Paul Carroll

What are the key opportunities for operational efficiency in the insurance industry?

Olga Vinichuk

Automation opens up immense opportunities for insurance efficiency gains. But it’s not about throwing technology, well, everywhere. The biggest potential lies where inefficiency is curbing the insurer’s profits and hurting customer experience the most.

From what I’ve seen across ScienceSoft’s clients in insurance, three areas really stand out: claims processing, underwriting, and customer servicing. Claims is where delays are most visible to the customer and where inefficiencies quickly add up in payer profitability leakage. Prolonged and gapped claim fraud detection alone costs the sector more than $300 billion annually. Automating parts of the claims lifecycle, especially around document intake, evidence validation, and decision-making, has shown huge potential. That can start with rule-based automation but becomes really powerful when paired with artificial intelligence (AI).

Underwriting is another big one. The process is often slow and heavily dependent on human reviews and judgment calls. McKinsey estimates that around 40% of underwriter work time is locked in low-value manual routines like re-keying data and profiling risks. Software to handle lower-risk, rules-based underwriting tasks has been here for decades. With machine-learning-based analytics and generative AI, we now have the tools to automate data processing entirely and assist underwriters in risk scoring. This speeds things up and boosts risk profiling precision.

Customer servicing — everything from inquiry handling to reminders — is still manual at many insurers. This hinders both efficiency and customer experience: The level of customer satisfaction with digital servicing in insurance is the lowest among all finance domains. From my experience, automating those tasks cuts operating overhead, boosts customer satisfaction, and allows servicing teams to focus on complex relationship-building work, which is where they generate the biggest value.

Paul Carroll

As we go deeper into specifics, let’s start with underwriting.

Olga Vinichuk

Underwriting is the area where the impact of automation is especially tangible in terms of speed and capacity. Risk evaluation and quoting are the primary candidates for transformation as they drive the biggest efficiency gains. In some segments, software is now helping insurers underwrite up to 80% faster and handle 40% bigger volumes of tasks with the same headcount.

A few years ago, I worked as a consultant for an aviation insurance automation project where the client's underwriting process was completely manual. I was on-site at their office, and they were literally writing out quotes on paper, stamping documents, and walking them across the office. Their risk rating algorithms lived in Excel and required manual input and calculation every time. It was incredibly time-consuming. The worst thing is that the manual process led to inconsistent underwriter decisions and limited scalability.

We helped the carrier move that entire process into a digital environment. ScienceSoft’s engineers built a custom system where underwriters can now input data about the insured object, say a cargo or a fleet, and relevant parameters like usage type and coverage needs. The software calculates premiums using the same scoring logic, now running automatically. As some of the aviation lines assumed tailored arrangements and region-specific regulatory constraints, we left room for underwriters to check algorithmic outputs and apply human judgment.

The solution allowed the carrier to write more, produce quotes faster, and free underwriters to focus on complex risks and negotiations. I can’t disclose the impact for that particular project, but it’s more or less aligned with our benchmark findings: a twofold increase in underwriters’ productivity, quoting in minutes, and 10%+ operational cost savings.

Paul Carroll

How does AI fit in?

Olga Vinichuk

AI is the natural next layer of value once rule-based automation is in place. From my experience, the most impactful applications of AI in underwriting are automated risk data processing and intelligent decision-making.

Intelligent decision-making solutions can auto-process applicant data, profile risks, and produce personalized quotes in just a few seconds. For insurance lines that mainly deal with standard, low-risk cases, smart engines can underwrite over 90% of applications outright. This opens vast opportunities for freeing up underwriter capacity.

Fully automated risk profiling became possible with the rise of large language models (LLMs). LLM-powered tools can extract risk insights from heterogeneous documents and summarize them into writing-ready files within minutes, highlighting what’s relevant and flagging gaps. That’s especially helpful in commercial and specialty lines where submissions are bulky and unstructured. Early adopters of such tools report a 2x increase in underwriter productivity and 4x faster time to quote.

With LLMs, you get not just automated risk data processing but full-scale intelligent assistance. For instance, underwriters can ask an LLM copilot to evaluate the conditions of the insured object, map risks, and interpret their implications for coverage. The tool immediately provides relevant responses, freeing the team from tedious data search and analysis. Global risk technology leaders like Verisk already have LLMs in their stack, so you can leverage the capability with a familiar platform.

Paul Carroll

What are the major opportunities for efficiency you've identified in the claims process?

Olga Vinichuk

One major opportunity lies at the claim input and processing stage. Many insurers have already implemented self-service portals and mobile apps for insureds to submit claims and evidence digitally. ScienceSoft’s clients who did that achieved a nearly 50% reduction in employee workload and 5x faster claim processing. 

What’s driving the next wave of efficiency is the integration of intelligent technologies. Leveraging AI-fueled image analysis, natural language processing, and machine learning, you can automatically parse any sort of evidence, validate the data, classify severity, route the claim to the right handler, and in some cases, even settle claims outright without any human touch. From my experience, such intelligent automation helps establish a 5–10x faster claim cycle and boost adjuster productivity by around 30%.

Paul Carroll

I think there’s a lot of potential with fraud detection, if only because an AI can process a much higher volume of data than humans can.

Olga Vinichuk

Absolutely — the volume and complexity of insurance fraud make AI essential. We’re talking about billions lost annually. What really stuck with me was something I recently heard from an insurer during a project discussion: They said they were losing trust in their provider network and had to enforce stricter controls to avoid fraud leakage. That really brings home the operational impact of fraud, beyond just financial losses.

AI tools automate two critical tasks. First, they can analyze vast volumes of historical claims data to detect, say, unbundling, upcoding, or duplicate billing. These used to be handled by rule-based engines, but fraudsters adapt fast, so AI now plays a role in identifying anomalies and subtle correlations that would be hard for a human to spot in time.

The second is evidence fraud detection. For example, ScienceSoft recently helped an insurtech startup create a solution that automatically recognizes forged dental X-rays submitted by providers to justify inflated claims. We built computer vision and machine learning models to detect manipulated images. Our algorithms were trained to spot visual gaps that indicate alteration and mismatches between image content and patient data that hint at reuse. The models validate in minutes and flag nuances that seem plausible to human reviewers, all in a HIPAA-compliant manner.

Perhaps the best thing about AI-powered fraud detection is its scale. Insurers can check 100% of claims instead of just a sample without a heavy lift of manual review. This increases efficiency and dramatically reduces fraud-associated losses. And as fraud schemes increasingly become AI-supported, it’s only logical that AI becomes the tool we can’t avoid to fight back.

Paul Carroll

What opportunities for efficiency are you observing in customer service?

Olga Vinichuk

I see one of the most compelling efficiency opportunities in agentic automation. With today’s advanced large language models and cloud-based orchestration tools, insurers can deploy AI-powered agents that autonomously handle over 70% of routine service interactions, like policy lookups, claim status checks, and document submissions, without human intervention. Unlike the rule-based bots of the past, these agents deliver a smooth, conversational experience that rivals human service quality. You get your human agents freed for complex tasks and high-emotion claim cases where a real conversation is needed.

Speech-to-speech assistants are maturing, and they are especially helpful for older and less tech-savvy insureds who prefer to talk rather than use a screen. The R&D team at ScienceSoft has recently introduced an AI voice agent that can hold live calls with customers and automatically book appointments. Based on our estimates, the agent can speed the process by 40%, cut call abandonment rates by 30%, and lower operational costs by at least 50%. As it can handle multiple calls simultaneously, it can process 70% more calls per hour than a human service rep — a huge win from the efficiency standpoint.

Paul Carroll

Any final thoughts?

Olga Vinichuk

Whether rule-based or AI-driven, automation is a strategic lever for insurance efficiency, especially when deployed iteratively. Even with modest budgets, I’ve seen insurers achieve fast wins using basic tools for high-friction workflows like first notice of loss (FNOL) intake or quote generation. These early gains often justify broader investment in intelligent automation, like AI for risk scoring, fraud detection, or customer service. Once automation starts delivering measurable efficiency or improvements in insured experience, momentum builds. It’s not about a big-bang transformation — it’s about creating a scalable path from manual to intelligent operations.

Paul Carroll

Right. You can get a virtuous circle started. You spend a bit on automation, which frees up funds, which lets you invest more in automation, which… and so on.  

Thanks, Olga.

About Olga Vinichuk

Business Analyst and Insurance IT Consultant

Olga has built a vibrant career at ScienceSoft as a business analyst and insurance IT consultant. She participated in ScienceSoft’s 11 major insurance projects, guiding 8 of them as a leading business analyst. As an insurance IT consultant, Olga shapes the unique solutions that digitally transform underwriting, claim settlement, policy management, and compliance monitoring workflows. Olga is also involved in ScienceSoft’s outsourced product development projects, where she helps SaaS insurance companies turn their high-level product concepts to fully-functional solutions.

Artificial intelligence isn't settling for a foot in the door in 2025 – it wants to bust doors down for full commercial use, particularly with insurance companies.

A recent study by Digital Insurance shows 78% of insurance leaders saying they're expanding their technology budgets in 2025, and 36% of survey respondents said the bulk of their IT budgets are going to AI. That makes AI the highest IT priority among insurers, well ahead of top-tier issues like big data and analytics, cloud computing, and digital infrastructure.

"AI is now central to how insurers operate, affecting underwriting, claims, customer experience, and fraud detection," said Guy Gresham, a global capital markets board advisor and former director of investor relations at BNY.

Most insurers are in the speculative and development stage with AI, the DI study reports, but in specific insurance sectors like healthcare, "are already in full AI production."

So, what does the AI-led insurance innovation landscape look like in mid-2025? Here's an inside look.

How AI Is Changing the Insurance Landscape.

In mid-2025, the U.S. insurance sector is seeing AI fuel the rise of continuous underwriting, where pricing and risk exposure adjust dynamically rather than on an annual cycle.

"At the same time, intelligent agents are transforming service delivery by handling claims and customer interactions with increasing sophistication," Gresham said. "For insurers, this marks a shift from being product providers to becoming real-time risk managers. For investors, the value lies in firms that can embed AI across functions to drive efficiency, resilience, and scalable insights."

With those factors in play, major insurance companies have wasted no time in leveraging AI.

Allstate, for example, is helping agents prepare claim emails, while State Farm is deploying the technology to swiftly gather data on customers for improved outreach and is using natural language models to speed the contract analysis process.

Other insurance professionals say they're quickly getting comfortable with AI in the workplace.

"We're using AI to streamline underwriting, flag fraud faster and offer customer service that doesn't include canned elevator music," said John Espenschied, agency principal at Insurance Brokers Group, an independent agency.

Espenschied said predictive analytics is the "new" gut instinct. "AI can look at more variables than any underwriter, making quote accuracy better, pricing fairer (most of the time), and claim turnaround faster," he said. "Claims bots are getting so good they might start asking how your weekend was before denying coverage."

Is AI affecting policies and pricing?

AI is also helping insurers clarify risk and set up optimized price points.

"Live telemetry gives insurers a clearer view of actual risk, not theoretical posture," Espenschied noted. "That clarity lets good actors get better pricing. It also helps advisors push for coverage that aligns with what clients are actually doing, not just what they say on an application."

On the production side, insurers are using AI to develop new guidelines for operational risk assessment, which in turn affects pricing.

"Insurers today typically rely on broad data sets like historical loss data, demographic information, and general weather reports to assess risk," said Stan Smith, CEO and founder of Gradient AI, a fintech insurance company. "In the future, we will see AI models that incorporate more specialized data sources to improve the accuracy of risk assessments.

Cryptocurrencies and blockchain are being deployed.

Blockchain is no longer a peripheral experiment for insurers. Now, it's evolving into a core infrastructure technology with broad applications across the insurance value chain. From underwriting and claims to reinsurance and regulatory compliance, blockchain delivers transparency, automation, and data integrity on a large scale.

Take AXA, the France-based insurance and investment firm that's been challenged by a common roadblock in the insurance sector: lengthy and delay-prone claims processes related to airline flight delays. In response, AXA created Fizzy, a blockchain-based parametric insurance that delivers automated payouts to travelers on flights delayed by two hours, with no need for the traveler to file a claim.

"Blockchain in insurance has promise," Espenschied said. "Smart contracts are the big draw, especially automatic payouts when conditions are met," like weather parametrics.

There's also a growing industry concern that over the next 15 years, insurance may shift in part to a tokenized system where risk pools are moved and manipulated.

"That's where insurance coverage is tied to how many tokens within that pool have been staked," said Ted Patestos, CEO and founder of Tiger Adjusters. "Nexus Mutual is one example of a company playing in the DeFi insurance space."

Experts also note that alternative finance technologies have ample room for expansion and market share growth. "Blockchain for the first time has joined forces with the reinsurance sector to bring sufficient liquidity to the asset class, and a whole new avenue for alternative capital to enter the industry," said Ted Georgas, co-founder and chief technology officer at OnRe, a blockchain insurance company.

Backers argue that the tokenization approach democratizes access to insurance investments, accelerates capital inflows, and enhances product liquidity, particularly in catastrophe risk markets.

Fintech companies, such as Chainlink Labs, which developd the Chainlink network, a decentralized oracle network connecting blockchains to real-world data and off-chain systems, are collaborating with insurers to integrate real-world data feeds into decentralized insurance products, particularly in emerging markets.

"Regulatory risk is the biggest roadblock for insurance shifting to blockchain-based automated solutions, followed closely by the 'what if this token is worth eight cents tomorrow?' factor," Espenschied said.

What will AI-powered insurance look like in 10 years?

Insurance industry specialists anticipate significant technological shifts that will transform the insurance sector over the next decade.

"Risk will be priced in real time," Knuth said. "Protection will be embedded, not bolted on. And advisors will act more like financial strategists, guiding clients through risk exposure with dashboards, not paper apps."

More automation, however, may lead to more industry upheaval.

"We'll see deeper exclusions in policies, less human interaction, longer wait times to see true resolution or indemnification on claims," Patestos said. "We'll also see more 'Band-Aid models' from smaller insurance companies attempting to capitalize on the vacuum in the market from larger carriers with a lower risk appetite pulling out of certain markets."

Mergers and acquisitions (M&A) are occurring at a breakneck pace, with firms rushing to expand their footprint, unlock synergies, and gain a competitive edge. But success in M&A isn't measured by the number of deals closed or the splash they make — it's measured by what happens next.

The real work begins after the ink is dry. That's when teams face the hard part: integrating systems, aligning workflows, unifying cultures, and preserving the client experience. Too often, deals that look great on paper unravel in practice. Studies show that between 70% and 90% of M&A transactions fail to deliver the intended results, often due to poor post-merger integration.

Without a strong post-merger strategy, even the most promising acquisitions can lead to fragmentation, friction, and lost momentum. The deal isn't the win, it's just the starting line.

It's Not the Deal, It's the Alignment

Every acquirer comes with a playbook, and once the deal is signed, they'll expect new agencies to fall in line. That's not inherently a bad thing. But if you're not aligned on platform strategy, technology priorities, or cultural values, what seemed like a great opportunity can quickly turn into friction.

Misalignment on integration plans is one of the top reasons deals underperform or fail altogether. What begins as a growth story can end in stalled momentum, lost clients, and teams that feel more burdened than empowered.

Why Integration Stalls

Many agencies underestimate how hard integration really is. Even with the best intentions, focus can drift, priorities conflict, and progress stalls. The most successful integrations start with one thing: a clear, top-down definition of success. From there, leaders must set an adaptable path because mergers are rarely linear. Agility is key, but so is fortitude. You must finish what you start.

This is especially true when it comes to systems. Technology integration is one of the top two reasons M&A fails, followed by cultural misalignment. Yet most agencies still treat technology as an afterthought rather than a foundational pillar.

The Secret to Seamless Integration

Insurance operations are already complex, layered with regulatory nuance, product variability, and service obligations that differ from state to state. When two or more agencies merge, that complexity doesn't just double, it compounds. Disparate workflows, competing tech stacks, and varying definitions of "how things get done" can create a chaotic operating environment that bleeds time, trust, and profitability.

That's where standardization becomes a silent force multiplier. It isn't just about documentation or process control, it's about creating clarity across every layer of the business. Standardized processes reduce risk, drive consistency, and enable more confident decision-making at every level of the organization. When everyone is rowing in the same direction with the same tools, agencies can scale, not with chaos, but with confidence.

Tech as a Unifier, Not a Barrier

Before signing on the dotted line, agencies need to do more than analyze financials, they need to scrutinize their technology infrastructure. What systems are in place? Where do they overlap? What will be sunset, and what will remain mission-critical? Without clarity on these questions, integration efforts can spiral into costly delays, fragmented data, and staff confusion. It's like building a house on two different foundations — eventually, the cracks will show.

Technology should be viewed not as a post-merger clean-up task, but as a strategic enabler from day one. The most successful agency combinations prioritize tech alignment before the merger, ensuring shared systems can support everything from CRM and commissions to compliance and performance data. When technology is unified, it accelerates operational consistency, improves speed to value, and strengthens the client experience.

Trust Is the True Currency

In times of transition, transparency and delivery are everything. While leadership teams are focused on internal alignment, systems integration, and operational change, clients are watching for one thing, consistency. They don't care about your new logo, your press release, or how many agencies were acquired. They care that their questions get answered, their policies stay intact, and their experience doesn't suffer.

That's why transparency and follow-through are vital during a merger. Clear communication goes a long way in preserving loyalty. But communication alone isn't enough. What cements trust is execution. When you say you'll deliver (and you really do), your credibility strengthens at every touchpoint.

Positioning for What's Next

Consolidation isn't slowing down, and neither is innovation. New entrants will continue reshaping the landscape, bringing fresh solutions to long-standing inefficiencies.

The future belongs to agencies that align early, standardize boldly, invest in the right technology, and never lose sight of the trust that keeps clients coming back.

Lemonade, a digital-first insurance company known for selling renters, pet, and term life coverage, reimagined the entire insurance experience as a productized user experience (UX) flow. Want renters insurance? 90 seconds. Need to file a claim? Done in three. No agent. No paperwork. No "please hold."

Where legacy brands treat digital as a convenience layer, Lemonade treats it as the core product. From chatbot onboarding (hello, Maya) to claims resolution (thanks, Jim), every step is built for clarity. Speed and simplicity here are differentiators. Lemonade claims its sign-up takes just 90 seconds, with claims processed in as little as three minutes. And when 87% of your customers are buying insurance for the first time, that's growth through innovation.

The Psychographics That Matter Now

Lemonade is creating demand, not just siphoning customers from State Farm. Their core audience is first-time buyers who are:

• Startup-minded
• Values-driven
• Subscribed to TED and Coinbase
• Fluent in fintech, allergic to friction

RightMetric's research found three dominant audience clusters: Future Finance Heads, Modern Money Minds, and Start-up Realists. These are people who treat insurance as a smart tool and expect it to behave like one. This group lives in NYC, SF, and Reddit. They don't want brands that "understand Gen Z." They want brands that think like them.

Why Absurdity Outperforms Authority

Lemonade's tone is part pink soda, part TED Talk, and part meme. One of their blog posts is literally titled 'We Suck, Sometimes.' Their TikToks feature child CEOs answering red desk phones. Their ad copy says things like "insurance for your skincare shelf."

The approach shouldn't work. But it does because the absurdity is for laughs and for strategy. Lemonade builds emotional resonance through tone that disarms. Humor is the differentiator in a category drenched in fear and formalwear.

The TikTok Gap: What They're Underinvesting In

How Lemonade Stacks Up: Side-by-Side Estimates with State Farm

Paid media estimates reflect tracked digital ad placements across major platforms from May 2024 to April 2025. Figures are based on third-party intelligence and may not capture all direct or offline spending. Raw video view data for YouTube, Facebook, and TikTok is sourced from Tubular Labs. Instagram video views and analysis are based on proprietary analysis using third-party ad intelligence tools.

While State Farm enjoys broader scale, Lemonade maximizes creative resonance per dollar, driving nearly 800 million impressions through highly targeted, tone-driven campaigns. The opportunity is in closing the platform gap, especially on TikTok and with influencers.

For all of Lemonade's cultural fluency, there's one platform where the disruptor isn't...disrupting: TikTok. Despite having the tone, audience, and aesthetic for virality, Lemonade's presence here is small. Really small.

Compared with legacy brands like State Farm, which generated over 2.7 billion impressions on just one campaign, Lemonade's presence is modest, despite having top-performing campaigns that drove nearly 800 million impressions on just $11 million in spending. Their TikTok presence and influencer engagement remain limited. That's an opportunity.

For brands competing in the same space: TikTok is wide open. Influencer partnerships, testimonials, and remixable user-generated content (UGC) are all growth levers just waiting to be pulled.

What to Steal From the Legacy Brand Playbook and What to Stop 

Here's what legacy brands can and should steal from Lemonade:

• Design for speed: If your quote flow takes longer than a Chipotle order, it's too long.
• Lead with values: Lemonade's Giveback program is nice and sticky.
• Talk like a human: Enough with "trusted coverage for your family's future." Try something like Lemonade's approach: "insurance for your skincare shelf" or "renters insurance for your gaming rig."

And what to stop?

• Stop mistaking legacy for loyalty.
• Stop hiding behind legalese.
• Stop thinking people will care just because it's important.

In an industry built on risk, Lemonade proved the real risk is sounding like everyone else.

Footnote: All campaign data referenced, including spend, impressions, and CPM, was sourced from RightMetric's proprietary teardown of Lemonade's digital marketing strategy. Data was collected between April 2024 and May 2025 using tools including Tubular Labs, AdClarity, and additional third-party analytics platforms. Figures are modeled and directional

In 2025, artificial intelligence (AI) is not just reshaping consumer interactions—it's fundamentally reinventing how insurers assess, predict, and manage risks. With AI risk detection tools becoming smarter and faster, the once-static process of underwriting and risk profiling has evolved into a real-time, dynamic system.

In this article, we'll explore how AI detection is changing risk assessments, the technologies driving the change, and what it means for insurers, policyholders, and the entire financial ecosystem.

From Traditional Methods to AI-Driven Solutions

For decades, risk assessments in insurance relied heavily on historical data, manual reviews, and human judgment. While effective to an extent, this approach was time-consuming, prone to error, and often generalized, ignoring individual nuances. This model is rapidly becoming obsolete.

In its place, AI-driven risk detection offers a data-driven, real-time, and hyper-personalized alternative that promises better accuracy, lower costs, and enhanced customer trust. AI detection leverages a combination of machine learning algorithms, natural language processing (NLP), and big data analytics to assess risk more accurately than ever before. Here's how it functions in modern insurance:

• Data Ingestion: AI systems pull structured and unstructured data from various sources such as sensors, social media, medical reports, and transaction records.
• Pattern Recognition: Algorithms analyze this data to identify hidden patterns, anomalies, and correlations.
• Risk Scoring: AI tools generate real-time risk scores, offering detailed insights that can trigger automated policy decisions or human review.
• Continuous Learning: AI systems learn over time, continuously refining their accuracy and recommendations.

Key Technologies Transforming Risk Assessment

Predictive analytics engines use historical claims and behavioral data to forecast future risks and loss probabilities with uncanny precision. Computer vision in claims and underwriting allows AI models to analyze photos and videos to assess property or vehicle conditions instantly, improving claim verification and reducing fraud.

Natural language processing (NLP) tools can parse through massive volumes of textual data, including claims descriptions, call transcripts, and social media posts, to detect fraud indicators or emotional sentiment. Meanwhile, telematics and IoT integration enables AI to interpret data from smart devices like fitness trackers, vehicle sensors, or home monitors to assess real-time behavior, health, or environmental risks.

Consider Lemonade Insurance, an AI-driven insurtech firm that processes claims through its AI bot, Jim. In 2025, it's reported that over 40% of Lemonade's claims are resolved within seconds. The system uses behavioral analysis and NLP to detect potential fraud and approve low-risk claims immediately—showcasing the power of AI in real-world scenarios.

Insurance Segments Embracing AI Risk Detection

• Health Insurance: Wearables and biometric data help AI models predict potential health issues, enabling personalized premiums and preventive care models.
• Auto Insurance: AI detects risky driving behavior through telematics data and adjusts premiums or coverage accordingly.
• Home Insurance: Smart home systems integrated with AI can identify potential fire, flood, or intrusion risks, allowing real-time risk scoring and alerts.
• Commercial Insurance: AI assesses operational data from factories, supply chains, and employee behavior to detect safety risks or cyber vulnerabilities.

Benefits and Challenges of AI Risk Detection

The advantages are compelling. AI minimizes human bias and error, leading to more precise risk profiling and increased accuracy. Automated systems can analyze thousands of data points in seconds, enabling instant policy decisions and faster underwriting. Risk scores update in real time, allowing insurers to adjust pricing based on current behavior, not outdated statistics, through dynamic premiums. AI also detects inconsistencies, anomalies, or repetitive claim patterns that may suggest fraud, often before payouts are made. Policyholders appreciate personalized pricing and faster service, which leads to higher satisfaction and retention and improved customer trust.

However, significant challenges remain. With AI pulling data from personal sources, insurers must navigate tight regulations and ethical boundaries around data privacy and security. If not properly trained, AI models may replicate societal biases, unfairly affecting certain groups through algorithmic bias. Customers and regulators want clear explanations of AI decisions, and "black box" algorithms pose a risk to accountability regarding transparency and explainability. Many insurers still rely on outdated software, making seamless AI integration with legacy systems difficult.

What the Future Holds

As more insurers embrace AI detection tools, expect a dramatic shift in the industry toward fully autonomous underwriting, blockchain-backed AI data sharing, and global regulatory frameworks for AI governance. In essence, the convergence of AI and insurance will drive a new era of proactive risk management rather than reactive claims processing.

AI risk detection is not just a tech trend—it's a business imperative. Insurers that leverage these tools will gain unmatched agility, accuracy, and trust from customers. As algorithms grow smarter and more intuitive, the line between technology and traditional underwriting will blur, setting new benchmarks in how insurance operates.

AI isn't replacing humans—it's enhancing human judgment with machine precision, redefining what it means to assess risk in a modern world. 

Mick Moloney is a Partner at Oliver Wyman, based in New York, and is the Global Head of the Insurance & Asset Management. Mick spends his time working with leading insurers, asset managers, and advisory firms on a range of strategic and execution topics with a particular focus on growth, innovation, and efficiency in retail and institutional life and retirement markets. Mick has over 25 years of experience in financial services. Prior to joining Oliver Wyman, Mick spent 15 years at Mercer where he was a Senior Partner and global head of the firm’s Financial Strategy Group (FSG) – the team that leads on risk advisory work for large clients. In addition, he served as a member of Mercer Investment Consulting’s Global, US and European Operating Committees and the European Operating Committee for the firm’s Retirement, Risk and Finance business. He is a Fellow of the Institute of Actuaries in the United Kingdom, a Fellow of the Society of Actuaries in Ireland, an Associate of the United States Society of Actuaries and has an Executive MBA from the Smurfit Business School, University College Dublin.

Paul Carroll

You've been on a thesis for a while, and I’ve wanted to explore it with you for a long time. That's this idea of private equity getting into life insurance as a way to grab assets and then invest them. Would you lay that out for me?

Mick Moloney

Sure. As you probably know, Paul, it's not a new phenomenon, if you trace the history of Apollo Athene, which is kind of the poster child of what we're about to talk about. I mean, the Athene piece of that is probably from the late 2000s in terms of when that initial transaction happened. I may have that off by a couple years or so. [Apollo established Athene, a provider of annuities and retirement services, in 2009, then, in 2022, acquired the 65% of the equity that it didn’t already own.]

We're talking something that's been playing out for over a decade. The critical piece is that that trend has been accelerating materially.

It took a little while for the dynamic to kind of come together. What has been happening over the whole period of time, just to kind of set the broader scene, is that, as you know, as interest rates came down, life insurers, in particular, got constrained for capital. And the public insurers, in particular, either began to or had made cash flow commitments to shareholders about returning capital.

So what you had was a dynamic where, in a number of cases, capital became constrained in various ways for the public life insurers. And at the same time, private capital began to look at a life insurance balance sheet as a means of raising permanent or semi-permanent capital, which as you know, for a private capital player is a very attractive thing in that you know you're going to get a certain amount of flow. You have insight as to when that capital is going to arise….

Marry that with a machine that originated structured [credit] and managed private credit, in particular. As you know, the vast majority of life insurance balance sheets consist of credit in some form – private credit, in particular, as the thesis has played out, by providing loans to more underserved parts of the landscape, where the risk-adjusted spread on those loans was greater. If you could pass some but not all of that risk-adjusted spread through to policyholders when you priced insurance liabilities, there was effectively what became a win-win there. You were more competitive, and the policyholder ended up doing somewhat better. You had assets that were very well suited.

Then, two things happened. The growth in the private capital-held life balance sheets, in the U.S., in particular, far outstripped the growth at some of the more traditional carriers, and that balance sheet component allowed the private capital business to accelerate.

The numbers really speak for themselves. Look back over the last 10 years and put a composite together for the private capital players: Apollo, Blackstone, KKR, Carlyle, Ares. Their market caps over the last 12 years have increased by, let's say,13 times. It's been huge growth. At the same time, if you construct a composite for life insurers, they're up about 1.2 times over the same period of time. That's not saying that they haven't returned capital to shareholders, but what they have kept in and the value of it, which if you think of it as a measure of relevance, you're saying that these private capital players have increased 13 times at a time when the public life carriers have increased just a little bit over one.

At the same time, the traditional asset managers have not had a great time, either. If you take the traditional asset manager set and look at what's happened to them, that is up about 1.3 times. What's been happening there is that there has been a big shift to passive investment. There's been a lot of fee pressure. Sure, assets have gone up, but the profitability of the model hasn't gone up. And investors are saying, Look, I think you're going to lose share to private capital going forward in the more long-only, traditional space, and you're going to come under increasing pressure as that occurs.

So the private capital guys have increased earnings and have gotten re-rated for future earnings, which is the other piece. The story that all the private capital players are telling moving forward is very consistent. It is that there's a move going on from public to private capital overall.

There's a big demand for debt funding for things like infrastructure-building and digital economies being built. There is also global aging going on, which means that as people shift into retirement, their risk appetite goes down. They move money from equities into bonds. So there are these big structural tailwinds that they're pointing to in terms of their ability to continue to grow going forward.

And if you look at it, one of the tests is that the private capital firms are trading at forward price earnings multiples in the mid-20s. The traditional life carriers are doing pretty well if they're seeing nine or 10 times multiples. What it's telling you is that investors like the private capital story, and that private capital story, in a good number of cases, also has a significant leg to it, which is the life insurance balance sheet.

And anywhere life insurers are writing spread business, they're facing these firms. A lot of incumbents currently say, Do we think we can replicate elements of that model? And if we do, how do we want to do it? And how do we want to go about it?

The key is the acceleration, at least in my view, in the last three or four years. We’ve seen the model really come together because of the share of the kind of spread business that the new private capital-backed players have been successful in getting.

Paul Carroll

In some ways, as I've read what you've been writing about this, I've thought, what took so long? I mean, Warren Buffett showed the value of a Berkshire Hathaway-sort of model, where you grab some assets and you use them to invest, and if you're a smarter investor…. I assume a lot of the reason for the delay is comfort level. Life insurers have to be able to return money to somebody 50 years down the road. And I assume some regulators were uncomfortable with the idea of turning the money over to these sorts of cowboys who were starting hedge funds in the ‘90s.

Mick Moloney

There’s a little bit of that, I would say. I can point to a few things playing out. And the parallels to the P&C side of things are interesting. You are right about Buffett, and you could look at some others there and say that it's about a model in which I'm getting these assets and I can kind of invest them.

That model, though, particularly on the P&C side, is generally a little bit different in that they're generally shorter-dated liabilities. On the property and casualty side of things, because of volatility and various other things, I don't usually spend quite as much time worrying about precision and asset liability matching as I do on the life side of things.

On the life side, what regulators are always concerned about is whether the capital buffer is sufficient against the liabilities. The capital buffer itself is a function of the degree of match between assets and liabilities and how much capital I need to hold for a mismatch. And there is a high liability number and asset number that is a little bit higher. So you do need a lot of precision in the capital buffer to make sure that you're not going to lose if something happens.

But coming back to your question as to why has it only happened over the last decade: Why isn't it a 30-year-old phenomenon? I would say it is somewhat opportunistic. The other phenomenon that's been happening behind the scenes is that the volume of credit demand being served by banks has been decreasing systematically. It's been decreasing for a long time, but particularly in the post-financial crisis, as more regulation was put in and the kind of risk capital framework for banks shifted in a certain direction, you can see that that trend continued.

So what you've had is a shift of lending that moved from bank balance sheets to non-bank balance sheets. It's that set of non-bank balance sheets that are the players that we're talking about. And you can say at some point in the evolution of that, those players recognize that given that the volume of lending is increasing, one of the obvious places to raise funding for some of that lending is in these longer-term vehicles. And Apollo Athene, not to say they were the only ones that thought of that, but they really put a lot of muscle behind it. I mean, they bought Athene, then they floated Athene, then they kind of bought Athene back.

I forget the exact date, but it was maybe late 2000s when they did that initial transaction. It's not that there weren't things happening before that, but I think that one, and the degree to which they went progressively further and further into having that model, was pretty catalytic.

Paul Carroll

Trees don't grow to the sky, but it seems like there's a lot of room left for this trend to continue, right? I mean, some years’ worth anyway.

Mick Moloney

Yeah. I think there's a lot of room left for it to play out, frankly, and in different dimensions. There is the private credit story itself playing out. I would say there is tremendous upside in that for all the reasons that that I’ve talked about: the move from public to private credit, the global retirement wave, the shift of funding from banks to non-bank balance sheets, the infrastructure, the demand for debt. The secular tailwinds to private credit, I think, are unarguable. I would also agree with you that the piece of this that is the private credit intersecting with insurance balance sheets still has a long, long way to play out.

What we're seeing now is a lot of the incumbents mobilizing around shifting their model. You've heard me talk about it before. I call it the asset management-led insurer rather than an insurer that happens to have a general account and that happens to have an asset management capability. It's kind of turning the model on its head. And with that shift I see a lot of the large incumbents looking to mobilize properly around this trend. It will be their opportunity to generate growth going forward and to get a re-rating in their own growth prospects.

I mean, if you're a carrier sitting on eight times forward PE and looking at the private credit shops trading at 25-plus times, you know there are very few ways to get that kind of growth re-rating outside of that avenue, I would say. And you can see them beginning to do it. They're saying, Look, we're going to put more effort into our third-party asset management business. We're going to make it more visible to the outside world. We're going to change

the leadership. We're going to signal to investors that we're going to want to do more kinds of transactions as we build out capabilities.

The trick for the incumbents, I think, is that right now, in terms of their currency for buying things that are trading, very few of the incumbents have all of the capabilities that they need sitting inside their asset management business. One of the ways that they're going to solve for that is to buy boutique private credit managers or capabilities or platforms. Right now, it's very hard for them to justify that when they're trading eight times and they're buying something that's trading north of 20. It looks dilutive to shareholders unless you have a very careful rationale around it.

So I think the carriers will try and take a little bit from the playbook of the private capital carriers and say, we need to mobilize around this. We need to put points on the board in terms of showing people that we can do it. That will get us a re-rating. And as we get a re-rating, we can kind of accelerate into where we want to go. So I think it's going to be a super interesting five-plus years for the industry.

You can see it in the themes coming out in investor day presentations. A good example is Sun Life, who had an investor day presentation where they were majoring on asset management, insurance, and, in their case, kind of health and various other things. But the degree to which they were talking about their asset management business, I thought, was very notable. I think we'll see that as we see other investor days playing out.

Paul Carroll

Do you think the incumbents can change fast enough, or are private equity guys going to come in and buy a lot of the insurance companies?

Mick Moloney

That is the question, I think. I mean, as you know, the private equity guys have already bought a lot. And, they have platforms now and are buying more by way of liabilities and scaling up. The question of whether the incumbents will move fast enough, I think, is the big unknown.

I have to say that we're in a lot of these conversations with a lot of parts of the landscape. I'm much more optimistic now than I would have been if you'd asked me that question two or three years ago. In my mind, the discussion has shifted a little bit.

If you were talking to some of the incumbents, let's say three years ago, the conversation then was, I don't like the fact that these guys are disrupting my business. I'm not sure I agree with the model. I would very much like it if the regulators would change the rules such that what I think these guys are doing isn't going to be as possible going forward, etc.

I actually think that has changed a bit now. The mindset shift that I've seen is that executive teams have really come around to saying, Look, maybe regulation needs to change at the margin, but we are in the private credit business, and this is the means by which we can chart a course to a better future for the organization.

Now it's still a question in my mind as to whether the mobilization happens fast enough. But the other thing happening at the same time is that the firms that have been successful at it have grown massively and are themselves larger organizations. And as you know, you don't find very many supertankers that move at speedboat pace.

So you have this thing going on where that newer component of the industry is kind of maturing and scaling up. And it's having to look into more complicated places for growth going forward.

I think it's going to be a big feature of the industry, for quite a while to come. And I think it is one of the most interesting things playing out globally in the insurance landscape.

Paul Carroll

I do think that is the big question. I sometimes tell people I've been watching the same movie for more than 35 years because that’s how long I've been covering technology. And I had conversations like this with a guy who has become a friend, who was the senior VP of strategy at GM in the late 1990s and early 2000s. He actually is the guy who gave the world OnStar, so good for Vince. But he would talk about how GM was changing, and I would say, You're not changing fast enough. He'd say, We're changing as fast as we can, and I’d say, The market doesn't care how fast you can change. It's going to dictate what happens – and they had their problems not long after those conversations.

Mick Moloney

I agree. I've had that conversation with one executive, in particular, that I'm thinking of right now about how I think that the incumbents have executive teams, or at least some of the executive teams, that want to move very quickly. But as you know, you've got an elastic band that you can kind of pull so much in terms of getting the momentum to kind of shift in the direction.

So I think change is imperative, and the need is clear. The question is about the ability to move the ship in a different direction.

Paul Carroll

We'll see. It'll be interesting to watch.

Amid all the stories about the death last week of Hulk Hogan, one point is overlooked: the impact he had in getting companies to break out of the straight-laced marketing of the 1980s and see the potential for offbeat characters, even including antiheroes. 

For insurers, I don't think it's a stretch to say Hogan was the father of Mayhem and perhaps the godfather of oddball advertising icons such as Flo, the gecko and the emu. 

I say this both as an observer of insurance and as something of a student of pro wrestling. For a front page article in the Wall Street Journal, I attended a school for pro wrestlers in 1989 and wrestled a match on cable-TV, and the experience left a mark on my curiosity, as well as on my vertebrae and ribs. 

But Hogan didn't just show the value of creative branding; he also showed how important it is to always protect the brand and how quickly you can lose your following. 

Here is what a State Farm commercial looked like in 1988, after Hulkmania had begun but before it spread broadly beyond the world of pro wrestling. The ad was earnest and straightforward, seemingly based on real people. 

I won't even bother to provide an example from today, because all you have to do is turn on your TV and you'll be pelted with all sorts of characters who couldn't exist in the real world, including Mayhem, who might as well be a clone of the early incarnation of Hulk Hogan — evil but powerful and someone you kind of like despite yourself. 

Yes, of course there were other influences on the evolution of advertising, but I think Hogan was especially powerful because he blurred the traditional lines in wrestling and became wildly famous for doing so. Historically, there have been heels and faces (the good guys) in wrestling, but the categories were fairly static. Yet Hogan began his career as a heel, with his long, platinum hair and horseshoe mustache, before becoming a face and then sometimes switching personas over his long career. He opened the way for a much broader array of faces — and marketers in insurance, as elsewhere, took notice. 

Hence: Mayhem, Flo, the gecko, the emu and so on. 

(I learned the power of the face/heel distinction in my one match, in a college gym outside Philadelphia. I never for a second even thought about whether I'd be seen as a good guy. I mean, I'm a nice guy. Ask anybody. But my opponent, Tricky Nikki, who had often performed there, was a face, and that made me the heel. As the match progressed, I thought I won some respect and heard the crowd start chanting, "Wall Street, Wall Street...." But when I got a copy of the broadcast a couple of weeks later, I realized that the 800 or so people there were actually chanting, "Bull-s**t, bull-sh**t...." Oops.)

The cautionary part of Hogan's tale is that, once you've established a popular brand, you have to maintain it constantly. The New York Times obituary quotes Hogan, whose real name was Terry Bollea, as saying, “The moment I come home, the headband comes off the bald head, and it’s just Terry: dad, father, husband, friend.... The problem is, the moment I leave the house, the moment I walk out the front door, the world doesn’t want Terry. The mailman goes, ‘Hey, Hulk.’"

Hogan damaged his brand badly when a tape surfaced of him having sex with a friend's wife. He won an invasion of privacy suit against Gawker and put it out of business for sharing part of the tape — though controversially, as billionaire Peter Thiel was revealed as financing the suit. But a tape surfaced of Hogan calling himself "a racist, to a point," and using the N-word during the sexual encounter.

For insurers, I'd say the lesson is that if you're Lemonade and brag about having once settled a claim in three seconds, you'd better be fast on every claim. If you're State Farm and have established a quirky, friendly personality with your ads, you'd better be careful about asking for huge rate increases on homeowners insurance after wildfires in California. 

Consumers don't forgive, and they don't forget, at least not for years. 

Hogan was eventually accepted back into the pro wrestling world, which can be quite forgiving for its heroes, but later alienated part of his fan base by appearing at the Republican national convention last summer. Whether you think that made him a face or a heel depends on your politics, but he certainly adopted a heel's affect by tearing off his suitcoat and shirt to reveal a Trump/Vance T-shirt. Being a heel had certainly worked for him before. 

Hogan was so popular when I did my wrestling story that I wanted my nom de guerre to be a play on his. Given where I lived at the time, I wanted to be Hulk Hoboken (at 5'9" and 160 pounds), even though the guy running the wrestling school listed me as the Wall Street Warrior. 

Hogan's death feels like the end of an era. But his impact — and, I hope, the lessons from his life about the power of creativity and the need for vigilance about maintaining a brand — will live on.

Cheers,

Paul 

P.S. While I've singled out Alan Demers's piece about Hogan and the insurance ecosystem as one of the six articles to focus on this week, I'd be remiss if I didn't mention it here. It's not only very smart, but it's what got me reflecting on the wrestler's influence on insurance. 

Prevention in Modern Underwriting: How to Shift from Reactive to Proactive Risk Management

Traditional underwriting still relies heavily on historical claims data. As a result, risk management can only be reactive, acting “after the fact”. As we face an industry where margins are tightening and climate-related risks are growing, this reactivity is no longer enough. Proactive risk management, which heads off these losses before they occur, rather than responding after the fact, is critical.

Below, we look at how prevention technology is transforming underwriting and how using a service like bolt’s Prevention Technology helps reduce loss frequency before it happens.

Traditional Reactive Models: Challenges We Need to Escape

In the insurance industry, traditional underwriting models rely mostly on historical claims data to predict future risk. While this retroactive approach served us well for many years, it no longer addresses some critical modern realities. Underwriting innovation, especially in prevention technology, is essential to reinvent how we think about doing business in insurance.

The reality is that many losses are preventable – if consumers are using the right technology to alert of potential risk. Data-driven underwriting and predictive insights in insurance open up many new possibilities.

Consider these sobering statistics:

• Water damage comprised close to 24% of all US homeowners’ insurance claims, with 32% noting they’ve experienced an “adverse weather event” over the last five years.
• On average, these damage claims exceed $12,500 per event, costing insurance companies more than $13 billion annually.
• Water/freezing damage is the second most frequent claim after wind and hail damage. 

Then there’s the real kicker: weather-related catastrophes where damages cross the billion-dollar mark have risen from 3.3 events per year to more than 17 annually since the 1980s.

There’s a pressing need for underwriting innovation, as this escalation exposes the weaknesses in traditional insurance risk selection processes. The missing key? Real-time visibility as risk conditions evolve. If underwriters are confined to assessing risk at only a single point in time – such as when policies are taken out or renewed – they have no chance to influence risk outcomes over the policy lifecycle.

New Proactive Risk Management: The Solution to Traditional Underwriting Weaknesses

Proactive risk management is a key facet in modern underwriting innovation. And it’s being driven by new technology offerings. Traditional underwriting and its dependence on historical claims data fails to address preventable losses, driving up claim severity and frequency.

However, by integrating real-time monitoring, IoT sensors, and better use of predictive data, underwriters can help prevent claims – before they occur. This proactive risk management helps to return profitability to underwriting, reducing claims costs and allowing more accuracy in pricing strategies.

IoT Sensors in Underwriting

Until now, we’ve only had historical data to rely on. With the growth of the Internet of Things, or IoT, underwriters can now benefit from continuous and real-time information about insured assets or properties.

Smart sensors, for example, detect early warning signs of potential damage, such as water leaks or frozen pipes. They can even detect subtle issues, like electrical anomalies and structural weaknesses. This helps facilitate intervention before minor issues become major claims.

There’s significant interest in using IoT sensors in underwriting, alongside other AI-powered solutions. Even in 2019, 44% of the Top 100 insurers had already introduced this kind of intelligence into their operations, with 39% piloting it actively. Large residential property insurers indicated a massive 70% were interested in these solutions. However, while 42.8% of those using machine learning have already implemented it for auto insurance, there are only 8.6% who can say the same in home insurance. 

However, real-time sensor data can improve damage assessment accuracy by up to 40%, reduce fraud by roughly 35%, and cut claims processing time by up to 50% while improving response times by as much as 70%.

But this level of impact is only achieved in an actively managed program. Otherwise, the carrier is reliant on self-selection by engaged policyholders, which limits results. Bolt has found that with managed engagement and rapid response it is possible to meaningfully lower non-weather water risk. In fact, our revised rate filings show a three to eight times increase in effectiveness compared to passive approaches.

Data-Driven Underwriting

Data is king for effective underwriting. Being able to bring data from connected devices alone has value, as we noted above. Now add the ability to crunch data on weather patterns, analyze building specifications, and even introduce data on behavioral indicators. 

Together, this gives underwriters embracing underwriting innovation almost unprecedented insights into risk profiles. With these predictive insights in insurance, carriers can more accurately segment customers by:

• Identifying high-risk properties that may appear deceptively “normal.”
• Recognizing low-risk properties that conventional underwriting may misclassify.
• Developing dynamic pricing strategies, reflecting real-time risk conditions.
• Incentivizing (and rewarding) risk-reducing behaviors among policyholders.

Prevention Technology in Action with bolt

This growing need for proactive risk management and smarter underwriting innovation was one of the key drivers behind bolt Prevention Technology, which integrates seamlessly into underwriting, claims, and even policy workflows, offering actionable risk insight to reduce water-related losses at every step.

bolt Prevention Technology has been proven to decrease water loss claim frequency by 39% and severity by 12%, helping insurers cut costs and improve profitability. As bolt CEO, Jon Walheim, notes, many sensor programs fail to drive policyholder engagement or integrate risk insights directly into carrier workflows.

Historically, it has been challenging to unite sensor adoption and policyholder engagement for sensor providers alongside carriers. What sets bolt Prevention Technology apart is the ability to leverage bolt’s integration to carrier systems create our unique and effective loss mitigation experience.  We help insurers bridge that gap, ensuring actionable prevention data, targeted follow-ups, and better compliance, leading to lower risk and measurable claim reduction for better underwriting performance.

Additionally, bolt Prevention Technology provides automated notifications and guidance, ensuring sensors are used properly to minimize non-compliance and remove inactive devices. Carriers can also work with a range of sensor partners, allowing insurers to customize and scale as needed by different risk profiles. 

With simple integration into existing underwriting workflows, there’s no need to overhaul operations. bolt’s Prevention Technology will fit into current processes, offering new prevention capabilities through:

• Stronger Risk Selection: Identify optimal candidates for prevention programs, using property characteristics and risk algorithms.
• Dynamic Risk Assessment: During the policy lifecycle, real-time data allows carriers to update risk profiles continuously, for more precise policy administration.
• Pricing Optimization: Using relevant and specific data for each property or asset helps keep pricing optimized and realistic.
• Risk Intervention Opportunities: Education and adoption change the fundamental risk profile as sensors increase awareness and can detect policy impacting concerns, then automatically alert policyholders so minor issues don’t escalate.
• Claims Integration: In the event of a loss despite these preventative measures, data history offers adjusters the insights they need to streamline the claims process and carriers insight to future prevention.

Using tools like bolt Prevention Technology to foster proactive risk reduction alongside underwriting innovation helps insurers deliver value throughout the insurance lifecycle. The result? Improved outcomes for both carriers and customers.

Implementing Proactive Risk Management in Your Underwriting Strategy: Actionable Steps

For carriers looking to improve their proactive risk management, the path can seem overwhelming. However, with the help of bolt’s Prevention Technology, implementing predictive insights in insurance needn’t be a headache.

We suggest that carriers start by assessing their portfolio to identify where the highest loss ratios or more preventable claims occur. For many, that will be water damage in residential properties, as we’ve examined, and also specific commercial risk segments.

From here, carriers can launch targeted pilot programs, establishing clear data metrics to track success across financial and customer experiences. Next, look at working with a prevention partner like bolt, with the experience to integrate our technology with existing policy management or underwriting systems.

Lastly, make sure underwriters are equipped to incorporate this prevention data into their existing decision-making, and look to developing pricing structures to incentivize and reward policyholders for participating in prevention programs and keeping their risk low.

Embrace a Preventative Future with Proactive Risk Management and Data-Driven Underwriting

The shift to proactive risk management is set to transform how the industry thinks about underwriting. Reducing loss frequency isn’t just about carrier profitability, although that’s always a compelling side effect.

When carriers embrace predictive insights in insurance, they shift from being a “pay platform” that only reacts after disasters to being active partners in risk mitigation. 

As claims escalate in severity and climate risk intensifies, carriers who wish to thrive need to move past reactive models and embrace the power of prevention through underwriting innovation.

If you’re ready to take your underwriting from reactive to proactive, bolt can help you integrate prevention technology into your workflows hassle-free. Learn more about bolt Prevention Technology, or contact us for a demo today. 

Sponsored by ITL Partner: bolt

It's sometimes said, by those observing the geopolitical unrest, the economic stresses, the warming climate, and the fierce debates over culture and policy, that the world is coming apart. But it would be truer to say that it is, in fact, knotting itself more tightly together.

Consider the following scenario: The climate crisis makes extreme weather more common. Fires and floods damage roads, ports, data centers, factories, and other critical infrastructure. Damaged infrastructure disrupts supply chains, leading crops to fail, ships to be delayed, and energy to become scarce or costly. Prices rise. Trust in institutions falters. Unrest brews. Now concerned with problems at home, states turn inward. Protectionist policies are signed into law. Internal conflicts, over tax, trade or migration, grow more intense. The economic and social instability makes for fertile ground for geopolitical unrest. Nations target each other's infrastructure, companies or information systems. Cyberattacks, state-sponsored and otherwise, rise. That weakens our response to climate disasters. And so the cycle begins again.

Even presenting this as a cycle is slightly misleading. This phenomenon involves overlapping, interlocking risks. The link between cause and effect becomes less and less easy to see. These challenges I've mentioned are almost like different threads in the same tangled rope.

What is going on here?

The answer to that question was given some years ago by the late French philosopher Edgar Morin. What Morin perceived is that in a globalized and increasingly complex world, challenges would not arrive one by one but all at once – that they would be linked, layered and mutually reinforcing. He was writing in the early 1990s, just after the end of the Cold War, and noticed that the rapid expansion of economic globalization threw light on how deeply connected national economies and infrastructures had become. Societies were vulnerable to disruptions that could quickly escalate from apparently isolated local incidents to complex international crises.

Morin was drawing on emerging concepts in systems theory and complexity science. These fields are interdisciplinary or transdisciplinary, involving the study of cohesive groups of related, interdependent components that can be natural or artificial. The fields appeared because it was becoming increasingly plain that we live in a world of systems: climate, energy, digital, economic, political. Each of these is complex enough. Morin's insight was seeing what happened when these overlapped. A political shock in Eastern Europe can drive up food prices in Nairobi. A data breach in Tokyo exposes infrastructure vulnerabilities in São Paulo. One crisis bleeds into another. This is a "polycrisis."

It's easy to see what a challenge this poses for insurance. Tasked with ensuring societal resilience, the world's major insurers have to find a way to grapple with a risk landscape so complex that some are already stepping back. The shift in mindset and approach needed to move from a place where crises are seen as separate events, with their own models, to one where there is really one major crisis that is more than the sum of its parts, is daunting, to say the least.

Given the complexity, and given the cascading nature of polycrisis events, the only practicable approach is to rethink the character of insurance itself. Insurance, traditionally, helps people to recover what they've lost in the wake of a disaster. That's a simplification, of course; but for our purposes here it'll do. Now, insurers have to stop events from happening in the first place, insofar as it's possible, and to prepare their clients well in advance of something happening, should it happen anyway.

No, insurers don't have a crystal ball. But they do have something a little like one. We're already using satellite data to predict fires and floods and monitor fire spread and flood zones in real time. Already, we can use vulnerability analyses and human training programs to protect companies from cyberattacks (most breaches come about because of human error within the company, making the creation of a "human firewall" paramount). We can bring geopolitical intelligence, provided by geopolitical consultants with deep expertise working within governments and their security services, to make sure companies have as much knowledge as is feasibly possible and can make decisions with it in mind.

This is not an exhaustive list of what insurers are now able to do, but they give an idea of the role that technology can play in addressing the biggest challenge that insurance has ever faced. The tools available to us are transformative. They redefine what insurance is, but to the degree that the role of insurance is to ensure societal resilience, they in fact "complete" insurance, allowing us to be the very best version of ourselves. We are becoming a proactive partner, and in a world like this, that's exactly what businesses need.

Let me be clear: The polycrisis can't be solved. But it can be navigated. Businesses can become more resilient and robust, and the protection gap can be closed. That means that those who run and work in those businesses can sleep soundly and chart a course across the choppy waters of this polycrisis.

This is the future of insurance, and it's already here.

Artificial intelligence (AI) continues to dominate conversations in the insurance industry. It is being used across the board from risk modeling to claims handling and promises faster insights, more accurate pricing and improved customer experiences. 

But the truth is, no matter how advanced the model, AI is only as good as the data it’s trained on.

For insurers, the difference between an effective AI integration and one that falls short often comes down to a single factor: data quality.

Understanding the Role of Data in Insurance Industry AI Models

At its core, an AI model is a system that learns from past data to identify patterns and make more accurate predictions than traditional insurance algorithms. Over time, an AI model can simulate decision-making processes, flag anomalies, or suggest next best actions. For example, AI can help evaluate the likelihood of a claim going into litigation or estimate the cost of a payout.

Insurance organizations generate and manage large volumes of data. This includes structured data like policy details, claims histories, and property characteristics, as well as unstructured data such as adjuster notes, medical notes, and accident and property images. 

This same data serves as the foundation for training AI models. But for these models to work as intended, they need to be trained on high-quality datasets. 

The Risks of Incomplete or Inaccurate Data

If the data used to train an AI model is missing key variables or is inconsistent across records, the resulting outputs will be flawed. This can lead to underpricing risk, inaccurate claim predictions, or compliance issues. For instance:

• Incomplete data may cause the model to miss important risk factors
• Inaccurate data may result in unreliable predictions or pricing
• Biased data can unintentionally discriminate or underperform for certain populations

Insurance is a high-stakes, highly regulated environment. Data integrity influences not only outcomes but regulatory compliance and customer confidence. Therefore, when the data used in AI models is accurate, real-time, and comprehensive, the advantages of AI become far more obtainable. 

Where Clean Data Drives the Most Value

Risk Management: AI helps insurers shift to more accurate predictive frameworks. When fueled by high-quality data, models can assess systemic or correlated risk across portfolios. This enhances catastrophe modeling and improves early warning systems.

Underwriting: Underwriters can leverage AI to rapidly analyze applicant profiles, identify hidden risk factors, and deliver more personalized pricing recommendations.

Claims: AI can improve claims management for both claimants and insurers by triaging claims more quickly, flagging inconsistencies, and even suggesting optimal resolution paths. 

Compliance and Explainability: Regulators increasingly want to know not just what decisions were made but how insurers are making them. If the data trail is messy or undocumented, insurers will struggle to demonstrate fairness or explain the rationale behind automated outcomes.

Building the Right Data Foundations for AI Insurance Models

Clean data isn’t something that just happens. It requires effort and investment—from consistent data governance practices to systems that capture and store relevant and accurate data. It also means knowing when to look beyond your own walls.

Many carriers find that supplementing in-house data with anonymized, contributory industry data can expose their AI models to a broader set of scenarios and outcomes, improving accuracy across geographies and lines of business.

What If Your Organization Doesn’t Have Enough Quality Data?

One of the biggest challenges insurers face when adopting AI is realizing that their internal data, while valuable, is often not enough on its own. It may be limited in volume, skewed to specific geographies or products, or lack the historical depth needed to train robust models. Or there may be data quality issues such as missing fields that would undermine a model’s reliability. According to a recent Deloitte AI Institute report, nearly one third of companies surveyed say that data-related challenges are among the top barriers holding back their AI efforts.

To address these data issues, many insurers are starting to explore solutions such as: 

• Participating in de-identified industry data consortiums. 

• Supplementing internal data with licensed, external datasets

• Partnering with organizations that curate and maintain high-integrity training sets

• Investing in tools and governance practices that improve data quality upstream

By leveraging these approaches, insurers can gain access to large-scale, anonymized datasets that reflect a much broader range of underwriting scenarios and claims outcomes. Broader, cleaner datasets reduce blind spots, strengthen explainability, and support better predictions across lines of business and populations. 

Looking Ahead

The power of AI in insurance lies not just in more efficient workflows, but in better predictive insight. And insight depends on quality input. 

As the industry continues its current transformation, organizations that invest in strong data foundations will be better equipped to gain the full value of AI. Accurate algorithms matter. But the real power lies in clean, relevant, quality data.