that this isn't uncharted territory. Other areas of medicine have faced similar challenges and found ways to overcome them. Oncology, for example, is historically a highly variable field and has benefited greatly from the development of staging protocols, molecular diagnostics, and treatment pathways that tie directly to insurance approval criteria. Orthopedics, once plagued by inconsistent documentation, now uses tools like the Oxford Hip Score or WOMAC index to evaluate treatment needs and outcomes. These frameworks didn't emerge overnight, but they've transformed how care is delivered and reimbursed.

Behavioral health can follow suit. By adopting standardized acuity measurement tools and tracking progress using evidence-based outcome scales, psychiatric facilities can provide payers with the clarity they need to authorize care more efficiently and predictably. This doesn't mean reducing complex human conditions to a single number, but rather creating operational language that clinicians and insurers share.

I've seen firsthand how applying structured measurement and documentation practices can dramatically reduce friction in behavioral health claims. Facilities that track acuity and outcomes consistently are not only more likely to secure authorization quickly, but also more likely to see improvements in patient engagement, length of stay, and readmission rates. Payers benefit, too, with lower administrative costs, fewer appeals, and better visibility into network performance.

Toward a More Sustainable System

Fixing the operational gap in behavioral health isn't just about reducing claim denials. It's about making the system sustainable for everyone involved. Standardized measurement can help preserve inpatient units, strengthen networks, and ensure patients receive care at the right intensity, in the right setting, at the right time.

We're at an inflection point. Behavioral health is finally being recognized as central to overall health. But unless we modernize the operational infrastructure that supports it, we risk repeating the mistakes of the past, underfunding care, alienating patients, and burning out providers.

It's time to bring behavioral health up to operational parity. Not just because it's the fair thing to do, but because it's the smart thing to do, for payers, providers, and the millions of people who depend on this care.

AI and generative AI are revolutionizing risk management, replacing standardized, one-size-fits-all models with personalized risk assessments and precisely tailored mitigation strategies. This transformation empowers businesses to make data-driven decisions, fostering a more predictive and proactive approach to risk. Insurance organizations that embrace AI-driven solutions in risk management will not only enhance their resilience but also gain a competitive edge in an increasingly dynamic market. 

This article explores key foundational strategies for risk mitigation, emphasizing the integration of AI and agentic AI to enhance workflow efficiency, mitigate hazards and enable proactive decision-making.

Aggregating Data and Seamless Integrations

To truly realize the power of "predict and prevent," insurers must first establish a strong data foundation. This involves bringing together enormous and diverse pools of data – everything from prior claims and customer data to real-time IoT sensor data and external environmental data – into a single coherent and shareable repository. 

Modern architectures like the data lakehouse bring the scalability and flexibility required to set up such an endeavor, while allowing structured and unstructured data to exist together. Flexibility to consolidate this data centrally and integrate it with sophisticated analytics tools and third-party data sources is also key. This can be achieved through partnerships with specialized data providers that offer pre-packaged risk intelligence or by building sophisticated homegrown integration solutions, whereby actionable insights can be extracted and communicated efficiently within the organization. 

The ability of AI to aggregate and interpret information from increasingly heterogeneous sources (text, images, video, sensor inputs, geospatial data, biometric data, etc.) will produce ever more inclusive and contextually aware risk estimates.

The Dual Approach to Future-Proof Risk Management

To mitigate risks by addressing hazards in real time, organizations must implement a well-structured central repository to store rules, manage versions, track changes, and facilitate reuse across applications or processes. Logical grouping—whether by process stage or product line—enhances navigability and ensures streamlined rule deployment. 

A centralized rules engine powered by agentic AI can enable carriers to dynamically adapt straight-through processing workflows, allowing for real-time risk mitigation and proactive decision-making. With access to central data, agentic analytics can be used for live streams of IoT sensor data, telematics, and customer behavior to detect risk patterns as they develop. For instance, by analyzing claims data anomalies in combination with external signals (such as weather and market trends), agentic platforms can alert insurers to high-risk conditions before they translate into large-scale losses.

While agentic AI analytics detect anomalies, simulate exposure, and trigger actions in real time, centralized rules ensure uniform decision-making across underwriting, claims, compliance, and catastrophe response. Together, they reduce fraud, improve regulatory adherence, and enhance operational resilience in an increasingly complex risk landscape.

Ethical AI in Risk Mitigation

The integration of AI comes with complex ethical considerations. Bias in data can result in unfair or discriminatory outcomes, creating significant reputational risks. Privacy and data security remain critical concerns, given the extensive use of personal information in AI-driven systems. The opaque nature of certain reasoning models, particularly their hidden layers, raises challenges around explainability and trust.

To ensure responsible adoption, organizations must establish clear accountability measures and conduct regular audits of AI systems. Concerns about AI fabricating responses further underscore the importance of verification methods, such as retrieval augmented generation (RAG), to ground outputs in factual data. 

Additionally, discussions around AI bias in underwriting highlight the necessity of objective criteria and human oversight to prevent discriminatory practices. By integrating actuarial oversight into the AI lifecycle, organizations can create a more transparent, accountable, and resilient risk mitigation strategy. Actuaries play a vital role in contextualizing model-generated predictions within real-world constraints, protecting against overfitting, bias, and unforeseeable consequences.

Looking Ahead

The shift to a "predict and prevent" model of risk management is a continuing approach, and both opportunity and challenge are compelling it forward. As catastrophic loss prediction using AI becomes increasingly sophisticated, the industry has the rare opportunity to put more effective preventative solutions in place, potentially reducing the impact of major events and creating a positive effect on the property market. 

In spite of risks like data bias and job displacement, AI's role as an intelligent assistant is indisputable—augmenting human knowledge instead of replacing it. The insurance sector is at a tipping point, having the technology and strategic intent to create a more robust tomorrow. 

Effective use of AI and GenAI for pre-loss risk prevention demands an integrated approach—one that weighs technological advancement with a resolve for responsible development, ethical application, and continuing learning. As these technologies develop, their impact in helping to protect assets, save lives, and create improved organizations and communities will only increase, heralding a new era where loss prevention is no longer a dream but a reality.

The insurance industry is built on trust, scale, and history. But legacy systems and decades-old infrastructure are slowing insurers as they navigate increasingly digital supplier relationships. External administrators, legal service providers, and managed IT vendors all depend on digital access, yet many insurers still rely on identity systems built for internal employees.

These systems were not designed for today's demands. As insurers lean more on third parties to deliver services, the inability to manage supplier access efficiently becomes a source of risk, delay, and noncompliance.

The Hidden Cost of Supplier Friction

Suppliers are critical to daily insurance operations, but their user experience is often overlooked. Onboarding can take days. Most insurers still rely on fragmented tools to manage supplier access. These include email requests, ticketing systems, and one-off provisioning scripts. The workflows are slow, inconsistent, and heavily reliant on institutional knowledge. As external relationships grow more complex, this patchwork leads to errors, delays, and blind spots in access visibility. Communication is fragmented. Manual provisioning introduces delays and errors. These bottlenecks do not just frustrate external teams. They delay policy servicing, claims handling, and tech rollouts.

Loose identity verification also opens the door to impersonation and fraud, especially when outdated processes rely on email requests and human approvals.

Inadequate Delegated Access

Insurance workflows often mean insurers must manage multiple external users or teams across various systems, be they claims adjusters, legal representatives, or IT support. If they cannot autonomously manage access rights, they are forced to rely on centralized IT intervention, creating bottlenecks and increasing the risk of human error.

Not unlike the challenge insurers face with delegated access for policyholders and their proxies, suppliers frequently operate under a hierarchy of users that need different levels of access. Without well-designed, role-based access controls, these relationships can introduce vulnerabilities and inefficiencies.

Security Vulnerabilities

The increase in third-party integrations has expanded insurers' attack surface. Poorly managed suppliers can become inadvertent conduits for cyberattacks. High-profile incidents, such as the Infosys McCamish Systems breach, highlight how external access points can be a stepping stone to compromising millions of sensitive records.

Bad actors are highly adept at exploiting fragmented identity and access management (IAM) systems, pivoting between digital portals and human-assisted channels like call centers. If a supplier's access is not continuously monitored and intelligently verified, attackers can escalate privileges or move laterally across systems unnoticed.

Regulatory Compliance Challenges

Insurance providers operate under growing regulatory mandates such as GDPR, CCPA, PIPEDA, and industry-specific compliance requirements. When suppliers interact with sensitive customer data, complexity around consent, data minimization, auditability, and breach reporting is inevitable.

When suppliers are not fully integrated into an IAM system, insurers battle to track which external users accessed what data and when, facing a lack of visibility that can endanger the business.

Operational Inefficiencies

Many insurers still rely on manual processes to create and remove supplier accounts. This increases the chance of human error and makes it harder to ensure that access is removed when a contract ends.

This mirrors a broader insurance industry challenge: outdated customer directories that aren't regularly audited or verified. Just as insurers must revisit and clean up dormant policyholder records, they must also manage the supplier identity lifecycle continuously.

How B2B IAM Addresses These Challenges

Modern B2B IAM solutions are designed to handle the scale, complexity, and operational nuance of insurance-related industries. Key capabilities include:

Federated Identity and Single Sign-On (SSO)

In the insurance sector, third-party agents, brokers, and service providers often need access to internal portals for claims processing, underwriting tools, or policy management systems. Federated identity enables these external users to authenticate using their own trusted identity providers, reducing the need for duplicated credentials and minimizing overprovisioning. Combined with single sign-on (SSO), federated identity ensures seamless and secure access while maintaining strict access controls aligned with compliance requirements.

Self-Service Onboarding and Automated Lifecycle Management

Modern B2B IAM solutions automate the entire supplier onboarding process. Self-service portals, identity proofing, and pre-configured workflows simplify access provisioning and apply consistent verification requirements to all users. Access is automatically revoked when contracts or relationships end, reducing human error and limiting risk.

Delegation

B2B IAM enables suppliers to manage their own users and access rights within strict, pre-defined boundaries through delegated user management. This model solves a key scalability problem: Insurers cannot realistically handle every external access request themselves. By allowing trusted third parties to manage their internal teams, insurers reduce operational overhead without giving up control. Governance and security policies still apply, and the process avoids the bottlenecks of central IT intervention.

Adaptive Authentication and Risk-Based Access

Advanced B2B IAM systems enforce strong, continuous authentication, including multi-factor authentication (MFA) and adaptive access based on behavioral analytics. real-time monitoring and detection of anomalies, like access from high-risk geographies or at odd times.

Fine-Grained Authorization

Most insurers rely on role-based access control (RBAC) as the foundation for managing access. It assigns permissions based on a user's function and is effective for internal teams. But in supplier ecosystems, roles alone are not enough.

As external relationships become more complex, attribute-based access control (ABAC) helps refine access using context like geography, business unit, or risk level. Even then, a key dimension remains missing: who the user represents.

Relationship-based access control (ReBAC) fills that gap. It evaluates the connection between the user and the insurer. A supplier working on behalf of Insurer A should only see data tied to that relationship, even if they have the same role and attributes as a supplier representing Insurer B.

RBAC, ABAC, and ReBAC are not competing models. Together, they provide the layered control insurers need to manage external access precisely, reduce exposure, and support growing third-party networks without added risk.

Audit Logging and Compliance Reporting

To meet regulatory standards, these solutions provide detailed audit trails, consent monitoring, and policy-enforced access controls. Every supplier activity is logged, verifiable, and auditable.

Managing Supplier Relationships Securely and Efficiently

Insurers will always depend on external partners to deliver digital services, so the challenges of supplier integration will become more complex and riskier. Whether the threat comes from dormant accounts, weak verification standards, or inefficient workflows, the consequences can be dire: data breaches, regulatory fines, and lost trust.

B2B IAM is becoming a critical capability in managing these supplier relationships securely and efficiently. It improves security and compliance while enhancing agility, UX, and operational alignment. In a digital insurance market, entities prioritizing flexible, risk-aware identity strategies will mitigate threats and set themselves apart as trusted, modern partners.