When most people talk about AI, they focus on cost savings or speed. But there's a more human story unfolding.

Insurers that invest in smarter systems—platforms that automate submission intake, triage, or data rekeying—aren't simply buying efficiency. They're improving the daily experience of their employees. Less time wrestling with spreadsheets and duplicate entry means more time for real analysis, strategy, and client relationships.

It's not hard to imagine AI becoming part of the "total compensation package." In the same way a 401(k) or flexible work policy attracts talent, better tech now keeps people in their roles longer. When employees feel their time is respected, they stick around. The thought of leaving for a competitor is even more daunting as great systems in insurance are hard to come by!

Redefining Productivity

Productivity in underwriting is about to look very different.

Traditionally, carriers measured success by volume: how many submissions an underwriter reviewed, how many policies were quoted, how fast a claim was closed. With automation in play, those measures don't tell the whole story any more. Submission to Quote & Quote to Bind ratios are going to be looked at differently.

As tools like Feathery and other workflow automation platforms take on repetitive, low-value work, underwriters are free to focus on higher-impact decisions—evaluating complex risks, managing relationships, and shaping portfolios.

The result? A future where output expectations rise, but so does job satisfaction. "High performance" will soon mean something different: a blend of human judgment, data fluency, and the ability to guide AI tools effectively. AI is not going to replace underwriters themselves, yet an underwriter using AI will have a clear advantage over one who is not.

The Changing Face of Entry-Level Roles

Every generation of insurance professionals has seen their "first job" evolve. Decades ago, it was filing cabinets and fax machines. Then came Excel. Now, it's maintaining and improving AI systems.

Tomorrow's entry-level employees may spend less time on clerical tasks and more time curating data—keeping AI models accurate and aligned with shifting loss trends, regulations, and coverage language. Tasks could include uploading current guidelines and claims data into an internal GPT tool.

It's a subtle but profound shift: the next wave of underwriting assistants or analysts will act as trainers of digital teammates, not just administrators of manual work. That's a skill set both technical and strategic—a rare and valuable combination.

The Bigger Picture

AI is not replacing insurance professionals; it's redefining the playing field. The insurers that embrace automation as a people strategy, not just an operational one, will be the ones that win the next decade.

Happier employees, smarter workflows, faster decisions—these are all connected. And as the technology improves, the firms that treat it as a tool for empowerment rather than elimination will see the highest returns on both productivity and culture. AI automation in insurance is here to stay and is just getting started.

--Infrastructure is not just a technical issue—it's a moral one. It determines who gets access to opportunity and who is left behind. --Esther Duflo, Nobel laureate, MIT economist

Public infrastructure encompasses a wide range of essential assets (see Figure 1)—including transport networks, water systems, energy infrastructure, public buildings, and digital infrastructure—that underpin the societal and economic functioning of any country.

Figure 1 Public Infrastructure Assets

In the developed world, every nation has experienced a phase of infrastructure boom—driven by post-war reconstruction, industrial expansion, and rapid urbanization—that transformed them from agrarian-based societies into industrialized economies. In many of these countries, a significant portion of the existing infrastructure was built or rebuilt during the post–World War II era, particularly between 1950 and 1970. As a result, these assets are now 50–70 years old. Designed with a finite lifespan of 40 to 100 years, much of this infrastructure is either approaching the end of its intended service life or has already exceeded it.

These assets were designed and constructed using the technological standards, materials, and construction methods available at the time, based on the then-current and projected trends in population growth, demand, usage patterns, capacity requirements, and weather conditions. However, evolving usage patterns, rising demand, rapid urbanization, and increasing exposure to adverse climate and weather conditions are placing significant stress on these assets, leading to deterioration in quality and increased fragility. Although deterioration is typically gradual, aging infrastructure becomes vulnerable to sudden failure when a critical tolerance threshold is breached or when exposed to high-intensity stressors such as extreme weather events. While digital infrastructure is relatively newer compared with other asset classes, it is still susceptible to cascading impacts resulting from failures in interconnected systems.

Catalyzing the Collapse

While any infrastructure asset is naturally prone to wear and tear from years of continuous use, the risk of breaching its critical tolerance threshold is often amplified by a confluence of additional factors (see Figure 2)—including design deficits, maintenance neglect, chronic underfunding, urbanization, changing usage patterns, and climate change with extreme weather events.

Figure 2 Factors Impacting Infrastructure Aging

Design Deficit

Infrastructure systems were originally designed to meet the conditions and demands of their respective eras. At the time of their conception and construction, many of today's developments—such as rapid urbanization, shifting usage patterns, and increasing climate volatility—could not have been reasonably anticipated. Consequently, these assets are now under strain from factors that far exceed their original design capacity. This has resulted in a "design life deficit," where even well-maintained legacy infrastructure is inherently vulnerable to contemporary challenges. Addressing this deficit requires more than routine maintenance; it necessitates fundamental redesigns, capacity upgrades, and substantial improvements in resilience.

Maintenance Neglect

Effective infrastructure maintenance—including protective, preventive, corrective, and rehabilitative measures—minimize service disruptions, improve operational efficiency, extend asset lifespan, reduce long-term costs, and enhance safety and reliability. Conversely, the absence of regular maintenance, particularly deferred maintenance, accelerates asset deterioration. Deferred maintenance refers to the postponement of necessary upkeep until an asset fails or a major issue disrupts normal operations. While routine maintenance may not fully resolve challenges arising from evolving demands and usage patterns, well-maintained infrastructure is significantly less prone to major failures and more resilient to otherwise manageable events. Without consistent maintenance, overlooked or neglected issues can escalate into major problems, making the cost of repair or modernization far exceed that of timely intervention.

Chronic Underfunding

The simultaneously deteriorating infrastructure assets impose compounding maintenance costs, yet budgetary allocations often fail to keep pace with the growing need for upkeep and structural rehabilitation. A key reason for this funding gap is a strategic shift from a "maintain and repair" approach to one focused on "redesign and rebuild." This shift is frequently influenced by political considerations, as governments tend to prioritize greenfield projects that offer greater visibility and political mileage. In contrast, maintenance of existing infrastructure—though critical—typically yields lower political returns. Infrastructure budget allocations are also closely tied to a country's prevailing economic and geopolitical conditions. Nations facing prolonged economic crises or heightened geopolitical tensions often reduce capital expenditure, affecting both the maintenance of existing infrastructure and investment in new projects. These reductions contribute to substantial backlogs, rendering existing funding mechanisms inadequate and unsustainable.

Urbanization

In the developed world, industrialization triggered mechanization, infrastructure expansion, the rise of factory-based employment, and a shift in economic focus from agriculture to manufacturing and services. These transformations led to significant improvements in living standards and accelerated urbanization—defined as the migration of populations from rural areas to urban centers in pursuit of industrial employment. The resulting increase in population density places immense, and often unforeseen, stress on infrastructure systems that were never designed to accommodate today's high-volume demands. Many legacy assets are already under severe strain. However, upgrading or retrofitting these assets presents a complex challenge, particularly in densely populated urban areas. Consequently, infrastructure management is frequently deferred until a disruption forces reactive intervention.

Changing usage patterns and demands

While rising population and urbanization have increased usage loads on infrastructure, the advent of new technologies has introduced both enhanced capabilities and new demands. These developments place considerable stress on legacy systems that were never designed to accommodate such requirements. On a positive note, emerging technologies, advanced materials, and modern construction techniques offer significant potential to improve resilience, durability, structural integrity, and the service life of infrastructure assets. To illustrate the broader context, some indicative changes that have affected infrastructure systems are outlined below.

• Roads and Bridges: Many bridges and roads were designed decades ago for traffic volumes, vehicle weights, and travel speeds that no longer reflect current usage. Over the past five decades, the number of personal motor vehicles and commercial trucks—as well as the speeds at which they travel and the loads they carry—has increased dramatically. With millions of trips made daily on aging and structurally deficient transport networks, these systems are under excessive and sustained stress.

• Dams: Many dams were constructed using engineering standards that were appropriate at the time but are now outdated and no longer compliant with modern safety and design codes. Critically, most of these dams were not designed to withstand the frequency and intensity of extreme weather events observed in recent years. Additionally, rising demand for hydropower imposes fluctuating operational loads on aging structures, further compromising their structural integrity.

• Water Infrastructure: Urban water systems were originally designed to support residential use, but many now serve mixed-use zones that include both residential and commercial areas with vastly different consumption patterns. Legacy pipelines—often made of iron or steel—are prone to corrosion. While modern systems incorporate corrosion-resistant materials, digital leak detection, and smart metering technologies, retrofitting older infrastructure is highly complex. Much of it is buried deep underground, making replacement both difficult and costly. Similarly, legacy stormwater systems were designed for predictable rainfall and lower levels of impermeable surfaces. However, changing weather patterns and increased urban impermeability now frequently overwhelm drainage systems, leading to flooding and system failures.

• Power Generation, Transmission, and Distribution: Many power plants, substations, and transmission lines were constructed decades ago and are not equipped to handle today's elevated demand levels or the increasing frequency of extreme weather events. These systems were not originally designed with the foresight of modern electrical loads, such as widespread electric vehicle (EV) charging infrastructure, energy-intensive data center operations, and cryptocurrency mining.

 • Railway Assets: Many railway corridors operate beyond their designed capacity and at full usage, leading to severe congestion on tracks and at stations. Overloaded infrastructure also reduces the time available for routine safety inspections and maintenance, increasing the risk of accidents such as derailments and collisions. Rising freight and passenger volumes accelerate track wear, shorten asset lifespans, and drive-up maintenance costs.

• Ports: The surge in global trade and the advent of mega-ships have pushed many aging ports beyond their original design capacity. Modern container vessels are more than 15 times larger than those available 50 years ago. Accommodating these vessels requires deeper channels, larger cranes, reinforced berths, and expanded yard space—features that many older ports lack. Additionally, limited rail and road connectivity to ports further compounds bottlenecks, affecting supply chain reliability.

Climate change and extreme weather events

Climate change is not just another risk factor but a powerful force multiplier that amplifies the impact of all other risks and can directly trigger the collapse of infrastructure assets. Most aging infrastructure was designed based on historical climate data and weather patterns. The benchmark parameters considered in infrastructure designs such as temperature, storm frequency, and precipitation levels—are now outdated and no longer reflect current climate realities.

Climate change introduces new stressors—such as rising sea levels, increased temperatures, and more frequent and intense storms—all of which accelerate infrastructure failure. The frequent occurrence of previously rare extreme weather events—such as flash floods from atmospheric rivers, cloudbursts, rapid cyclone intensification, heat domes, and megafires/firestorms—places immense stress on aging infrastructure. While retrofitting infrastructure for climate resilience is essential, it requires substantial funding. Moreover, implementing upgrades without disrupting daily operations poses significant engineering and logistical challenges. An indicative list of climate change and extreme weather impacts on infrastructure is provided below.

• Extreme Heat and Rising Temperatures: Rising temperatures and heatwaves can significantly affect infrastructure performance and integrity. Asphalt on roads may soften, leading to buckling and rutting. Steel components in bridges expand under heat, potentially causing structural stress and misalignment. Thermal expansion can crack concrete and damage expansion joints, which are critical for structural integrity. Extreme temperature variability causes repeated expansion and contraction of metal structures, accelerating material fatigue and joint failure in railway tracks. Heatwaves also strain power infrastructure, causing transmission lines to swell and sag, which reduces efficiency and increases the risk of outages.

• Increased Precipitation and Flooding: Extreme weather events—such as 100-year floods, once considered rare, are now occurring with increasing frequency due to climate change. Heavy rainfall and flooding can damage road surfaces, wash out foundations, accelerate erosion, and lead to complete road closures. Sewage and stormwater systems are frequently overwhelmed, resulting in untreated sewage discharge and damage to water treatment facilities. Dams face heightened risks as increased reservoir inflows and shifting hydrological patterns raise the likelihood of overtopping and structural failure, potentially causing catastrophic downstream flooding.

• Increased Storm Intensity: Frequent and intense storms pose serious risks to infrastructure. High-wind events can cause structural damage, scatter debris, and lead to road closures. Storm surges and wind-driven debris threaten the integrity of dams, particularly older ones not designed to withstand such pressures. Drinking water and wastewater systems are vulnerable to physical damage and service disruptions caused by flooding and debris impact. Power transmission lines are susceptible to wind damage, while substations may be inundated, resulting in prolonged outages and grid instability.

• Sea Level Rise and Coastal Flooding: Rising sea levels significantly affect port infrastructure and coastal assets. Many ports will require elevation of existing structures or the construction of higher seawalls to remain operational. Container yards, warehouses, and terminal buildings face increased flooding risks, while even modest rises in base water levels can cause groundwater intrusion—resulting in foundation instability, basement flooding, and damage to underground utilities and electrical systems.

Managing the Risk

The growing crisis of aging infrastructure is a complex interplay of functional obsolescence, governance deficits, fiscal short-sightedness, evolving usage demands, and the intensifying stressors of climate change. The most straightforward solution to manage the risk is to proactively repair, replace, or retrofit aging assets. However, the scale of funding required, and the operational challenges involved make this a formidable task.

As a primary risk bearer, the re/insurance industry has both a vested interest and a unique capability in recognizing, assessing, and mitigating these complex and evolving risks. Insuring infrastructure assets is not new for insurers, and aging infrastructure represents a low-predictability, high-magnitude risk. While low-impact scenarios may lead to service interruptions, high-impact failures can cause personal injury, loss of life, significant property damage, and substantial disruptions. Catastrophic events—such as dam or bridge collapses or power grid failures—can affect the risk experience across multiple lines of business, including life, health, motor, property, casualty, business interruption, and liability, triggering multi-billion-dollar claims that may even threaten the solvency of insurers.

However, the challenge for insurers now is to assess the worsening nature of risk due to the interplay of several stress factors. Traditional risk models are increasingly challenged and upended by the unprecedented, dynamic, and unpredictable nature of climate change and its compounding impacts across the risk landscape. Moreover, infrastructure deterioration does not follow a linear or uniform timeline across different asset types. Each class of infrastructure experiences non-linear degradation, with deterioration rates varying based on asset type, usage intensity, maintenance history, and environmental exposure. The concurrent aging of these assets elevates overall risk.

Furthermore, infrastructure systems are highly connected and interdependent. Failures rarely remain isolated; a disruption in one system can trigger cascading effects across others. Given this predicament, all responsible stakeholders must adopt a system-of-systems perspective instead of focusing on isolated sectoral issues. Effective use of current-age technologies could tremendously transform how infrastructure is managed by shifting maintenance from reactive fixes to data-driven prevention.

Deploying a network of IoT sensors can continuously monitor stress, vibration, temperature, and corrosion, enabling early detection of structural issues. artificial intelligence and predictive maintenance models can analyze historical and real-time data to forecast potential failures, allowing for timely interventions before breakdowns occur. digital twins can create virtual replicas of infrastructure assets to simulate wear and tear, test scenarios, and optimize maintenance schedules. Drones and robots can inspect hard-to-reach areas, capturing high-resolution images and thermal data to identify cracks, rust, or material fatigue. Geospatial and satellite imaging can detect land shifts, water seepage, or vegetation changes around infrastructure, indicating potential foundational issues. Using cloud-based asset management platforms and big data to aggregate information from multiple sources helps stakeholders collaborate, access real-time data, and make informed decisions on prioritizing which assets need urgent attention based on risk and usage.

As more assets approach the critical point of breaching their failure thresholds, the threats posed by aging infrastructure become increasingly probable and imminent. Insurers must adapt by leveraging technology and real-time data to revise their risk models, underwriting procedures, pricing strategies, and capital reserves to ensure continued resilience. Nevertheless, this recalibration will be a challenging task, as it requires insurers to anticipate the effects of extreme climate and weather events on all types of assets, consider how a single failure may create a domino effect or reverberate across interconnected systems, and measure the complex web of risks that may emerge.

Conclusion

Aging infrastructure is central not only to public safety and economic prosperity but also to the resilience and insurability of modern society. Many of these assets have supported communities for decades and are so deeply woven into daily life that their presence often goes unnoticed—until a failure occurs. Upgrading or retrofitting them is particularly challenging in crowded urban environments, where such projects demand significant funding, may require displacing residents, and present complex engineering obstacles.

Addressing these challenges demands cross-sector partnerships that bring together governments, private-sector asset owners, re/insurance companies, and the public to mobilize resources, align incentives, share risk, and commit to robust investment and transparent maintenance. Only through this collective approach can we hope to modernize the vital infrastructure for a safer, more reliable, and insurable future.

Reference

American Society of Civil Engineers. (2025). A Comprehensive Assessment of America's Infrastructure. https://infrastructurereportcard.org/wp-content/uploads/2025/03/Full-Report-2025-Natl-IRC-WEB.pdf

Homeland Security. (2010). Aging Infrastructure: Issues, Research, and Technology. https://www.dhs.gov/xlibrary/assets/st-aging-infrastructure-issues-research-technology.pdf

Little, R., & Fellow, S. (2012). Managing the Risk of Aging Infrastructure. https://irgc.org/wp-content/uploads/2018/09/R.-Little_Risk-of-Aging-Infrastructure_revision-Nov2012.pdf

Willis Towers Watson. (2020). Ageing Infrastructure - More than a bump in the road. https://www.wtwco.com/-/media/wtw/insights/2020/02/ageing-infrastructure-jan-2020.pdf

The cyber risk and insurance landscape in 2025 reveals a complex and evolving threat environment. Large insured companies are becoming increasingly resilient against cyber-attacks as strengthened cyber security and preparedness and response capabilities help mitigate the impact of some of the large cyber losses in 2025 to date. However, the reliance on digital supply chains, impact of expanding privacy regulation, and more sophisticated social engineering attacks targeting employees are also broadening the scope of potential losses for all companies, according to the latest Cyber Security Resilience Outlook from Allianz Commercial.

During the first half of 2025, analysis of Allianz Commercial cyber claims shows the overall frequency of notifications was in line with activity a year earlier, with around 300 claims. Despite the increasing sophistication and volume of attacks companies face, claim severity has declined by more than 50%, while the frequency of large-loss claims is down by around 30%, driven by larger companies' cumulative investments in cyber security, detection and response.

However, the expanding risk landscape means there is no room for complacency. Ransomware attacks remain the top driver of cyber incidents while the focus of attackers is also shifting to smaller or mid-sized companies, which are less resilient against cyber-attacks and data breaches. Overall, the total number of cyber claims in 2025 is expected to remain stable (around 700), with a seasonal uptick in activity expected around Black Friday at the end of November to year-end.

Several ransomware events have hit the headlines this year, but overall, we see that insured losses from these attacks have decreased in 2025 to date. Insured companies' increased detection and response capabilities are helping to stop some attacks at an early stage. Every step an attacker progresses, and every minute that they are in the system, the impact goes up exponentially. The cost of a ransomware attack that progresses to data theft and encryption can be 1,000 times higher than an incident that is detected and contained early.

Ransomware remains biggest driver

Ransomware attacks accounted for around 60% of the value of large claims during the first half of 2025. High-profile incidents across many industries underscore continuing threats, although there are signs of international co-ordination by law enforcement agencies and the strengthening of cyber security by large corporates is having a positive impact. Attackers are also shifting focus to smaller firms, which are typically less resilient than multinationals, as well as firms in other territories, such as Asia. Ransomware was involved in 88% of data breaches at small and medium firms compared with 39% at large firms, according to Verizon.

As large companies have improved their response capabilities, recent years have seen a shift from purely extortion-based ransomware attacks to double extortion, including data exfiltration – 40% of the value of large cyber claims during the first half of 2025 included data theft, up from 25% in all of 2024. Losses involving data exfiltration were more than double the value of those without. The average global data breach cost hit a record high at almost $5 million in 2024, according to IBM, driven by factors such as the impact of stricter data privacy regulation.

The retail sector has been particularly vulnerable to cyber incidents, entering the top three of most affected industries, according to analysis of large cyber claims over the past five years, accounting for 9% of claims by value, after manufacturing (33%) and professional services firms (18%). Retailers often have high revenues, handle large volumes of personal data, and are vulnerable to business interruption, which all provide leverage when making extortion demands. Large numbers of staff, suppliers and IT systems create a wide attack surface.

Meanwhile, an expanding risk landscape is also broadening the potential scope of losses for companies, with non-attack incidents, such as wrongful collection and processing of data, as well as technical failure, accounting for a record 28% of large claims by value during 2024. At the same time, organizations continue to face new challenges and threats from their growing reliance on digital supply chains, the impact of expanding privacy regulation, and the increasing number of social engineering attacks involving sophisticated impersonations of company staff to gain access to company systems.

Resilience gap between uninsured and insured continues to widen

In Germany, insurance industry figures show that the loss impact of cyber insureds increased by around 70% over four years, compared with a 250% increase in the economic impact of cybercrime. This resilience gap of more than 3:1 reflects cyber insurance policyholders' heightened awareness of risk and their actions to mitigate it, many of which are a condition of obtaining insurance. It also reflects the effectiveness of risk prevention services and incident response assistance provided by insurers. Minimizing business interruption, which accounts for over 50% of cyber claim values, remains a key objective, as business continuity planning will significantly reduce costs for companies and insurers.

To read the full report, please visit: Cyber security resilience 2025 | Allianz Commercial.

Insurance carriers are navigating one of the most complex operating environments in recent memory. Economic pressures, rising claim costs, and evolving policyholder expectations are all converging, creating new demands on claims organizations.

The question is no longer whether volatility will disrupt claim severity, expenses, and growth. The more important question is how carriers can adapt with consistency, protect profitability, and preserve the trust of their customers when uncertainty becomes the norm.

Resilience is not about eliminating volatility. It is about building the agility to withstand it, protect profitability, and maintain trust with policyholders.

Three qualities—speed, transparency, and technology—stand out as defining what resilience looks like for carriers today.

Why Speed Matters

Timing is critical in claims management. Carriers that can evaluate a claim within 48 hours gain an immediate advantage: early clarity on exposure, settlement potential, and cost containment. That window can be a critical factor in whether a claim is resolved efficiently or spirals into prolonged disputes and mounting losses.

When claims drag out, risks can multiply. Medical conditions can worsen, attorneys can enter the picture, and cases can escalate into multimillion-dollar nuclear verdicts -- a trend that has become more common in recent years. By contrast, prompt action contains costs, reduces uncertainty, and demonstrates competence to policyholders.

Speed is not just an operational advantage; it is a strategic imperative. In periods of economic strain, when claim volumes often spike, the carriers that respond quickly are the ones that preserve financial stability.

Transparency Builds Trust and Loyalty

Speed alone rarely delivers its full value without visibility. A fast, transparent process signals to policyholders that their needs are being prioritized. This builds trust at precisely the moment customers are most vulnerable.

For example, a claimant who receives acknowledgment within 24 hours and benefits within a month is far more likely to remain loyal. That loyalty matters: Retaining existing customers costs significantly less than acquiring new ones, especially in today's competitive markets.

Transparency also minimizes disputes. By keeping communication open and expectations clear, carriers reduce the likelihood of misunderstandings that can escalate into costly litigation. In this sense, transparency is both a customer-experience priority and a financial safeguard.

Operational Risks Carriers Can't Ignore

While speed and transparency define resilience, many carriers face structural barriers that prevent them from executing consistently.

Amid many challenges, talent shortages, rising workloads, and compliance risks stand out as the most pressing.

Many seasoned adjusters are retiring, taking with them decades of institutional knowledge — instincts, judgment, and client rapport that cannot be replicated overnight. Newer hires, while eager, often lack the experience to navigate complex claims or identify early warning signs.

At the same time, workloads are intensifying. It is not uncommon for adjusters to manage hundreds of simultaneous cases. Without modern systems and well-integrated vendor support, that volume becomes increasingly difficult to manage. Errors multiply, documentation is missed, and compliance risks escalate.

Complex regulatory requirements also demand accurate, timely reporting. Gaps in documentation or oversight can quickly escalate into penalties and reputational harm.

These challenges underline a simple truth: Resilience requires investment in infrastructure that equips adjusters to manage high volumes without sacrificing accuracy or service quality.

Technology as the Enabler

Technology is quickly becoming the foundation of modern resilience. Advances in artificial intelligence (AI), predictive modeling, and digital record exchanges are transforming how carriers approach claims and shifting the process from reactive to proactive.

AI and advanced analytics are evolving the claims process by automating routine tasks such as data entry, document review, and analysis. These capabilities reduce human error, accelerate processing times, and provide fairer, more consistent outcomes for policyholders.

Predictive modeling allows insurers to analyze historical data and spot risks early. Fraudulent patterns, high-cost medical providers, or claims likely to escalate can be flagged before they cause significant losses. This proactive approach protects financial resources and strengthens customer confidence.

Digital record exchanges eliminate the inefficiencies of manual documentation, enabling faster and more secure sharing of critical information. Integrated into claims systems, these platforms also support real-time fraud detection, ensuring that no key details are overlooked.

Together, these tools allow carriers to scale operations up or down without compromising accuracy, compliance, or customer service. They also empower adjusters to focus on high-value decision-making rather than repetitive tasks, multiplying both efficiency and employee satisfaction.

Lessons From History With Strategies for the Future

Market volatility is not new. History shows that surges in commodity prices, catastrophic natural events, and regulatory shifts have long reshaped insurance economics. What has changed is the speed and complexity of today's environment.

Tariffs can increase claim costs almost overnight, and customer expectations for transparency and speed have never been higher.

Resilience is not built on size or history alone. It is defined by how quickly carriers act, how clearly they communicate, and how effectively they use technology to manage risk.

The challenge is shifting from whether to modernize to how to implement it responsibly and effectively. Those who act decisively will be better positioned to mitigate risks, contain costs, and differentiate themselves in a crowded market.

The insurance industry is undergoing a pivotal transformation, yet true AI autonomy remains out of reach. While a few leaders have successfully scaled AI, most insurers are still constrained by legacy infrastructure, regulatory caution and immature governance frameworks. Even among advanced adopters, fully delegating operations to autonomous AI agents is not yet feasible.

This paper explores two strategic paths forward: the long-term pursuit of artificial general intelligence (AGI) and the immediate application of reinforcement learning (RL). We introduce a novel reinforcement-switch framework which combines continuous learning with proactive human-AI control transfers to enable accountable autonomy. This model ensures resilience in dynamic environments by embedding trust, reversibility, and oversight into AI operations. It represents a fail-forward approach to engineering safe, scalable autonomy in insurance.

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Sponsored by Cognizant & Venbrook

About the Authors

Dr. Venkatesh Upadrista leads global transformation for the BFSI-IOA vertical at Cognizant. In this role, he is responsible for driving AI-led transformation across the unit, ensuring customer success and enhancing the delivery of modern business operations within the financial services and insurance sectors.

Justin Slaten is Chief Information Officer at Venbrook Group, LLC. He is an accomplished technology executive with over 25 years of management experience. He is recognized for his innovative approach to product development, business process improvement, and scaling teams and operations. His leadership consistently drives enhanced productivity and sustainable growth.

We were recently having a discussion with insurance industry peers, bemoaning a vendor announcement of significantly lower pricing for a popular service in a fiercely competitive segment of the collision repair industry. As we thought about it more broadly, we agreed that the announcement was just the latest in a long series of strategic decisions that reflect the serious threat of a problem we call a "race to the bottom."

The term "race to the bottom" describes a competitive situation where companies lower prices to gain an advantage. The origins of this process are often thought to be a result of globalization and government attempts to bolster their economies. Race to the bottom tactics most often spark a destructive cycle in which the ultimate outcome is negative for all involved.

The property/casualty insurance ecosystem is one of the largest U.S. economic segments and touches hundreds of millions of consumers and businesses. It is a victim of this race to the bottom in many serious ways – some well-recognized but even more that are less visible and rarely discussed candidly. The recent roller coaster cycles of steep rate increases, underwriting losses now turning into gains and corresponding efforts to contain operating costs also have downstream implications to scores of industry service and solution providers.

Property & Casualty Insurance Ecosystem

Prior to 2021, both auto and homeowner insurance lines experienced this very competitive scenario. Advertising budgets swelled, with leading carriers like Progressive spending at $1.95 billion and GEICO at $1.13 billion in 2020. "Switch and save" marketing was everywhere with telematics, usage-based-insurance offers to "bundle and save" luring price-sensitive consumers.

More glaring and lasting was the ramp up to today's runaway homeowner insurance premiums. Weather exposure and inflation attracted much-deserved attention, obscuring what was developing in plain sight during the same period. Insurance to value (ITV) was rapidly eroding as housing prices shot up, followed by steep repair/rebuild costs from 2021 to the present. Rates were simply not keeping pace and were already well behind due to years of suppressed rates: California's Prop 103 contained rates, only for there to be a massive reckoning following the Los Angeles wildfires.

Combined, this has been one of the most disruptive insurance cycles in decades, with highly competitive pressures serving as the "race to bottom" catalyst.

CATALYSTS AND IMPLICATIONS

Private equity and consolidation: The services side of the property/casualty insurance industry – particularly personal insurance claims services – was historically as fragmented a marketplace as any, characterized by many thousands of smaller family-owned businesses. These markets (collision repair, auto salvage, independent appraisers and adjusters, repair and home renovation contractors, software providers and numerous others) were ripe for consolidation, a process that can yield large gains over time but requires immense amounts of investment capital and expertise. Many large private equity funds with mountains of "dry powder" (free cash) entered these spaces, which has triggered many aspects of this race to the bottom. And this is still unfolding.

Reduced profitability: Lower premiums may constrain carriers' ability to adequately cover claims and other operational costs and, in the worst cases, lead to insolvency.

Lower underwriting standards: Less stringent underwriting and risk assessment standards can lead to adverse selection, leaving carriers overweight with high-risk policies and further threatening profitability.

Brand and reputational damage: If carriers prioritize low costs over customer experience, brand loyalty declines. A reputation for poor claims service or missing coverage can make it difficult to attract and retain profitable customers, even at lower prices.

Innovation stagnation: A focus on price competition can stifle innovation in products and services in the name of cost savings and perpetuates product and service commoditization.

The Ecosystem

Effect on ecosystem partners: The majority of insurer operating and claim costs are external, and as carriers seek to contain and reduce these costs, they tend to impose their leverage on their business partners throughout the ecosystem.

Pressure on compensation and commissions: The race to the bottom can affect how insurers compensate independent agents and brokers. This can pressure reps to recommend the cheapest policy, even if it is not the best fit for the customer's needs.

Disruption of the traditional role of agents: The shift toward lower cost digital-first information models threatens the traditional agent's role as a trusted advisor, potentially leading to their replacement by apps or automated systems.

Claim costs: Pressure to reduce loss adjustment expenses (LAE) and cutting full-time equivalent (FTE) labor has been continuing. The corresponding effect (but hard to quantify with precision) on loss costs is now catching up, and job cuts are turning into job replacements and additions of adjusters. After a decade of hammering LAE downward, containing indemnity payouts is now center stage.

The Customers

Protection gaps: Customers may be lured by low premiums without understanding that their policy offers less coverage than they need. This leaves them exposed and vulnerable in the event of a large claim.

Higher out-of-pocket costs: When claims are denied or coverage is insufficient, customers often face unexpectedly high out-of-pocket expenses. This is particularly evident in healthcare, where medical debt is a major issue.

Poor claims experience: To cut costs, some insurers may invest less in their claims departments, leading to protracted, difficult, and adversarial claims processes. This can cause significant stress for customers during an already difficult time.

Eroding trust: The focus on aggressive pricing and cost-cutting undermines customer trust in the insurance industry as a whole. Many customers already view insurance as a necessary evil, and a negative experience can reinforce this sentiment.

Counterarguments and Debate

Not everyone agrees that a race to the bottom is an inevitable consequence of competition. Some economists and commentators argue that it is a myth, especially in the context of globalization. A 2023 article from the Cato Institute argues that global trade has generally led to improved working conditions and higher wages, with most foreign direct investment still flowing to relatively wealthy countries.

But proponents of free trade suggest that companies and investors are ultimately more interested in a skilled workforce and stable institutions, which are more common in countries with higher standards.

However, other research has found empirical evidence supporting the theory, particularly regarding the effects of globalization on labor standards. This divergence of evidence means the debate over the existence and effect of a "race to the bottom" is continuing.

Avoid the Bottom

In the property/casualty industry, these conditions may be dismissed as underwriting cycles as famously illustrated in Paul Ingrey's "Insurance Clock." (See, Insurance Industry's Mixed Signals.) On the one hand, you have new, high premiums, returning profits and growth appetite on the rise. On the other hand are uncertain and fluid economic conditions, weather exposure and threats from legal abuse trends signaling caution. Which will prevail is the question du jour, and has the new race to the bottom already begun?

We believe that this race to the bottom is avoidable even while mitigating its effect on earnings. Scale clearly affords efficiencies, but managing how to wield this leverage without affecting the ultimate quality of outcomes is paramount. Price competition is a positive market dynamic that benefits customers, but irrational and cannibalizing pricing serves nobody. Keeping safety and consumer satisfaction at the top of our strategic priorities is paramount and if not implemented voluntarily will only invite unwanted outside regulation.

As always, common sense is mandatory as we move forward.

AI Disclosure: AI tools were used for some content generation in conjunction with our own subject matter expertise. The authors take full responsibility for the accuracy of the final content.

At a time when highway deaths in the U.S. are finally in a sustained decline again, after a surge related to COVID recklessness and distracted driving, car makers seem to be set on a dangerous path. Wanting to show off their AI chops, they are rolling out and emphasizing autonomy-lite capabilities that are convenient in the best of all possible worlds but that could well increase accidents and fatalities.

Basically, many car companies seem to be heading in the Tesla direction, bragging about their cars' autonomous capabilities while telling drivers they need to have their hands on the wheel and be constantly alert — a combination that just doesn't work. Once you tell people their cars can do the driving, they let their cars do the driving, and the sort of autonomy that exists this side of Waymo and a few other providers of fully autonomous vehicles simply isn't reliable enough yet. 

I've become what's called a 410-er, and I think insurers should be, too.

410 — unrelated to 420, a coinage celebrating cannabis that seems to amuse Elon Musk — refers to three of the six levels of autonomy: levels 4, 1, and 0, which I believe are the safe levels at the moment. Car makers, meanwhile, are focusing on providing levels 2 and 3 to the mass market, levels that I believe are fraught with danger. 

Level 0, as you can imagine, involves no autonomy. Level 1 is referred to as driver-assist — your car keeps you in the center of the lane if you're starting to drift and lets your cruise control maintain a safe distance from cars around you. Both provide little enough assistance that the driver stays fully engaged.

Level 4 is fully autonomous within a certain area and under certain conditions. As long as you stay within, say, a well-mapped area in good weather in daytime, you could be asleep in the back of the car. This level is safe, too — once the technology is proven, of course.

Combine those three levels, all of them helpful without overpromising to the driver, and you get to 410. You could also add level 5, which is fully autonomous anywhere at any time, but we're not there yet, even with Waymo, et al. I think we'll get to a 5410 paradigm, just not yet.

The problem is that car companies want to brag about levels 2 and 3, which can lull drivers into danger. Level 3 is especially seductive, because it promises "conditional driving automation": The system handles all aspects of driving under certain conditions, but the driver must be available and able to take over.

Google, Waymo's parent, tried a Level 3 approach years ago and quickly gave up. The technology was so good that Google employees who volunteered as test subjects soon zoned out and started checking their phones, playing video games or whatever — but the car wasn't totally reliable. And when the car told the driver to reengage, many seconds passed before they could stop whatever they were doing, size up the issue and act. When you're traveling at 75mph, 10 seconds equals almost 400 yards of distance traveled while the driver is taking control of a situation. 

Tesla is finding out how dangerous Level 3 autonomy can be. It's faced numerous lawsuits over fatal accidents that occurred while drivers had engaged what Tesla calls Full Self-Driving (FSD) but which is really a Level 3 system. Tesla has mostly escaped liability by arguing that it warned drivers repeatedly that they were responsible for the car's actions, but it did recently lose a $240 million wrongful death judgment that should serve as a warning to Tesla, to other auto makers offering Level 3 systems and to auto insurers.

Tesla, seemingly unchastened, recently told its car owners that if they felt drowsy they should engage FSD — even though a drowsy driver would take even longer to reengage if told to by the AI driving the car. Other car companies are promising what are generally referred to as Level 2+ systems, and Telemetry says more than half of new cars will be equipped with such "hands free" systems by 2028. 

Car companies will surely promote these systems. Everybody wants to be seen as being at the cutting-edge of technology, and AI can be a real selling point these days. Autonomy is exciting.

But I hope cooler heads will prevail. 

"Customers really love these hands-free systems, especially on longer drives, but God is in the details, and... the worst of these systems may result in preventable injury or worse," Telemetry says.

Traffic deaths in the U.S. had been declining steadily for decades, reaching a low of roughly 35,400 in 2014, but increased steadily as smartphones tempted drivers with distractions and then surged as COVID somehow made drivers more reckless. Deaths peaked at approximately 47,000 in 2021. They dropped slowly from there, falling back to 44,700 last year, and declined a gratifying 8.2% in the first half of this year. 

Let's keep the progress going and push back on efforts to promote Level 2+ autonomy as anything more than an occasional convenience. Level 2+ and Level 3 are wildly impressive technology — impressive enough to be truly dangerous.

410, 410, 410....

Cheers,

Paul

P.S. What happens when a driverless car commits a traffic violation? Who gets the ticket?

That was the issue facing police officers when a Waymo vehicle made an illegal U-turn right in front of them in San Bruno, CA, just south of San Francisco, a week ago. They pulled the car over — Waymo cars pull off the side of the road when a police car turns on its emergency lights — but when they approached the car they found... no one in it.  

Under California law, tickets can't be issued to driverless vehicles until next summer — “Our citation books don’t have a box for "robot,” the San Bruno Police Department noted. Even once tickets can be issued, they carry no penalty. 

Expect the law to change — and expect further oddities as autonomous vehicles become more common. 

At the core of the trust formula, the reliability, honesty, and competence of insurance planners are crucial for building trust. Yet how insurance companies can quantify these factors objectively remains a major challenge. By establishing trust scores and trust levels, improvements in customer relations, sales performance, satisfaction, and competitiveness can be expected.

The purpose of this article is to present the basic reasoning and exploration of “making trust AI-driven.” In a human–machine collaboration environment, this provide support for humans; in a machine-based environment, it serves as guideline and regulation for AI. This is my fascinating learning journey together with GPT-4 — a process that moved from nothing to something, through initial exploration, iterative dialogue, and joint construction, transforming abstract concepts into digital form and refining a thinking framework for solving complex problems. Although the conclusions here have reference value, I suggest readers focus more on the process: to imagine innovative solutions, and to consider how to collaborate with artificial intelligence by building new thinking frameworks to solve their own problems.

What is trust? It is a highly abstract concept, yet so close to our daily lives. Why do we need trust? No one would deny that all transactions are built on trust, especially in insurance. This is not only because insurance is an intangible product, but also because its transactions involve a time gap — unlike “cash on delivery.” Therefore, trust is particularly important for insurance.

Insurance relies on “sales,” and primarily on “face-to-face sales.” The current insurance market is still dominated by company agents. Even in multi-channel sales, apart from pure online sales and telemarketing, bancassurance and broker channels are also based mainly on “people + face-to-face interaction.” Today’s difficulties and transformation challenges in the insurance industry may have their stage-specific inevitabilities, but technological trends still bring a glimmer of hope. Personally, I believe the primary problem in the insurance industry today comes from the sales side, with lack of trust being the core issue. Under the same thinking framework and system structure, merely relying on technological empowerment to rebuild consumer trust in agents and insurance companies is, frankly speaking, difficult. In the past we have shared many articles discussing the importance of building trust. Especially with the advent of the AI era, when human–machine collaboration has become a trend and machines are gradually replacing humans in many areas, how should we face such a scenario? How should we “effectively manage” trust? This has always been a question on my mind.

Therefore, how to make the abstract concept of trust more concrete — not limited to conceptual analysis and written description, but able to be digitalized, structured, and even AI-driven — is the focus of this article. Using insurance planning as the scenario, the views on trust and the final constructed “trust framework” in this text were developed through strategic, dialogue-based prompting with OpenAI’s GPT-4, combined with existing theories and logical reasoning. Although these views and results are based on current research and theory, their combination and application in different scenarios still carry originality and innovation. I encourage readers to think critically about these viewpoints, and to consider how they might apply to more diverse real-world situations. Furthermore, this highly experimental attempt does not fully cover all relevant knowledge and needs and still requires further exploration and validation by professionals.

This article includes two types of visual inserts to distinguish between human prompts and AI responses:

• Gray background with black text represents the prompts or instructions I provided to the AI.
• Blue background with blue text represents the AI’s responses.

These visual cues are designed to help readers follow the flow of interaction and better understand how the trust framework was co-developed through iterative dialogue.

To support this exploration, the article includes 12 visual inserts that document the process of building a trust framework through iterative dialogue with AI. These visuals are not standalone illustrations—they form a clear and progressive logic path for understanding how trust can be made AI-driven. From conceptual exploration to quantification, from system design to scenario-based application, each figure contributes to a modular and adaptable trust framework. Together, they demonstrate how AI can not only execute trust logic, but also participate in shaping, adjusting, and contextualizing it across different environments.

Below is an overview of the entire process of “building the trust framework.”

Figure 1. A Nine-Step Framework for Building Trust in AI Systems

As illustrated in Figure 1, the nine steps outline the process of constructing the entire trust framework.

I. Iterative Exploration Process

1.1 From vague to concrete

For a long time, our understanding of the question “How can trust be made AI-driven?” was vague, let alone having a concrete answer — and even today, the results cannot truly be called a final “answer.” Our knowledge of trust came only from theories and concepts found in books, along with subjective impressions shaped by personal life experiences. There was no consistency in understanding, much less any generalization. Because trust is deeply entangled with human relationships and subjective perceptions, we once assumed that digitizing trust was an impossible task. Unknowingly, we even “assumed” viewpoints such as: trust can only be described in words; trust must be built through process and details; trust cannot be quantified objectively.

When I came across Helen Keller’s description of her understanding of colors (Note 1), my rigid thinking was jolted. Later one day, I accidentally saw a written expression of “beauty” and “art” (see prompt illustration below). Suddenly, inspiration struck — could we try transferring the problem, and “use beauty to map the presentation of trust”? Perhaps this could allow AI to concretize the difficult and abstract concept of trust. Once it is concretized, subsequent digitalization and structuring would be relatively easier.

As my dialogue with GPT-4 continued, the questions and answers gradually became more concrete and in-depth. It was an iterative process. Each round of dialogue built on the previous one, where human and machine together clarified and refined the problem, jointly exploring and constructing an understanding of the concept of “trust.” Views emerged, expanded, and improved in the process.

Looking back now, many prompting strategies introduced in the past — such as Chain of Thought (COT), OPRO, knowledge generation, step-back prompting, etc. — together with advancements in AI capability, such as emotional intelligence, all transformed seemingly impossible barriers into goals that could be achieved.

1.2 Starting Point: Building a Thinking Framework

To obtain a concrete result for a virtual concept (such as trust), the process begins with abstraction, and abstraction starts from extracting the key information of the virtual concept.

This entire exploration journey began when I entered the following prompt of “from beauty to trust” (Note 2):

Figure 2. Prompt for Using the Concept of Beauty to Explore and Define Trust

After receiving the above question, based on the way of thinking I provided, GPT-4 demonstrated impressive reasoning ability in concretizing the concept of “trust.” Its process of “making trust concrete” was as follows:

Figure 3. Concretizing Trust Through Definition, Metaphor, and Social Relevance

From the above response, some key pieces of information already emerged, mentioning many possible keywords for trust elements, such as reliability, honesty, competence, time, interaction, emotion, expectation, dependency, scenario, etc. Although still not clear enough, I felt — it seemed I had asked the right question!

Next, two things needed to be done with these keywords: condense them again to abstract form, and then reason based on abstraction. When the experiment ended, I realized these two steps were consistent with the “Step-Back Prompting” methodology I had just learned.

In handling the first step, my thought was: GPT-4 has a vast knowledge base and letting it select the most essential keywords would certainly be more objective than if I did it myself. What I needed to do was to tell it how to abstract. Thus, I gave GPT-4 the task of condensing the keywords, while my purpose for abstraction was to build a “trust thinking framework.” I needed a method that not only represented this virtual concept of trust, but also could, through reasoning, later achieve digitalization and quantification. I thought, perhaps a “formula” could solve this tricky problem.

Although I had no confidence in how GPT-4 would respond, I asked the next question, right after its previous output (noting that context continuity is very important for GPT-4): “Can trust be transformed into a formula?”

GPT-4’s response was surprising! It really output a formula. At the same time, it explained that expressing trust as a formula is highly simplified and abstract, and that trust is in fact a complex human emotion and psychological state that cannot be fully described by a formula. Instead, the formula serves as a thinking tool to help us understand and analyze the different components of trust. I understood its point, but with a formula, the second step of reasoning became possible. Below is GPT-4’s output of the “trust formula.” I personally think the elements it identified are key, theoretically supported, comprehensive, and logically consistent (Note 3). I present the “trust formula” in the following original form:

Figure 4. Translating Trust Into a Quantifiable Formula

Overcoming the first hurdle, we have the trust formula：

Trust = (Reliability × Honesty × Competence) × Time / Interaction

Among these, the most critical elements are reliability, honesty, and competence, while time and interaction act as positive or negative forces influencing trust.

1.3 Process: Reasoning to Find the Answer

Having only a trust formula could not solve the problem; we also needed a method to “quantify trust.” To attempt digitalization, GPT-4 suggested a simplified method: assigning a quantitative value to each factor in the formula.

With this quantification method to overcome the barrier of digitalization, we need a “password,” which would not only indicate subjective trust levels but could also evaluate the value of trust, including reviewing our coming action plans and building a user trust system.

Inspired by Ant Group’s “Sesame Credit Score,” I temporarily called this password “Trust Score” (Note 4). Returning to the example above, the calculated trust score was 840.

II. Building a User Trust System

2.1 Final Confirmation and Key Prompts for Building a User Trust System

My next question was: “If we want to form a Trust Score based on the trust formula, and each user is an independent individual, how do I know my Trust Score in the user’s mind? And can this Trust Score be objective and accurate enough to serve as an indicator for continuous improvement in the future?”

This question was important because I was clarifying to GPT-4 who was evaluating whom, in preparation for applying it to the insurance planning scenario. And because in real business contexts, we need a quantified understanding of the trust level users place in us. These must be objective and accurate enough to provide a reliable foundation for guiding business strategies and improvement plans.

Since the application scenario has not yet been clearly defined, GPT-4’s response lacks specificity. However, its comprehensiveness and completeness still offer valuable reference points. Regarding the “objectivity of Trust Scores,” here is the simplified version of its response:

Figure 6. Operational Framework for Evaluating and Strengthening User Trust

At this point, we had roughly mastered abstraction of the virtual concept of trust (through transfer and mapping), the method for extracting key information (the trust formula), and the feasibility of objectively quantifying trust (the Trust Score). Next, with the goal of “completing the understanding of user needs, matching appropriate insurance products, and completing transactions,” I described a specific scenario to GPT-4 and proposed three action requirements: “Building a user trust system,” “Trust Score grading and standard operating procedures,” and “Operational guidelines for obtaining the Trust Score.” Please see the following "Three Action Prompts for Building User Trust":

Figure 7. Prompt for Constructing a User Trust System in Insurance Planning

2.2 Three Actions for Building a User Trust System

Below is my summary of GPT-4’s “Three Actions for Building a User Trust System,” omitting details, as reference for constructing a trust system and managing trust. Readers are welcome to try any methods mentioned here or copy the prompts and adjust them for their own scenarios.

Figure 8. AI-Generated Framework for Building a User Trust System

Action 1: Build a User Trust System.

• Reliability and honesty: evaluated through self-assessment, customer feedback, or direct communication with customers to understand perceptions and satisfaction.
• Competence: improved by regular training and assessments.
• Interaction: record not only frequency but also quality and effectiveness.
• Time: emphasize the importance of building long-term relationships.
• Trust Score: calculate periodically (e.g., monthly or quarterly) to monitor changes in trust levels. Based on results, identify areas needing improvement and set strategies accordingly.

Action 2: Trust Score Levels.

• Different levels correspond to different actions and goals.
• Example: Level 1 (0–200 points) — Low trust.
  • Recommended actions: strengthen basic communication skills, improve service transparency, ensure accuracy of information.
  • Goal: build basic trust and resolve misunderstandings.

Action 2 (continued): Standard Operating Procedures.

• Regular evaluation: assess each customer’s Trust Score to determine their level.
• Personalized strategy: design and implement service strategies based on trust levels.
• Continuous tracking: track effectiveness and adjust as needed.

Action 3: Guidelines for Obtaining the Trust Score.

• Positive interactions: ensure each interaction is constructive, focused on customer needs, providing professional advice and solutions.
• Avoid negative interactions: prevent delays, inaccurate information, or unsuitable suggestions.
• Post-service updates: collect feedback through surveys, especially on reliability, honesty, and competence.
• Regular updates: update scores based on feedback and interactions.
• Implementation:
  • Employee training on how to build and maintain trust in every interaction.
  • Monitoring quality of interactions and adjusting based on customer feedback.

III. Ensuring Objectivity of Subjective Evaluation and Notes on AI-Driven Implementation

To fully convey the integrity, transparency, and fairness of the methodological validation process to readers, this chapter presents excerpts of GPT-4's responses.

3.1 How to Ensure Objectivity of Subjective Evaluation

When considering the Trust Score and evaluation methods, readers may already notice that some approaches may carry subjective bias. Therefore, regarding “how to ensure the objectivity of subjective evaluation,” below is an excerpt from GPT-4’s response:

Figure 9. Strategies for Enhancing Objectivity in Subjective Trust Evaluations

We already know AI systems have advantages in handling data and ensuring consistency, but they have limitations in building human emotional trust and understanding complex interpersonal interactions. Therefore, if we want to apply the trust system to AI planners, some key adjustments and special considerations are required.

3.2 Notes on AI-Driven Implementation

To untie the AI knot, we must ask who tied it. GPT-4 suggests that when applying a trust model to AI planners, several key dimensions must be considered: 

• Demonstrating competence.
• Transparency and honesty (helping users understand how AI generates suggestions).
• Adaptability and personalization (learning and adapting based on user history and feedback).
• Context awareness (AI must understand and adapt to different user contexts and needs).

By integrating these theoretical elements, we can create a more complete and in-depth trust framework that applies not only to human service providers but also to AI systems. The following excerpts on “AI-Driven Trust” are drawn directly from GPT-4’s responses:

Figure 10. Foundational Components for Building Trust in AI Systems

In addition, GPT-4 offered further suggestions: continuous user education, enhancing user participation (channels for feedback and suggestions), use of social proof (customer recommendations and positive experiences), emphasizing personalized services, use of technology (CRM and data analysis tools), transparent communication (sharing both positive and negative feedback), flexibility and adaptability (customized services and solutions), risk management and compliance (clearly explaining risks and uncertainties), and continuous evaluation and improvement (keeping track of industry trends).

So far, our “trust framework” is roughly complete. It is built on existing theoretical reasoning and logical deduction, with strong internal consistency and theoretical foundation. This framework integrates multiple key factors in building trust, such as reliability, honesty, competence, interaction, and time — all widely recognized in trust theories and research.

IV. Other Suggestions

The above trust framework was developed in the scenario of insurance planning. But whether the framework can stand the test of generalization is still a concern. Every industry and scenario have its own unique characteristics and needs, which may affect how trust is built and maintained. Therefore, while we can provide a general framework, it may need to be adjusted and customized for specific situations.

For example, in healthcare, trust relies more on professional knowledge and privacy protection; in retail, it depends more on product quality and customer service. This is why an effective trust framework must have adaptability and flexibility to fit different environments. Industry knowledge, culture, and expectations are also indispensable in building trust frameworks.

4.1 Other Insurance Scenarios

Different insurance scenarios require different trust-building strategies. For example, in claims and underwriting, GPT-4 developed adjusted formulas to fit the unique needs of these contexts. Transparency, fairness, expertise, and effective communication remain essential elements of trust.

Figure 11. Scenario-Based Adjustments to the Trust Formula in Insurance

Different scenarios in the insurance industry may require different trust-building strategies, but transparency, fairness, expertise and effective communication are always key elements in building trust.

4.2 Other Industry Scenarios

To adapt to different industries, GPT-4 also created examples for healthcare and retail. In healthcare, trust is more tied to expertise, patient care, and communication transparency. In retail, trust emphasizes product quality, service, and price transparency.

Figure 12. Industry-Specific Trust Formulas for Healthcare and Retail

Beyond insurance, healthcare, and retail, other industries such as education, financial services, technology, and online services — as well as areas like corporate management and customer relationship management — can apply similar methods. By adjusting elements, weights, and sequence, each industry can find the most suitable trust formula. For some industries, honesty and transparency may matter more; for others, professional competence and long-term service quality may be emphasized. By understanding industry-specific needs, these factors can be effectively adapted for better trust-building.

V. Conclusion

By constructing a trust system through self-assessment, customer feedback, or direct communication, planners can enhance their reliability and honesty. Companies can use customer Trust Scores and levels to design standardized procedures and personalized strategies, driving decisions with data. Whether as a sales management tool or a way to improve satisfaction and loyalty, this should be helpful. Therefore, building a trust system can not only improve customer relationships and sales performance but also bring long-term benefits to companies — including brand building, competitiveness, employee and customer satisfaction.

In this article, we explored the application of the trust concept in insurance through AI. We discussed methods for building a user trust system, emphasized the importance of ensuring objectivity in subjective evaluations, and highlighted factors to consider when integrating AI. We also explained how to transform trust from an abstract concept into concrete, digitalized practices, and how to apply these principles in different contexts. The key elements of trust include reliability, honesty, competence, interaction, and time.

A major challenge is the objectivity of reliability, honesty, and competence. Although we provided a basic formula, real-world application requires flexibility to fit different situations. Trust-building is a dynamic process that demands ongoing effort and maintenance. This means regular evaluation and adjustment to reflect industry trends and changing customer needs. From examples in different industries, we can see that trust has both universality and specificity. Thus, the trust framework should be seen as a flexible, adaptive tool, not a rigid rule.

In this exploration journey, GPT-4, trained on massive text data, displayed strong contextual understanding, creative thinking, logical reasoning, and interactive dialogue ability — becoming an ideal partner. GPT-4 could track discussion context, adjust responses accordingly, and integrate new viewpoints throughout the process. Its creativity in handling complex problems was impressive, and its interactive dialogue proved that conversational prompting could match structured prompting in problem-solving ability.

This experiment was only a starting point and process for logical reasoning and theoretical validation. Looking ahead, AI’s role in building and maintaining trust in insurance will become increasingly important. With technological progress, we can foresee a smarter insurance ecosystem, where AI not only improves efficiency but also strengthens customer trust through precise data analysis and personalized service. In the future, AI will become humans’ best assistant, understanding customer needs more deeply, predicting market trends, and even supporting complex risk assessments and decision-making. This will change how insurance products are developed and marketed, and will redefine the relationship between insurance companies and customers.

Applying these theories to practice means insurance companies must continuously adapt to new technologies while ensuring ethical and legal compliance. In practice, companies should focus on cultivating employees’ understanding and use of AI, while strengthening communication with customers to ensure technology truly meets their needs and expectations. Companies must also take measures to protect customer data security and privacy — both are critical for maintaining and enhancing trust. Through these efforts, AI will not only be a tool but also a major driving force for making insurance more efficient, transparent, and customer-centered.

Trust is fragile, but it is also essential in all human relationships. In today’s fast-changing world, trust is not only the trigger for completing transactions but also the bond for maintaining long-term relationships. How to make AI a key part of building and maintaining trust is a question every decision-maker and manager must consider.

The insurance industry today urgently needs to find a way out of its current challenges. I hope this experiment can serve as a starting point—testing the waters and offering something that may inspire further exploration.

Further Reading

AI Reshapes Trust in the Insurance Industry. Insurance Thought Leadership (ITL). This article was also selected for promotion by the International Insurance Society, IIS.

References and Notes

1. Helen Keller’s reflections on her perception of color: “For me, there are also delicate colors. I have a palette of my own. I will try to explain what I mean: pink reminds me of a baby’s cheek, or a gentle southern breeze. Lilac was my teacher’s favorite color, and it reminds me of faces I have loved and kissed. To me, there are two kinds of red—one is the warm blood of a healthy body; the other is the red of hell and hatred. I prefer the first because it is full of vitality. Likewise, brown comes in two forms: one is the rich, friendly brown of living soil; the other is dark brown, like a worm-eaten tree trunk or a withered hand. Orange gives me a sense of joy and cheerfulness, partly because it is bright, and partly because it gets along well with many other colors. Yellow means richness to me—I think of the sun pouring down, full of life and hope. Green means vitality. The smell brought by warm sunlight reminds me of red; the smell brought by coolness reminds me of green.”
2. Source currently unverified. If any copyright concerns arise, please contact the author.
3. This formula integrates several key factors in trust-building—such as reliability, honesty, competence, interaction, and time—which are widely recognized across various trust theories and research. For example:
   1. Mayer, Davis, and Schoorman’s trust model identifies three core components: ability (skills and knowledge in a specific domain), benevolence (the belief that the trustee has good intentions), and integrity (adherence to principles and values).
   2. Rogers’ extended trust model builds on Mayer’s framework by adding contextual factors (how environment and situation affect trust) and historical interaction (how past experiences shape current trust levels).
   3. Other relevant theories include social exchange theory and psychological models of trust.
4. “Sesame Credit Score” evaluates users from the merchant’s perspective; “Trust Score” evaluates merchants from the user’s perspective. Though their starting points differ, their nature is similar.

I can remember back in 2013 when healthcare reform was passed, wellness programs were springing up everywhere claiming to reduce health insurance costs.  In theory it sounded great. Healthier employees meant lower insurance claims and showed employees the company valued them and wanted to keep them around. Many companies enrolled their employees into these programs expecting to see health insurance premiums reduced. Flash forward a few years, and many of these companies have dropped their wellness programs. Why? Because the hoped-for results didn’t happen. Instead, there was a. lot of initial interest, but engagement waned over time, and the promised results were not achieved.

Companies that dropped wellness programs discovered their programs weren’t living up to their promises because installing a program was not as easy as recruiting employees and waiting for results. It would take some work!

In today’s uncertain economy, employers are seeking ways to reduce costs, and, after payroll, employee benefits are usually a business’ highest expense. It may be the right time to revisit wellness and see if it could offer cost reductions.

For the companies that had good results, corporate wellness programs were able to offer significant financial advantages that leverage IRS-allowed reductions. Structured properly, these programs can generate a reduction in payroll tax liability while promoting healthy behaviors.

Structuring a Successful Wellness Program:

A successful wellness program is comprehensive, data-driven, and tailored to your organization’s unique needs. Here’s a structured approach based on best practices and expert recommendations:

1. Understand employee health needs, stressors, and preferences [1] [2]
2. Appoint an executive sponsor and form a cross-functional wellness committee (HR, health & safety, line managers, employee volunteers) to oversee the program [3].
3. Develop a holistic, inclusive plan that addresses multiple dimensions of wellness: physical, mental, social, financial, nutritional, environmental, spiritual, and intellectual [4] [5].
4. Set clear, measurable goals and objectives (e.g., increase participation rates, reduce stress, lower healthcare costs) [6] [7]. Establish a governance structure to manage and evaluate the program.
5. Make programming accessible and engaging, offering a mix of easily accessible activities and interventions that resonate with employees, such as fitness classes, mental health resources, health education, and flexible work options [1] [2] [5] [6]. Use a user-friendly platform for resources, progress tracking, and rewards [6].
6. Pilot, communicate, and iterate -Launch a pilot program in one department or location to test effectiveness [7]. Gather feedback through surveys and participation data; adjust programming as needed [7]. Communicate regularly
7. and clearly about program benefits, activities, and successes to maintain engagement [4] [6]
8. Measure and evolve - Monitor participation, health outcomes, and organizational metrics to assess impact[2] [4] [7].Use data to refine and expand the program, ensuring it continues to meet evolving employee needs[2] [7].

To maximize benefit usage, it is important to make sure the program is structured to keep the employee engaged. No single incentive works for all employees. A mix of monetary, benefits-based, and recognition incentives, tailored to employee preferences, is most effective.

Benefit -- Wellness Program Impact

Engagement -- 3x more likely to be engaged, higher job satisfaction, motivation and loyalty

Turnover/Burnout -- Lower turnover, less burnout, improved retention

Team Dynamics/Culture -- Better collaboration morale and inclusivity

Overview of Tax Incentives for Wellness Programs (see appendix 1)

By strategically implementing and managing wellness programs-while taking full advantage of available tax credits, deductions, and payroll tax reductions-employers may create a healthier, more engaged, and loyal workforce, all while improving their bottom line. There are three types of financial benefits for employers that create and implement wellness programs: Direct Tax Credits and Deductions, Payroll Tax Incentives, and Premium Deductions. Here is an overview of each:

Direct Tax Credits and Deductions: Recent initiatives allow businesses to claim tax reduction or deductions for investing in employee wellness. For example, some programs offer up to $650 per employee as a tax incentive, directly reducing the cost of implementing wellness benefits [9] [11]. Eligible benefits include [12]:

1. Health screenings and assessments
2. Fitness equipment and facilities
3. Preventive care initiatives
4. Mental health resources
5. Nutrition workshops and fitness challenges

Payroll Tax Incentives: Programs like the Preventative Health Initiative (PHI) enable companies to reduce payroll tax liabilities, freeing funds to reinvest in employee health and wellness without increasing out-of-pocket costs [11].

Premium Deductions: Employers can deduct the portion of health plan premiums that fund wellness programs, further lowering their taxable income [10].

Review of Tax Regulations for Employer-Sponsored Wellness Programs

This review assesses the accuracy of tax regulation statements regarding employer-sponsored wellness programs, focusing on the deductibility of expenses, tax treatment of incentives, and compliance with relevant sections of the Internal Revenue Code (IRC).

1. Tax Deductibility for Employers

• Ordinary Business Expense Deduction: Employers may deduct the costs of implementing and maintaining wellness programs as ordinary and necessary business expenses. This includes expenses such as health screenings, fitness equipment, wellness coordinators’ salaries, and educational materials [16] [15].
• Premium Deductions: Employers can deduct the portion of health plan premiums that fund wellness programs, further reducing taxable income [15] [16].
• Payroll Tax Incentives:  Some programs, like the Preventative Health Initiative (PHI), allow employers to reduce payroll tax liabilities, making wellness investments more cost-effective [13].

2. Tax Treatment of Wellness Incentives for Employees

• Tax-Free Benefits: Certain wellness benefits can be provided tax-free to employees if they qualify as medical care under IRC Sections 105 and 106. These include:
• On-site fitness facilities.
• Health screenings and preventive care.
• Employer contributions to HSAs, FSAs, or HRAs (if compliant with plan rules and nondiscrimination requirements)[16] [15].
• Taxable Benefits:** Cash incentives, gift cards, gym membership reimbursements (unless prescribed for a diagnosed disease), and other non-medical rewards are generally considered taxable income to employees. These are subject to payroll and income tax withholding unless they qualify as a “de minimis” fringe benefit or meet specific medical care criteria [15] [16].
• Wellness Incentives and Double Dipping: If employees pay wellness premiums via pre-tax salary reductions and later receive reimbursements for those premiums, the reimbursement is generally included in gross income, as this constitutes a “double-dipping” arrangement [15].

3. Individual Taxpayer Deductions (Section 213)

• Medical Expense Deduction: Individuals may deduct unreimbursed medical expenses exceeding 7.5% of adjusted gross income (AGI). Qualified expenses must be for the diagnosis, cure, mitigation, treatment, or prevention of disease, or for affecting any structure or function of the body [17] [18] [19] [13].
• General Wellness Exclusion: Expenses that are merely beneficial to general health (e.g., general gym memberships, nutrition counseling not tied to a diagnosed disease) are not deductible. Only those prescribed for a specific medical condition qualify [17] [18] [19] [13].
• Examples of Deductible Expenses: Physical exams, smoking cessation programs, prescribed nutritional counseling, and gym memberships if prescribed for a diagnosed disease [18].

4. Section 105 and Section 125 Plans

• Section 105 (HRAs): Allows employers to reimburse employees for medical expenses, including premiums, on a tax-free basis. This applies to a wide range of medical, dental, and vision expenses [13].
• Section 125 (Cafeteria Plans): Enables employees to pay for qualified benefits (including wellness-related health insurance premiums and FSAs) with pre-tax dollars. Plans must be nondiscriminatory and properly documented [13].

Key points from IRS guidance:

• Wellness Incentive Payments Are Taxable: If a wellness program provides cash or cash-equivalent benefits (such as gift cards or direct payments) to employees for participating in wellness activities, and those payments are not strictly reimbursements for qualified medical expenses, the IRS considers those benefits taxable wages. Employers must withhold federal income and employment taxes (FICA, FUTA) on these amounts [20] [21] [23] [24]
• No Double Tax Advantage: The IRS has specifically cautioned against programs that attempt to provide both pre-tax premium payments and tax-free benefit payments under fixed indemnity or similar plans, as this would constitute “double dipping.” If premiums are paid pre-tax and benefits are not strictly for unreimbursed medical expenses, the benefits are taxable [20] [24].
• Qualified Medical Expenses Only: Only wellness programs that reimburse employees for actual medical care expenses (as defined by IRC Section 213(d)) can provide tax-free benefits. Payments for general wellness, nutrition, or personal health expenses that do not qualify under Section 213(d) are not excludable from income.  Neither do gym and fitness equipment. [21].

Appendix 1

Appendix 2

Appendix 3

Wellness Platforms

Note: Suitability depends on an organization's specific needs, such as the importance of customization, holistic wellness, ease of use, or integration with existing systems

Appendix 4

Pros and Cons of Wellness Incentives

Appendix 5

The IRS has ruled the following expenses qualify for tax-free reimbursement:

• Acupuncture (excluding remedies and treatments prescribed by acupuncturist)
• Alcoholism treatment
• Ambulance
• Artificial limbs/teeth
• Chiropractors
• Christian Science practitioner’s fees
• Contact lenses and solutions
• Co-payments
• Costs for physical or mental illness confinement
• Crutches
• Deductibles
• Dental fees
• Dentures
• Diagnostic fees
• Dietary supplements with doctor’s le]er of medical necessity
• Drug and medical supplies (i.e. syringes, needles, etc.)
• Eyeglasses prescribed by your doctor
• Eye examination fees
• Eye surgery (cataracts, LASIK, etc.)
• Hearing devices and batteries
• Hospital bills
• Insulin
• Laboratory fees
• Laser eye surgery
• Nutritional counseling, weight-loss programs, and gym memberships if the purpose is to treat a specific disease diagnosed by a physician
• Obstetrical expenses
• Oral surgery
• Orthodontic fees
• Orthopedic devices
• Over-the-counter medication
• Oxygen
• Physician fees
• Prescribed medicines
• Psychiatric care
• Psychologist’s fees
• Routine physicals and other non-diagnostic services or treatments
• Smoking-cessation programs
• Smoking-cessation over-the-counter drugs
• Surgical fees
• Vitamins with doctor’s letter of medical necessity
• Weight-loss programs with doctor’s letter of medical necessity
• Weight-loss over-the-counter drugs with doctor’s letter of medical necessity
• Wheelchair
• X-rays

Appendix 6

Key points from IRS guidance:

• Wellness Incentive Payments Are Taxable: If a wellness program provides cash or cash-equivalent benefits (such as gift cards or direct payments) to employees for participating in wellness activities, and those payments are not strictly reimbursements for qualified medical expenses, the IRS considers those benefits taxable wages. Employers must withhold federal income and employment taxes (FICA, FUTA) on these amounts [20] [21] [22] [23].
• No Double Tax Advantage: The IRS has specifically cautioned against programs that attempt to provide both pre-tax premium payments and tax-free benefit payments under fixed indemnity or similar plans, as this would constitute “double dipping.” If premiums are paid pre-tax and benefits are not strictly for unreimbursed medical expenses, the benefits are taxable [20] [23][24]
• Qualified Medical Expenses Only: Only wellness programs that reimburse employees for actual medical care expenses (as defined by IRC Section 213(d)) can provide tax-free benefits. Payments for general wellness, nutrition, or personal health expenses that do not qualify under Section 213(d) are not excludable from income [21] [24].