I've never considered myself a flag-waving women's rights advocate. My philosophy has always been simple: Put your head down and do the work, and things will work out. For much of my career, I believed that hard work alone would even the playing field, regardless of gender.

But as I've learned more about workforce dynamics—especially in the insurance industry—and looked more closely at the data, one reality has become increasingly clear: Women, particularly those nearing or in retirement, are up against a set of challenges.

Sadly, the retirement landscape is very different for men than women. Women, on average, earn less than men throughout their careers, which directly affects their ability to save for retirement. According to the U.S. Census Bureau, women working full time in the insurance industry still earn just 82.7 cents for every dollar earned by men. This gap widens for women of color and as women advance in the ranks. Over a lifetime, this pay disparity can amount to hundreds of thousands of dollars in lost earnings.

Don't worry, this is definitely not an article that will try to sort that out! There are  some easons for the disparity, such as taking time off to raise kids and caring for aging parents. The fact is, though, these income differences have lasting consequences. Research from the Transamerica Center for Retirement Studies reveals that the median retirement savings for women is just $43,000, less than the $91,000 for men. More strikingly, nearly 50% of women aged 55 to 66 have no personal retirement savings at all.

The real kicker is that women live longer. On average, women in the U.S. outlive men by about five years. That means women must stretch their fewer financial resources across more retirement years, increasing their risk of outliving their savings.

Rather than viewing this as a societal problem to fix, insurance companies ought to see it as a strategic opportunity. The hiring of female retirees to do project work isn't about charity—it's about performance. From underwriting to actuarial work, claims to compliance, this industry rewards institutional knowledge, professional discipline, and detail-oriented thinking. Female retirees offer all of that, along with years and sometimes decades of practical experience.

Like their male counterparts, female retirees often require less onboarding and can make immediate contributions. Male and female retirees alike can serve as mentors to younger professionals, helping to bridge the generational gap. In an industry where critical knowledge is often undocumented or informally passed down, this transfer of knowledge is crucial.

Additionally, diverse teams are smarter, more innovative, and more resilient. In fact, a 2015 McKinsey study of 366 companies found that those with diverse management were 35% more likely to have financial returns above their industry mean.

Hiring female retirees to take on project work is more than an HR initiative—it's a smart business strategy. It aligns with the insurance industry's core strengths: assessing risk, planning for the long term, and building trust. These women have navigated complex careers while balancing family lives and households. They're now ready to continue contributing by helping companies achieve their goals.

Insurance companies have an enormous opportunity to look at retirement not as an ending but as a new beginning. By hiring women for project work, companies can support meaningful second chapters for women while enriching their organizations with valuable talent.

How many more children must die because of misinformation and downright lies about childhood vaccinations? The flu has resulted in 216 childhood deaths across the country so far this year, the highest number in 15 years due to the failure of many parents to get their children the flu shot.

National data shows the percentage of children getting vaccinated for the flu has fallen from 64% to 49% in the past five years. I have had college-educated people tell me they do not get vaccinated because they got the flu from the flu shot. That is scientifically impossible. They had the flu before they got the shot. Also, just as with the COVID vaccine, you may still get the flu after the flu shot but with dramatically less severe symptoms.

The worst measles outbreak in 30 years across the country has resulted in over 900 cases, 650 in Texas alone, with three deaths and serious illness among highly contagious children. The reason is, there are parents who believe the measles vaccine is more dangerous than the measles. Most children are not vaccinated based on the false belief that the measles vaccine causes autism.

The original link between autism and the measles vaccine was the most discredited "study" in the history of public health. (See, To Be or Not to Be Vaccinated, ITL, April 28, 2015.) In fact, international health officials called the supposed link to the MMR (measles, mumps and rubella) vaccine "the most dangerous public health hoax in the past 100 years."

The original researcher was found to have serious conflicts of interest, including accepting money from attorneys involved in lawsuits against vaccine manufactures, manipulating evidence and breaking ethical codes of conduct. Yet this outright lie lives on.

The measles is so dangerous that a single person can spread the disease to between 11 and 18 people, and an unvaccinated person has a 90% chance of infection. The real danger is that parents who don't get their children vaccinated put other people at risk, such as infants less than a year old and children and adults with weak immune systems.

There is also now a surge in whooping cough cases in the country. There are over 8,000 reported cases already, more than double the number in 2024. Whooping cough or pertussis is a very dangerous disease and can spread rapidly, especially for infants one to two years old. Infants are too young to have had all their shots. Whooping cough begins as a common cold and can progress to violent coughing that can be fatal. There were two infant deaths reported in Louisiana in the last six months.

We need a national public health campaign for parents who need to get their children vaccinated for the flu, the measles and other childhood vaccinations.

All these childhood deaths this year due to the measles, the flu and whooping cough were 99% preventable. It is heart-breaking that in today's social media and political environment public health officials feel they are under attack by people spreading false information and downright lies about the safety and protection provided by childhood vaccinations.

(I would like to dedicate this article to my niece Chandler, co-author of the original ITL article, "To Be or Not to Be (Vaccinated)" 4/28/15. She is now Chandler Berke, MD, and about to embark as a resident for the next seven years at Ohio State University's Wexner Neurosurgical program.)

According to McKinsey, 71% of organizations already use generative AI in at least one business unit, up from 33% in 2023. Meanwhile, a survey by Tidio finds that 82% of consumers would rather chat with a bot than wait in a support queue, underscoring a sharp shift in service expectations. These rapidly multiplying conversational AI statistics show the technology's evolution from novelty to enterprise necessity—and they highlight why error and bias mitigation can no longer be afterthoughts.

Large language models (LLMs) remain prone to hallucinations and systemic bias. A recent medical‑question benchmark reports a 29% hallucination rate for GPT‑4, 40% for GPT‑3.5 and 91% for Bard (PubMed). Bias persists, too; the U.S. National Institute of Standards and Technology warns that reaching zero bias is impossible, but says that structured audits can meaningfully reduce it. Enterprises that skip governance face reputational risk, regulatory scrutiny and customer churn.

Best Practices for Cutting Errors and Bias

The most reliable conversational agents pair technical guardrails with human oversight, updated continuously rather than retrofitted after launch.

Curate and debias training data

Accenture urges teams to "systematically strip biased or low‑quality data before fine‑tuning." Start with a data inventory: Flag personally identifiable information, duplicate entries and out‑of‑date documents. Next, run demographic‑parity tests and syntactic‑diversity checks to detect skew. Removing or reweighting problematic slices before training cuts both hallucinations and discriminatory outputs downstream.

Apply RLHF or RLAIF

Reinforcement learning from human (or AI) feedback has become the dominant alignment method. An OpenReview study shows multi‑turn RLHF can halve toxic completions compared with single‑turn tuning. Organizations should gather domain‑specific preference data—think safe medical advice or financial disclosure accuracy—and iterate reward models every quarter to keep pace with evolving norms.

Set up guardrails and policy filters

Rule‑based moderation is not outdated; it is the front line against jailbreaks and prompt injections. The 2024 Safety4ConvAI workshop cataloged pattern‑matching guardrails that blocked more than 90% of unsafe responses in a red‑team test without degrading helpfulness. Combine static rules with classification models to catch disallowed content in real time.

Continuous automated evaluations

A live scoreboard is more useful than a quarterly PDF. The Hugging Face hallucination leaderboard records GPT‑4 at a 1.8% hallucination rate on standard tasks; new model checkpoints can be benchmarked immediately, alerting engineers when regression creeps in. Plug such automated suites into your CI/CD pipeline so every deployment pushes fresh metrics to dashboards.

Human‑in‑the‑loop review

McKinsey finds firms that blend expert reviewers with automation cut total error rates by up to 50% within six months. Schedule random audits of conversation logs, tag edge cases and feed them back into RLHF loops. Human reviewers remain indispensable for subjective judgments such as tone, cultural nuance and brand alignment.

Publish transparent system cards

Before release, OpenAI posts model‑specific system cards detailing jailbreak tests and residual biases (OpenAI). Anthropic follows a similar disclosure protocol for Claude 3, noting lower scores on the BBQ bias benchmark (Anthropic). Adopting the same practice builds regulator and user trust, and it clarifies known limitations for downstream integrators.

Implementation Checklist

1. Baseline your model. Run hallucination and toxicity benchmarks on the unmodified LLM to establish starting metrics.

2. Sanitize data. Apply automated deduplication, profanity filters and demographic balancing.

3. Fine‑tune with RLHF. Use domain experts to label high‑risk prompts; train reward models on multi‑turn dialogs.

4. Embed guardrails. Deploy rule‑based filters at both input and output layers; monitor latency impact.

5. Automate evaluations. Schedule nightly hallucination, bias and jailbreak tests; trigger alerts on drift thresholds.

6. Insert humans. Rotate reviewers across time zones; audit flagged exchanges and feed insights back to engineering.

7. Publish transparency reports. Release system cards that document methods, known gaps and mitigation plans.

Obstacles and How to Overcome Them

Compute cost. RLHF and continuous testing are resource‑intensive. Mitigate by distilling smaller inference models or batching evaluation workloads during off‑peak hours.

Tooling fragmentation. No single platform covers data labeling, testing and deployment. Adopt open standards (e.g., OpenTelemetry traces for model metrics) and modular APIs to ease integration. To avoid getting locked into fragile or siloed setups, it's important to invest in open standards and modular architecture early on. For example, using standardized telemetry tools like OpenTelemetry can help you capture consistent metrics across training and inference stages. APIs should be designed to support pluggable modules so that tools for labeling, evaluation, or deployment can be swapped out as needs evolve. A modular mindset also future-proofs your stack against inevitable shifts in vendors, frameworks, or compliance requirements.

Regulatory flux. AI‑specific rules vary by region. Build a governance layer that maps local regulations to technical controls—such as data residency toggles—to avoid retrofits later. What's compliant in one region may be flagged in another. Emerging regulations touch on everything from explainability and algorithmic fairness to data localization and the handling of personally identifiable information. This patchwork environment creates uncertainty for teams that want to deploy AI products globally.

Rather than waiting until after a rule is finalized to adapt, build flexibility into your compliance stack from the outset. One effective approach is to create a governance layer that maps regulatory requirements to specific technical controls within your system. For example, implement configuration options that allow you to toggle data residency or anonymization rules depending on user location. If a region mandates additional model explainability or bias mitigation, your governance layer should be able to route those workflows dynamically without rebuilding core components. Proactively aligning your development process with evolving legal frameworks helps avoid costly retrofits later—and positions your team as responsible AI stewards.

Outlook

Analysts at Accenture forecast the conversational‑AI market will triple by 2028, driven by guardrailed, bias‑checked agents that replace first‑generation chatbots. Visual benchmarking sites predict rapid convergence toward single‑digit hallucination rates as evaluation loops tighten and policy filters mature. Expect native multimodal models to introduce fresh bias vectors—image, video, even sensor fusion—but the same best‑practice framework will apply: clean data, continuous tests and transparent cards.

Conversational AI is racing from pilot to production, and with scale comes scrutiny. Teams that embed rigorous data hygiene, reinforcement learning, guardrails and human oversight into every release cycle will slash errors, tame bias and build the trust needed for the next wave of AI‑driven dialogue.

Over the past decade, technology has revolutionized nearly every aspect of the claims process, from initial inspections to final resolutions. Drone technology, in particular, has emerged as a powerful tool for addressing some of the industry's most persistent challenges, including the need for increased accuracy, faster speed, and more cost-effectiveness.

As insurers seek ways to enhance their operations, the integration of drones with artificial intelligence (AI) and machine learning (ML) offers an unprecedented opportunity to rethink how property inspections and claims evaluations are conducted.

How Drone Technology Enhances Claims Processing

Traditionally, property inspections have required significant time and labor, often leading to delays in claims resolution and the potential for human error. Drone-based inspections address these issues directly. Equipped with high-resolution imaging capabilities, drones can capture comprehensive visual data of a property's exterior in a fraction of the time required for manual inspections.

This data is then analyzed using AI and ML to detect roof abnormalities, structural damage, and other potential issues with a high degree of precision. These technologies eliminate much of the subjectivity that has long characterized claims assessments, providing insurers with reliable, consistent insights.

Introducing Repair Estimates for a Seamless Workflow

While drone technology has become increasingly common for property inspections, recent advancements have expanded its applications to include repair estimation. This is a critical step forward, as it allows insurers to move seamlessly from damage analysis to actionable solutions.

For example, drone-based property inspection platforms are now being enhanced to include repair estimates. By integrating these capabilities, insurers can streamline the entire claims process—from initial inspection to final resolution—saving time and reducing operational costs while improving the experience for policyholders.

Why This Matters for Insurers and Policyholders

The benefits of these advancements are significant for all stakeholders. Insurers gain a faster, more efficient workflow that enables them to process claims with greater accuracy and consistency. For policyholders, the impact is equally profound. Faster claims processing means quicker access to the funds they need to recover from property damage, while the transparency offered by drone and AI technology builds trust in the claims process.

The Broader Context: Technology's Role in Insurance Innovation

Drone technology is not an isolated advancement; it is part of a broader wave of innovation sweeping the insurance industry. According to a recent Deloitte report, insurers are increasingly leveraging AI, blockchain, and IoT to modernize their operations and better respond to the needs of their customers. These technologies are helping insurers enhance risk assessment, reduce fraud, and improve customer satisfaction.

By adopting tools like drone-based inspections and repair estimation, insurers position themselves at the forefront of this transformation, meeting the demand for smarter, faster, and more responsive claims solutions.

Looking Ahead

As the insurance industry continues to evolve, the integration of advanced technologies like drones, AI, and ML will become increasingly essential. These tools not only enhance operational efficiency but also have the potential to reshape the relationship between insurers and their customers, fostering a new level of transparency, trust, and collaboration.

For insurers looking to stay ahead of the curve, embracing these innovations is no longer optional—it's imperative. By leveraging the capabilities of drone technology, the industry can move closer to a future where claims processing is not just faster and more accurate but also more equitable and customer-centric.

In today's rapidly evolving digital landscape, insurers stand at a juncture. The integration of advanced technologies such as artificial intelligence (AI) and the rise of insurtech innovations promise to revolutionize traditional insurance operations. However, the key to unlocking these advancements lies in one foundational shift: robust data modernization.

High-quality, integrated data systems are now essential to an insurer's adaptability and long-term success. Data not only underpins effective AI applications but also forms the backbone of the integration or promise of most insurtech solutions. Without a modern data infrastructure, insurers risk falling behind in a market that increasingly values speed, accuracy, and personalized experiences.

Understanding Data Modernization

What do we mean by data modernization? It is a comprehensive and strategic approach to improving an organization's data architecture. Often, this means moving from outdated legacy systems to advanced, scalable platforms. It involves migrating data storage and processing to cloud-based solutions, integrating disparate data sources across the organization, processes, and third-party providers, and implementing real-time analytics capabilities. The goal is to create a unified, agile data environment that supports informed decision-making and enhances operational efficiency.

The Imperative for Insurers

For the insurance sector, data is the cornerstone of virtually every function—from underwriting and risk assessment to claims processing and customer engagement. Traditionally, insurers have relied on data silos, inconsistent formats, and inefficient processes, which lead to delays and hinder their ability to respond swiftly to market changes and customer demands. Modernizing data systems resolves these issues by ensuring data is accurate, accessible, and actionable.

Enhancing Regulatory Compliance

One of the primary drivers of data modernization is regulatory compliance. The insurance industry operates within a complex regulatory framework that varies across jurisdictions. Maintaining compliance requires meticulous data management and strong reporting capabilities. Modern data platforms facilitate this by providing secure, transparent, and easily auditable data trails. Automated compliance monitoring and reporting tools can be integrated, reducing the risk of non-compliance and associated penalties. Data modernization is essential for insurers navigating the intricate web of local, national, and international regulations.

Operational Efficiency and Cost Reduction

Another major driver is operational efficiency and cost reduction. Outdated data systems often lead to redundant processes and prolonged cycle times. By adopting modern data architectures, insurers can automate routine tasks, streamline workflows, and reduce operational costs. For example, AI-powered analytics can process vast datasets in seconds, providing insights that would take humans significantly longer to derive. This efficiency lowers operational expenses while enabling employees to focus on more strategic activities.

Competitive Advantage: Meeting Evolving Customer Expectations

Today's consumers expect personalized, digital-first interactions. Modern data systems enable insurers to analyze customer behavior and preferences in real time, allowing for customized products and services. This personalization fosters customer loyalty and can be a significant competitive differentiator. Leveraging AI and modern data architectures enables insurers to create intelligent decision-making frameworks that enhance customer experiences.

AI Enablement Through Data Modernization

Artificial intelligence thrives on high-quality, well-structured data. Applications such as predictive analytics, automated underwriting, and fraud detection rely on seamless access to comprehensive datasets. Modern data infrastructures provide the necessary foundation for these AI applications to function effectively. Without such a foundation, AI initiatives may yield inaccurate results or fail to deliver actionable insights. Integrating AI into insurance operations can enhance risk estimation accuracy and drive better pricing strategies.

Building the Data Foundations for AI and Cloud-Native Insurance

As insurers adopt modern platforms like Guidewire Cloud, they're also rethinking how to manage data access and analytics in a more distributed, cloud-native environment. This shift presents an opportunity to streamline reporting and unlock greater agility—but it also requires retooling traditional approaches to data extraction and business intelligence. To accelerate this transition, some insurers are working with consulting partners who offer pre-built frameworks for accessing and organizing cloud-based data, ensuring continuity in analytics while laying the foundation for AI and compliance-ready data environments. Firms with deep expertise in insurance data architecture and governance can help insurers make this leap efficiently and strategically.

Driving Innovation Through Insurtech Partnerships

Data and analytics fuel innovation in insurance. The insurtech landscape is filled with startups offering solutions that can transform various aspects of insurance operations. However, integrating these solutions requires a modern data environment that supports interoperability and scalability. Collaborations between traditional insurers and insurtech firms can lead to the development of new products, improved customer engagement tools, and more efficient claims processing systems. For example, companies like Earnix or hyperexponential deliver AI-based solutions to insurers, enhancing their data analytics capabilities and enabling data-driven decision-making.

Making Quick Progress in Data Modernization

Data modernization is achievable, and insurers can make significant progress quickly with the right approach. By focusing on key areas and working with an experienced consulting partner, organizations can streamline their transformation and see real benefits faster. Here are a few key considerations:

1. Start With Strong Data Governance – Implementing clear policies and frameworks ensures data accuracy, security, and compliance from the start. A well-structured governance plan makes future enhancements easier.
2. Adopt Scalable, Cloud-Based Technologies – Modern cloud solutions allow insurers to expand their data capabilities as needed, reducing costs and improving flexibility.
3. Promote a Data-Driven Culture – Encouraging data literacy and empowering employees to use insights in their daily work helps organizations maximize the value of their data.
4. Leverage Expert Support – Partnering with experienced insurtech firms and technology providers can accelerate integration, helping insurers adopt innovative solutions with minimal disruption.

Data modernization is not merely a technological upgrade—it is a strategic imperative for insurers aiming to thrive in the digital age. By overhauling data infrastructures, insurers can enhance regulatory compliance, operational efficiency, and customer satisfaction. Moreover, a modern data foundation is essential for leveraging AI and fostering innovation through insurtech collaborations.

As the industry continues to evolve, those who prioritize data modernization will be well-positioned to lead in a more agile, customer-centric future. With the right strategy and expert support, insurers can unlock opportunities and remain at the forefront of the digital transformation of insurance.

Sunny Wadhwa, managing director/head of asset management sales at Conning, spoke with Andy Pace, managing director/portfolio manager at Conning, and Don Townswick, managing director/director equity strategies at Conning, about the current state of the equity market and why they think the firm's high-dividend equity strategy is ideal for insurance portfolios.

What is causing current market volatility?

DT: President Trump's April 2 "Liberation Day" introduction of new tariffs certainly led to a spike in volatility, but there has been a great deal of uncertainty gripping markets for some time now.

There are some outstanding questions about fiscal and regulatory policy that still need to be answered: Will tax cuts from Trump's first term be allowed to expire? Will the Trump administration bring forth a lighter regulatory environment, which would likely be helpful to businesses? Will the economy be slowed by federal budget deficits, and will immigration enforcement hamper labor markets? We also don't know how the U.S. Federal Reserve will respond to sticky inflation and economic growth concerns.

With this much uncertainty, it is no surprise that equities are volatile and that they may remain so for an extended period.

Will equity market conditions change? What do you think will happen?

DT: Markets are always changing, and this market will, as well. As we study the current dynamics, we actually see a silver lining in the outlook for dividend-equity stocks.

The stocks recently dominating S&P 500 Index performance have been in a narrow band known as the "Magnificent 7" stocks: Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia, and Tesla. Most other stocks in the index have been performance laggards. The outstanding earnings (which are closely related to net income) performance of the "Magnificent 7" have begun to taper and are projected to settle into a lower range, while earnings for the rest of the stocks in the S&P 500 are projected to rise through the rest of 2025.

We think this implies that a correction in the "Magnificent 7" stocks could happen along with a broadening market in which the rest of the market goes down less than the "Magnificent 7," while providing competitive returns when the market turns up again. The first implication already seems underway, as high-quality stocks have dropped far less than the "Magnificent 7" stocks. It remains to be seen whether the second part of that hypothesis will come to pass.

How are Conning clients reacting to the recent equity volatility?

DT: As most of our clients are insurance companies and tend to be fairly risk-averse equity investors, they are holding their allocations; we are not seeing wholesale buying or selling.

However, in many client conversations we've noted that a large number see this "tariff induced" selloff as a buying opportunity, once the acute uncertainty abates. Our clients have a very long horizon for their equity investments, and equities are usually the longest-duration asset they own. They often look to add during market dips or in situations like we are currently in, and like us, many clients believe the current selloff is overdone. The broadening of the market away from the "Magnificent 7" stocks has certainly been welcomed by our clients after two years of a small number of stocks driving the majority of the market's returns.

Given the volatility, why do insurers include equity in their portfolios at all?

AP: NAIC and state regulatory requirements limit insurance portfolio allocations to equity, and while most insurers don't like surplus volatility, they do understand the need to have an equity allocation to grow surplus. Conning's recommended approach in recent years has been an allocation to our lower-volatility, higher-dividend strategy.

Why do you support an equity strategy focused on dividend-paying stocks?

AP: Initially, what led us to develop this strategy was the period of historically low interest rates post-Great Financial Crisis: We saw the equities of many high-quality companies offering dividend yields that exceeded the yields of their long-term debt. The dividend income, plus the potential for growth, as well as the lower beta (historical beta of 0.82 to the S&P 500) and higher quality approach (average NRSRO credit rating of the holdings of A), made a strong case for insurers to consider an allocation to this strategy, either solely or as a complement to their existing equity allocations.

Lastly, dividends have contributed a meaningful share of the total return of equities over long time horizons and, in periods of uncertainty or volatility, provide a stabilizing element to performance.

Tell us about the process: How do you build the portfolio?

AP: Our goal is to create a diversified portfolio of companies with strong balance sheets and free cash flow, that have higher dividend yields than the S&P 500, a history of stable payouts and dividend growth, and potential for capital appreciation.

Our rules-based method of building the portfolio has been the same since inception, repeated in the last month of each quarter and based on a disciplined three-step process (quantitative screening, qualitative review, and finally name selection) designed to filter the universe of potential investment candidates from the S&P 500. So not only is this a low-volatility, high-quality, value-oriented approach, it is also a low-turnover/cost approach, as we only trade on a quarterly basis.

How do insurance portfolios benefit from this dividend-focused equity strategy?

AP: In addition to the higher dividend income, insurers have also benefited from the strategy's strong historical risk-adjusted return, which since inception has provided upside market capture of 90% of the S&P 500's gains; the downside protection has been evident with a downside market capture of only 82% of the index's declines.

Historically, the strategy has performed best when equity markets are experiencing volatility, such as we've seen this spring. Our clients have found having a higher-quality, larger-capitalization, lower-volatility portfolio quite valuable during periods of market uncertainty.

Are there other benefits?

AP: We've talked about many benefits already, but we can't stress enough the importance of the dividend income -- insurance companies can never have enough income! While we do not view equity dividends as a substitute for fixed-income coupons, we believe that an equity component offering a higher dividend than the broad market is of value to income-focused insurers. Lastly, I want to add that our portfolio approach is equal-weighted rather than market-weighted. This strategy was built with insurance companies in mind, and we firmly believe that an equal-weighted approach offers better diversification, reducing concentration risk and potentially leading to better risk-adjusted returns.

The insurance-linked securities (ILS) market represents a major evolution in how risks are transferred, managed and financed globally. Insurance, among the world's oldest commercial activities, offers institutional investors participation opportunities through ILS and reinsurance via capital markets.

The risk transfer market, traditionally dominated by insurers and reinsurers, attracts institutional investors seeking alternative assets that provide both portfolio diversification and stability during market turbulence.

The ILS market has evolved significantly, reaching $50 billion in catastrophe bonds for the first time as of March 2025, according to Artemis reports. This growth represents a 9.7% compound annual growth rate over the past five years as investors increasingly seek alternative assets offering both diversification and resilience to market volatility.

Through instruments like catastrophe bonds and collateralized reinsurance, ILS allow investors to assume insurance risks for competitive returns that remain largely uncorrelated with traditional financial markets. This bridges capital markets and the insurance industry while enhancing the sector's capacity to absorb major risks, particularly from natural disasters.

How ILS Work

In the traditional insurance model, companies sell policies to customers in exchange for premiums, providing coverage for potential future losses. When catastrophes occur and too many policyholders file claims simultaneously, insurers face significant financial strain. To manage this, they often turn to reinsurers.

Catastrophe bonds, a major ILS type, typically involve an insurer issuing bonds through a special purpose vehicle (SPV). Investors receive coupon payments but may forfeit principal if predefined events like earthquakes or hurricanes occur. Various trigger mechanisms determine payouts, including indemnity triggers based on actual losses, parametric triggers tied to objective measurements like wind speed, industry loss triggers based on total market losses, and modeled loss triggers calculated through predetermined models.

For example, a sample catastrophe bond for California earthquake risk might offer investors a 7% annual coupon rate for three years. If no triggering event occurs, investors receive their interest and principal. However, if an earthquake meeting specific parameters strikes, investors could lose part or all of their principal, which the insurer would use to cover policyholder claims.

Market Growth Driven by Climate Change

Weather-related disasters increasingly strain the traditional insurance model. Between 2019-2023, the U.S. averaged 20.4 catastrophic events annually causing over $1 billion in damages each, up from the historical average of 8.5 since 1980.

The distribution of damage from U.S. billion-dollar disasters shows tropical cyclones causing the most damage at $1,427.2 billion (inflation-adjusted), with an average cost of $22.3 billion per event. Drought ($361.9 billion), severe storms ($513.8 billion) and inland flooding ($201.9 billion) have also caused considerable damage.

Rising building values further strain insurers' capabilities, compounded by carriers exiting wildfire and wind-prone markets. This exacerbates coverage challenges in vulnerable areas. In 2023 alone, the U.S. experienced 28 confirmed weather and climate disaster events exceeding $1 billion in losses, resulting in 492 deaths and significant economic repercussions.

Expanding Beyond Natural Catastrophes

While catastrophe-linked products dominate the market, the ILS sector is expanding beyond natural disasters to include other high-impact risks:

Cyber Risk: Catastrophic cyber events such as ransomware attacks and widespread outages share many traits with natural catastrophes—they're hard to model, potentially devastating, and increasingly systemic. In 2023, Beazley issued "Cairney," the first cyber catastrophe bond, transferring tail cyber risk to capital markets.

Terrorism Risk: These systemic and infrequent risks fit the ILS model well. France's terrorism risk pool GAREAT issued a $105 million terrorism risk cat bond in 2024, marking an important expansion into man-made perils.

Pandemic Risk: COVID-19 catalyzed interest in pandemic-related securities, although these face complex modeling and trigger challenges. Nevertheless, pandemic-related ILS like mortality bonds are drawing attention from reinsurers and health insurers.

Technological Evolution

Technology plays a crucial role in the ILS sector's development, particularly in risk assessment, modeling, transparency, and operational efficiency. Leading companies in the risk modeling space include Verisk AIR Worldwide, RMS (Moody's), and CoreLogic (EQECAT), which are used in approximately 99% of ILS contracts. Emerging players like CyberCube, Synthetik, Parametrix and Katrisk are also making significant contributions.

Blockchain technology and decentralized finance (DeFi) present new opportunities to enhance efficiency and accessibility. In 2024, Schroders Capital conducted a successful tokenization pilot that enabled reinsurance contracts to be tokenized and traded on a public blockchain platform using smart contracts. This initiative demonstrated potential for automating time-consuming processes like subscriptions and settlements.

The regulatory environment for crypto assets remains uncertain, raising questions about how tokenized ILS should be classified and regulated across jurisdictions. Consumer protection concerns exist, particularly regarding potential retail investor exposure to complex insurance risks. Additionally, the current ILS investor base, primarily pension funds and specialized ILS funds, may adopt crypto-based platforms slowly.

Future Vision

The long-term vision for ILS could include tokenized catastrophe bonds with rapid settlement times, global insurance pools funded by stablecoins, and catastrophe swaps traded on decentralized exchanges. According to Fitch, blockchain-driven applications in ILS are expected to grow as the technology becomes more integrated into the reinsurance sector.

This could eventually enable an insurer to seamlessly transfer specific risks to global investors through smart contracts. These investors would contribute digital stablecoins in a transparent, regulated process. When triggering events occur, payouts would happen automatically, allowing same-day relief through parametric solutions. This combination of speed, automation and trust could redefine how capital is deployed for disaster response and risk management.

Despite challenges, the convergence of digital finance and ILS represents a significant opportunity to create a more efficient, transparent and inclusive risk transfer market. As climate-related events increase in frequency and severity, innovative financial instruments that expand risk-bearing capacity will become increasingly important to global financial resilience.

Only tax evasion exceeds insurance fraud on the list of most costly white-collar crimes in the U.S. In fact, 10% of insurance claims are fraudulent, and the problem is only growing. 

TransUnion says suspected digital fraud attempts surged 80% globally from 2019 to 2022, and generative AI heightens the problem. It lets bad actors manipulate or even make up evidence such as photos and lets them create synthetic identities (combining real and fake personal information).

But generative AI is turning out to be much more of a blessing than a curse, mostly because it so greatly expands the amount of information an insurer can evaluate for signs of fraud, according to a recent report by Deloitte. Unstructured information such as handwritten notes, photos (including their metadata), videos, phone calls and sensor data can all be scanned for inconsistencies and key words or patterns that suggest fraud. 

By 2032, Deloitte says, P&C insurers could save between $80 billion and $160 billion a year on fraudulent claims — if they take the right approach. 

Here are Deloitte's key recommendations for how to save loads of money on fraud. 

The Deloitte report says soft fraud — when a claimant exaggerates a loss — accounts for 60% of fraud and is only caught 20% to 40% of the time. Hard fraud — when someone stages a loss or simply makes it up — accounts for the remaining 40% of fraud and is caught more often, between 40% and 80% of the time.

Improving on detection won't come cheap. Deloitte expects insurers to be spending $32 billion on fraud detection technology by 2032 — but says they could then reduce fraud by between 20% and 40%, more than making up for the technology's hefty expense.

Deloitte lists these techniques as key to fraud detection, while emphasizing the need to combine multiple approaches: 

• "Text analytics. Natural language processing analyzes textual data of claims forms, emails, and social media posts to identify keywords and entities....
• Audio-image-video analysis. Speech recognition and sentiment analysis can examine customer calls for signs of duress.... Photo analytics can uncover irregularities in metadata, manipulation, and repeated use. Causation analytics can identify if alleged injuries were likely consistent with the experienced accident. Video analytics can verify the occurrence and extent of damage, identify authenticity of images, and highlight signs of tampering or staging.
• Geospatial analysis. Satellite images and comprehensive 3D drone footage can verify the extent and location of damage that may not be clearly visible in physical inspections....
• Internet of Things data. Real-time surveillance devices like vehicle telematics can reconstruct accidents and verify the legitimacy of claims. Smart home sensors like water leak detectors and security cameras can help gather evidence that can be used to verify claims and detect fraudulent or staged activities.
• Simulation models. Replicating the behavior of medical providers, repair shops, and others that individuals may work with under different scenarios in a controlled virtual environment can identify patterns and deviations from standard industry practices and detect instances such as overbilling, unnecessary services, and coordinated activities or probable collision rings between entities."

Every honest customer hates fraud, because we all know it's raising prices for the rest of us. Customers are surely even more sensitive about the issue now, given that premiums have risen sharply for a couple of years and that Trump's tariffs are starting to reignite inflation for cars, car parts and materials that insurers use to repair or replace homes. 

So insurers will not only cut costs but be applauded by customers for doing so. Let's do it.

Cheers,

Paul 

In today's fast-paced digital landscape, insurance carriers stand at a crossroads. Traditional operations—burdened by siloed systems and manual workflows—are no longer sustainable. Outdated models not only slow decision-making but also erode customer trust and inflate operational costs. The path forward is clear: Embrace intelligent, AI-driven operations that cut through complexity, deliver real-time insights, and elevate both efficiency and experience.

Intelligent Operations: The Strategic Advantage

Intelligent operations are not just a tech trend— they are the new foundation for competitive advantage. By leveraging agentic AI, machine learning, and real-time analytics, insurers can automate decision-making, streamline processes, and hyper-personalize customer interactions. Agentic AI, in particular, represents a breakthrough: autonomous, purpose-driven agents that learn, adapt, and act independently to deliver outcomes at scale. According to McKinsey, insurers implementing AI are achieving up to a 25% reduction in operational costs and a 25% boost in customer satisfaction—a leap in both efficiency and impact.

The Time for Transformation Is Now

The urgency to act has never been greater. Customer expectations have shifted—demanding instant, seamless, and personalized service across every channel. Legacy systems and fragmented processes simply can't keep up. Meanwhile, insurtech disruptors and digital-native competitors are setting a new bar for agility and innovation. To stay relevant, traditional carriers must reimagine their operations through the lens of intelligent automation. Those that move now will lead the future of insurance. Those that don't risk being left behind.

Taking the First Step: Initiating the Transformation

Embarking on this journey requires a structured approach:

1. Assess Current Operations: Identify areas where inefficiencies exist and where AI can have the most impact.
2. Set Clear Objectives: Define what success looks like—reduced processing times, improved customer satisfaction, or cost savings.
3. Collaborate With Experts: Partner with technology providers specializing in insurance digital transformation.
4. Pilot and Scale: Start with pilot projects, gather insights, and then scale successful initiatives across the organization.
5. Foster a Digital Culture: Encourage continuous learning and adaptability among employees to embrace new technologies and processes.

The Cost of Inaction: Risks of Not Embracing Intelligent Operations

Insurers that delay modernization face mounting risks. Without embracing AI and digital-first solutions, organizations fall behind more agile competitors, struggle with inefficient legacy systems, and expose themselves to regulatory challenges. Operational inefficiencies lead to increased costs, delays, and errors, while outdated systems make compliance more difficult. Most critically, customer dissatisfaction grows as digital expectations rise—leading to churn and a decline in brand trust. The absence of AI-driven insights also means missed opportunities to identify emerging trends and evolving customer needs.

Enhancing Job Satisfaction Through Intelligent Operations

Intelligent operations don't just transform systems—they elevate people. By automating routine tasks, employees are freed to focus on higher-value, strategic work. Upskilling becomes part of the journey, as staff gain new digital competencies that enhance their career prospects and value in a rapidly evolving industry. Streamlined processes also improve work-life balance by reducing burnout and enabling more meaningful contributions. The result: a more engaged, capable, and future-ready workforce.

From Chaos to Clarity: A Strategic Imperative

The transition from fragmented, traditional operations to intelligent, AI-enabled clarity is more than a technology upgrade—it's a strategic imperative. Insurers that embrace agentic, AI-powered, and digital-first operations position themselves to deliver faster, smarter, and more personalized experiences. In doing so, they unlock greater agility, customer satisfaction, and sustainable growth in a competitive landscape that favors innovation and responsiveness.

When people talk about AI in insurance, they usually imagine global giants, shiny dashboards, and Silicon Valley pilot programs. But the reality is far broader—and in some ways, more grounded. Even in emerging markets like Ukraine, artificial intelligence is becoming a silent partner in day-to-day decision-making, risk management, and claims processing.

From Curiosity to Daily Tool: Where We Use AI

I wouldn't call myself an AI veteran, but over the past year, these tools have become a quiet backbone in my work. I use them to analyze market trends, structure internal reports, and systematize competitive data that used to take hours to compile. In short, AI is like having an analyst who never sleeps and never needs a coffee break. (Though if it learns to bring coffee, I'm in trouble.)

Beyond personal use, we're gradually integrating AI into our operational workflows. Some real examples from inside our company:

• Automated underwriting that speeds up policy issuance and improves consistency.
• Remote claims adjustment using computer vision tools to assess damage from user-submitted photos.
• Medical AI integration in health insurance products to provide early diagnostics and smarter pricing models.

Where AI Shines in Insurance (and Where It Still Struggles)

The areas where AI truly adds value are well known:

• Underwriting based on historical and behavioral data
• Fast-track claims resolution
• Actuarial calculations at scale
• Fraud detection through pattern recognition

But the challenge isn't always in the algorithm—it's in the environment. In Ukraine, we lack clear regulation on AI, so companies are hesitant to fully automate sensitive decisions. There are also concerns about customer trust and data ethics. Without clear legal frameworks, some executives prefer to play it safe.

That said, this cautious phase is normal. We've seen it before with online banking. It started slowly—and now no one can imagine life without it.

People First: Culture Matters More Than Code

One of the biggest misconceptions is that AI will replace people. In practice, it empowers them. But that message needs constant reinforcement.

In our company, we've started internal workshops to help teams understand how AI supports—not replaces—their work. Once employees see how tools help them save time or reduce routine, resistance melts away.

Still, change management is essential. Some team members fear the unknown, others are simply overwhelmed by "yet another tool." That's why leadership has to be transparent and patient and lead by example.

East vs. West: What's Different—and What's Not

Compared with Western markets, Ukraine is behind in AI penetration—but not in ambition. While U.S. and E.U. insurers boast automated five-second claims approvals, we're still scaling those capabilities. But the bright side? We get to learn from others and leapfrog past failed models.

In some ways, being late to the party is a strategic advantage.

Final Thought: Start Small, Think Bold

AI is not a threat. It's a lever. A multiplier. An amplifier of human capability. It helps us do what we do—only faster and more intelligently.

My message to industry colleagues: Don't wait for the perfect regulation or the perfect tool. Start where you are. Use what you have. Explore. Adapt. And most of all—bring your teams along for the journey.

The future isn't about man versus machine. It's about partnership.