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Professional Services Need a New Pricing Model

Professional services for insurers need a new pricing model as AI and cloud technology reshape traditional consulting approaches.

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In an industry where 70% of digital transformations fail to meet their objectives, it's time to fundamentally rethink how we structure, deliver, and measure professional services in insurance.

For decades, the professional services model has remained largely unchanged: Assemble subject matter experts, bill by the hour, and measure success by project completion rather than value creation. This model has become increasingly misaligned with the realities of today's technology landscape.

The Value-Based Pivot

Consider this scenario: An insurer faces a $50,000 problem—perhaps streamlining underwriting workflows or reducing claims leakage. Traditionally, this would translate into a statement of work detailing X consultants at Y hourly rate for Z weeks.

But in the AI-cloud era, this equation no longer computes.

If technology can solve in hours what once took weeks, why should clients pay for the historical time commitment rather than the value delivered? More importantly, if a solution generates $250,000 in value against a $50,000 problem, shouldn't the compensation model reflect this outcome rather than the inputs?

Insurance Transformation: The Data Reality

Our industry research reveals a sobering truth: 84% of insurance transformation initiatives exceed budgets while only 28% deliver their promised value. This disconnect isn't merely a failure of execution—it's a failure of our fundamental approach.

The most successful transformations in our benchmark data share a trait: They define success in terms of concrete business outcomes (reduced combined ratios, improved customer retention, decreased time-to-quote) rather than technology implementation milestones.

The Path Forward

For insurers and service providers alike, three shifts are essential:

  1. Value-based contracting: Define compensation structures tied directly to measurable business outcomes, not hours worked.
  2. Solution-agnostic delivery: Free providers to leverage the optimal mix of human expertise, AI capabilities, and cloud technologies without artificial incentives to maximize billable hours.
  3. Outcome measurement: Develop sophisticated metrics that track value creation rather than activity completion.

A Real Conversation Happening Now

Currently, discussions with a Top 10 property and casualty insurer revolve around a claims optimization challenge they estimate at $2.3 million annually. The traditional approach would involve a 12-month road map with a team of eight to 10 specialists billing hourly.

Instead, a different approach is possible: a value-based arrangement creating an AI-powered claims triage solution with compensation directly tied to demonstrable reductions in leakage. The projected delivery would be 14 weeks rather than 52, with a smaller team supplemented by purpose-built AI models.

Analysis suggests this approach could deliver a 23% reduction in claims handling costs and 14% improvement in customer satisfaction scores. The insurer's leadership is genuinely intrigued—they've never procured services this way, but the risk/reward profile is compelling.

What's particularly revealing about these discussions is the organizational friction around measurement. The procurement team wants hourly rates; the business leaders want outcomes.

The Imperative for Change

As insurance leaders, we must ask ourselves: Are we still buying and selling professional services based on how business operated in 2000, or are we aligned with the value-creation possibilities of 2025?

The insurance industry has long discussed becoming more agile, innovative, and customer-centric. Perhaps the most profound step toward that future isn't a technology implementation but rather a fundamental reshaping of how we structure the relationships between those who need transformation and those who enable it.

What's your perspective? Is your organization still procuring based on hours or on outcomes?

The Strategic Shift to Cloud Landing Zones 

Cloud Landing Zones offer insurers a secure blueprint for digital transformation amid rising pressure to modernize operations.

White Clouds on Blue Sky

In an era where digital transformation is reshaping industries, insurance companies are under mounting pressure to modernize their IT infrastructure and build greater agility while maintaining stringent regulatory compliance and ensuring operational resilience. One of the most effective solutions for facilitating this shift is the Cloud Landing Zone—a structured, pre-configured cloud environment that enables insurers to securely and efficiently migrate to the cloud.

Cloud adoption in the insurance industry has accelerated significantly, with 85% of P&C insurers leveraging cloud infrastructure now compared with under 30% in 2020. However, while many insurers have embraced cloud technology, the transition of core operations such as underwriting, claims, and billing remains a work in progress, with more than half of insurers having only partially migrated core functions. The industry is expected to continue this shift, with cloud services and applications investment projected to grow at a 23% clip through 2028.

For many insurance executives, cloud adoption is no longer a question of "if" but "how." The industry is increasingly recognizing that traditional IT systems cannot keep pace with evolving customer expectations, digital opportunities, regulatory demands, and the competitive landscape.

Moving to the cloud can be a complex endeavor, especially for major migrations, which require meticulous planning, strong governance, and robust security measures. This is where Cloud Landing Zones provide significant value to insurance companies, offering a structured and secure framework for cloud adoption.

Understanding Cloud Landing Zones

A Cloud Landing Zone is essentially a blueprint for cloud adoption. It provides insurance companies with a predefined, secure, and scalable environment in which to deploy cloud-based workloads. Designed to ensure consistency across an organization's cloud infrastructure, a Landing Zone includes critical elements such as account structure, networking, security, identity management, and compliance controls.

Instead of starting from scratch with each cloud migration strategy or application, insurance firms can leverage a Cloud Landing Zone to streamline the process. This approach not only reduces onboarding time but also ensures that security and compliance frameworks are integrated from the outset, rather than being retrofitted later—a crucial factor for an industry where regulatory compliance is paramount.

Leading technology providers offer Cloud Landing Zone solutions to help organizations efficiently move to the cloud era. Amazon Web Services (AWS) provides the AWS Control Tower as an accelerator; Microsoft Azure offers the Azure Landing Zones architecture as part of its Cloud Adoption Framework and implementation accelerators; and Google Cloud Platform (GCP) provides structured guidance for designing and implementing landing zones, with a focus on identity management, resource organization, and security policies. These frameworks streamline cloud adoption while ensuring best practices in security, compliance, and operational efficiency. However, their predefined approaches may require adjustments to fully align with your company's specific needs.

Guidewire provides core systems solutions for the P&C insurance industry through its Guidewire Cloud, a platform hosted on Amazon Web Services (AWS) that enables insurers to manage underwriting, policy administration, billing, and claims. While Guidewire Cloud serves insurance operations, Cloud Landing Zones can complement this by offering a framework for integrating additional third-party cloud-hosted applications and services.

Why Insurance Companies Are Turning to Cloud Landing Zones

The insurance industry is increasingly adopting Cloud Landing Zones for a number of compelling reasons. First and foremost, security and compliance. Insurance companies handle vast amounts of sensitive customer data, from personally identifiable information (PII) to financial records. Ensuring that this data is stored and managed securely while complying with strict industry regulations such as the California Consumer Privacy Act (CCPA), the Gramm-Leach-Bliley Act (GLBA), GDPR, and NAIC guidelines, and state insurance solvency requirements is a formidable challenge. Cloud Landing Zones address this by embedding security best practices and regulatory requirements into the infrastructure from the start.

Additionally, scalability and operational efficiency are key drivers. Traditional IT environments often struggle to support the rapid scalability required to meet fluctuating demands. With a Cloud Landing Zone, insurers can dynamically scale their operations, ensuring they have the necessary infrastructure in place to handle peak loads, such as during natural disasters when claims processing spikes.

Another crucial factor is governance and cost control. Cloud Landing Zones provide built-in guardrails that help organizations manage their cloud resources efficiently, preventing cost overruns and optimizing cloud spending. Insurance executives are well aware of the financial implications of cloud mismanagement, where unchecked resource provisioning can lead to unnecessary expenditures. With a Landing Zone, usage policies and budget constraints can be predefined to align with financial goals.

The Role of Automation and Standardization

Cloud Landing Zones streamline cloud deployment by automating security, compliance, and operational tasks. Automation reduces human error, a major cause of security risks and compliance breaches, while standardizing configurations across cloud environments ensures consistency and efficiency.

A well-structured Cloud Landing Zone includes multi-account architecture for segregating business functions, security and compliance controls with predefined policies, network configuration using virtual private networks (VPNs) and firewalls for workload isolation, and centralized monitoring to track activities and ensure compliance.

By automating these processes, insurers can improve efficiency, lower costs, and focus on innovation rather than IT maintenance.

Real-World Impact: How Insurers Are Benefiting

For insurance companies that have already adopted Cloud Landing Zones, the benefits are tangible. Firms that previously struggled with slow deployment cycles now find themselves able to launch products and services faster. The ability to integrate advanced analytics, artificial intelligence, and machine learning tools into cloud environments has further enhanced insurers' ability to assess risk, improve customer experiences, and streamline claims processing.

Take, for example, a multinational insurance provider that moved its operations to the cloud using a Cloud Landing Zone. Before the migration, the company faced challenges with siloed data, inconsistent security policies, and high infrastructure costs. By implementing a Cloud Landing Zone, they established a unified, secure, and scalable environment, reducing deployment times from weeks to days and achieving a 30% reduction in operational costs.

Moreover, as insurers expand into digital ecosystems, partnerships with insurtech firms, third-party data providers, and cloud-based service vendors are becoming more common. Cloud Landing Zones provide the structured foundation necessary for seamless integration with these partners, enabling insurers to rapidly innovate and adapt to market changes.

Looking Ahead: The Future of Cloud in Insurance

The adoption of Cloud Landing Zones is not just a passing trend—it is a strategic imperative for insurers seeking to remain competitive in a rapidly evolving landscape.

According to a recent survey on AI from Sollers Consulting, as insurers continue their cloud transformation, AI will play an increasingly central role in underwriting, claims processing, and compliance. However, many insurers struggle with adapting AI to existing IT landscapes, workflows, and user interfaces. Without robust planning, complexity can spiral, leading to higher costs and inefficiencies. Cloud Landing Zones provide a structured foundation to integrate AI-driven capabilities more effectively, ensuring that insurers can innovate without disrupting core operations or incurring unnecessary expenses. As artificial intelligence, big data analytics, and digital customer experiences continue to shape the industry, cloud infrastructure will play a central role in enabling these advancements.

For insurance executives, the decision to implement a Cloud Landing Zone is a forward-looking investment in agility, security, and long-term sustainability. By embracing this structured approach to cloud adoption and maintenance, insurers can future-proof their operations, mitigate risk, and unlock opportunities for innovation.

'Flow' Insurance Platforms Drive Growth

Automation and technology-enabled "flow" processes open up a $350 billion market, primarily made up of small and medium-sized businesses.

Close-up of Frozen Water

In the commercial insurance industry, business can be segmented into two distinct archetypes – complex and flow – which require different methodologies for underwriting and processing insurance policies. As middle market business becomes increasingly consolidated in the broker space and sought after in the large carrier space, approaching each risk across the complexity spectrum with the appropriate operating model will be critical to competing effectively and maintaining a healthy expense ratio. Getting this right will be a ticket to growth.

Complex business refers to insurance that undergoes desk underwriting, where an underwriter plays an active role in risk assessment, form selection, and pricing. The underwriters manage specialized risks with complex wording and coverages, navigate across multiple jurisdictions, and handle large limits and capacity. Complex business is typically commercial business distributed through brokers via individual submissions.

Flow business is characterized by automation and technology-enabled processes, with underwriting done primarily through proprietary platforms, portals, or out-of-the-box SaaS solutions. Underwriting operations and decisions are surfaced with little to no support from human underwriters, as the end-to-end process is fully automated for straight-through processing (STP). Industry-leading flow platforms efficiently handle large volumes of submissions via application programming interfaces (APIs) and portals and provide responsive quote turnaround times, while still delivering bespoke customer interactions. Flow business can be distributed to customers through various channels, including brokers, agents, alternative distribution channels, partner organizations, or direct-to-consumer (DTC).

Challenges in the Insurance Flow Segment

The flow segment of the insurance industry represents a $350 billion global market, which is primarily composed of small to medium enterprises (SMEs). This segment of the industry has historically been underserved by large carriers due to the high cost of booking this business manually and technology constraints preventing them from leveraging STP. As a result, SMEs face limited insurance offerings despite accounting for approximately 90% of businesses worldwide.

Low-touch carriers, such as Hiscox and The Hartford, have established themselves as leaders within the SME business segment. As new entrants in the market, small carriers and insurtech firms were able to build technology stacks from the ground up geared toward serving the SME segment of the market. These low-touch carriers typically provide coverage for lines such as commercial property, general liability, and professional liability, which involve relatively standard risks and straightforward underwriting criteria. These lines may also have higher transaction volumes but lower individual premiums, making them less attractive for larger carriers with higher operational costs.

Established insurance carriers have the expertise to understand the underlying risks and process policies in this segment, but they lack the IT infrastructure to do so efficiently. Studying the blueprint laid out by new entrants can provide valuable insights for larger carriers. By understanding the needs of the flow segment and strategically investing in flow-enabling technology, large carriers can efficiently serve this market.

While flow business strategies prove highly effective in certain lines of business such as SME, they may be less applicable or successful in other lines. Specialty, complex commercial risks, and high-net-worth personal lines often require more nuanced underwriting, personalized risk assessment, and specialized coverage solutions that cannot be easily standardized or automated. Therefore, carriers operating in these segments should strike a balance between automation and personalized service to meet the unique needs of their customers.

A shift in consumer behavior has also driven demand for flow strateges. In today's technology-driven world, people are more likely to purchase insurance online – specifically for smaller risks like property and general liability insurance. To remain competitive and win business, carriers should adopt a lean operating model: a centralized referral underwriting team to handle any complex cases, and standardized product structures that allow for scalability.

Benefits of Adopting a Flow Platform

Automated and streamlined workflows will allow carriers profitability by allowing underwriters to focus primarily on higher-margin, complex business, resulting in lower expense ratios and more gross written premium (GWP) per underwriter. Faster turnarounds produced by the low-touch flow platforms will improve customer satisfaction and engage more brokers with its ease of use. Additionally, technology-enabled operating models have integrated systems and data sources, allowing for increased scalability across all lines of business and geographies.

Implementing the future-state operating model requires heavy investment that poses some challenges and risks for carriers. There are large costs associated with the technological development required to separate flow and complex business, particularly with API and artificial intelligence (AI) integration and developing digital trading solutions. Disjointed systems should be rationalized and integrated with one another, and the carrier will need all the internal systems previously described for the investment to be worthwhile and expand business. The system capabilities should either be built internally or purchased from an insurance-in-a-box platform.

How to Adopt a Flow Platform

Many large carriers are relying on shared service centers and underwriters to assess and triage submissions in a manual, time-intensive process, as well as manually quoting, booking, binding, and issuing policies that are straightforward and low risk. This leads to carriers incurring a high fixed expense basis, which would require low loss ratios to remain profitable and competitive. Instead, underwriters should focus their time on complex business that cannot be conducted through STP and handle requests for flow business through an exception-based referral process. This allows underwriters to use excess capacity to go after more complex business, while maintaining the same fixed expense basis and simultaneously increasing market share in the underserved flow market segment through low-touch STP.

To capture flow business while optimizing efficiency and competitiveness, carriers should develop flow platforms and processes that prioritize simplicity and scalability. Although generative AI is gaining traction in the insurance industry, its high development and maintenance costs may not improve loss ratio enough to bolster the carrier's competitive advantage. Instead, flow solutions should leverage simpler rules engines and decision models, providing a cost-effective way for carriers to enhance automation and take on more of this business with minimal manual intervention. Embracing exceptions within the flow process is crucial, as an optimal model will decrease exceptions over time, thereby enriching and accelerating automation while laying the groundwork for future AI-augmented automation. This approach enables underwriters to direct their focus toward complex business that necessitates their expertise, while efficiently handling flow business through an exception-based referral process. Implementing this type of flow solution can simultaneously reduce operational costs, increase market share in the underserved flow market, and strengthen carriers' competitive position in the evolving insurance landscape.

High operational costs and poor expense ratios for flow business are just two of the hurdles insurers venturing into flow business face today, especially as this sector becomes increasingly commoditized. The expenses associated with transacting flow business and the need to streamline operations to remain competitive make it difficult for carriers to find the value in investing in flow, but the potential for carriers to differentiate themselves by investing in flow remains. Carriers also face fragmented and outdated internal systems that prevent them from embracing technological advancement. These systems may be difficult to rationalize, but modernizing these systems presents an opportunity to improve efficiency and cost management. As the insurance landscape evolves and competition intensifies, carriers must prioritize updating their infrastructure to reduce costs and improve their competitiveness in both the flow and complex markets.

Developing a Flow Solution

To capitalize on the growing SME market and expand into the lower middle market, insurers should build a flow platform that can integrate with an underlying policy admin system, supporting workflow (including referrals), and simplified raters for flow business. Once this has been established, carriers will be able to engage with brokers digitally to underwrite and service business. Brokers expect that carriers will provide immediate turnaround times on simple transactions, with the convenience of obtaining issued policies at any time, not limited by standard business hours. Brokers are turning to carriers that can offer products with agile pricing through digital service distribution, which includes full-service online portals that are user-friendly, as well as API connectivity into in-house broker platforms or trading marketplaces (e.g. Lloyds).

Carriers that are currently excelling in the flow segment of the insurance market have these flow platforms and are heavily investing in digital trading solutions. These solutions offer full-cycle online services (quote, buy, amend, renew), sophisticated pricing models, and integrated AI. Future underwriting operating models will use integrated APIs and AI to auto-populate submission fields and use a rules engine to triage submissions, categorizing as either flow or complex. If the submission is flow, it will be end-to-end processed through a flow platform for STP. If the submission is complex, generative AI will be harnessed to produce an output with pertinent information for underwriters to refer to in their quoting process.


Brian Nordyke

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Brian Nordyke

Brian Nordyke is a vice president in the financial services practice at SSA, a global management consulting firm.

He leads teams as an engagement manager in areas such as organizational and operational model redesign, cost-to-serve and market profitability analysis, consolidation and relocation strategies and portfolio optimization and resource allocation. 

How to Integrate Generative AI

Carriers must ensure data readiness, carefully validate outputs, and adopt governance structures that balance speed with compliance.

An artist’s illustration of artificial intelligence

Generative AI (Gen AI) is emerging as a transformative force in property and casualty (P&C) insurance. By producing text, synthetic scenarios, and advanced policy language, Gen AI allows carriers to expedite policy development, automate communications, and refine claims handling. Despite promising pilots, though, many organizations find it challenging to embed Gen AI into their daily workflows. These hurdles arise from a mix of legacy systems, diffuse governance, skill shortages, and strict regulatory obligations. 

This article outlines practical steps for preparing robust training data, validating generative outputs, and aligning team structures so that P&C carriers can fully harness Gen AI's potential.

1. Ensuring Data Quality and Model Reliability

Effective AI implementation relies on high-quality data and robust validation mechanisms.

Data Curation and Preprocessing

High-performing Gen AI models depend on comprehensive datasets that capture the nuances of policy language, claim files, and relevant external materials (e.g., regulatory bulletins). When sources are inconsistent, a generative system can produce flawed or misleading outputs.

  • Data Cleansing and Standardization: Removing duplicates, filling gaps, and aligning formats establishes a consistent base for model training. Adopting common taxonomies for coverages, claim types, and underwriting codes helps the system learn domain-specific subtleties.
  • Metadata Tracking: Detailed documentation of data provenance and version history is essential for regulatory reviews or internal audits. It clarifies which records influenced the model's text generation and how data were transformed.

Validation and Testing Protocols

Even if the underlying dataset is robust, poor validation can cause the system to violate regulatory or corporate expectations.

  • Train/Test Splits and Fine-Tuning: Splitting data into training, validation, and testing cohorts reveals whether a model generalizes well. Fine-tuning steered by experienced underwriters and claims experts refines text outputs and addresses domain quirks.
  • Human-in-the-Loop Review: Particularly for endorsements and settlement letters, domain specialists must inspect generated text. Their feedback ensures factual accuracy and compliance, catching issues that a purely algorithmic approach might miss.
  • Bias Audits and Fairness Checks: Regularly auditing outputs for skew is critical. If the model references demographic factors or inadvertently discriminates, carriers should re-balance training data or implement more precise prompts.

2. Streamlining Data Integration and Governance

Seamless integration of AI with existing systems ensures consistent and reliable performance.

Fragmented Systems and Legacy Technology

Policy forms, claims notes, and underwriting materials often reside in multiple repositories, complicating the flow of data to Gen AI models.

  • Middleware and APIs: Standard APIs or data-exchange layers unify these repositories, giving Gen AI platforms consistent access to forms, manuals, and state-specific directives.
  • Centralized Data Lakes: A central repository with version-controlled text corpora and structured reference tables supports timely updates, ensuring that generated outputs keep pace with new mandates and product offerings.

Agile Governance Frameworks

Gen AI evolves rapidly through repeated fine-tuning, necessitating governance that can adapt to frequent model updates.

  • Gen AI Oversight Boards: Bringing together compliance officers, data scientists, and product managers, these boards evaluate outputs for alignment with brand standards and regulatory rules, greenlighting new model iterations.
  • Adaptable Approval Processes: Narrow pilots (e.g., a single policy line) allow real-world testing of generative capabilities. Swift approval channels then facilitate broader deployment if the pilot proves successful.

3. Navigating Regulatory and Compliance Challenges

Ensuring compliance with regulatory frameworks is crucial for responsible AI adoption.

Explainability and Accountability

While large language models can appear opaque, carriers must document how text is generated and why certain policy clauses appear.

  • Traceable Prompting Methods: Storing prompt logs clarifies the chain of reasoning behind each generated document. This traceability becomes vital when auditors question certain terms or phrases.
  • Compliance Tags and Anchors: Embedding references to known regulations or internal guidelines in the generative pipeline clarifies how the system aligns with legal and policy frameworks.

Mitigating Bias and Discrimination

Older underwriting manuals or legacy coverage language might inadvertently contain biased terms. Gen AI may replicate or amplify them.

  • Content Filters: Automated filters can detect disallowed terms or sensitive phrasing. When flagged, the output is routed to a compliance specialist for manual review.
  • Monitoring: Periodic spot-checks of generated drafts reveal emergent biases, whether from newly introduced data or shifts in the model's language patterns.

4. Bridging Skill Gaps and Enhancing Talent Management

Building AI fluency among employees is essential for successful adoption.

Cross-Functional Collaboration

Effective generative models rely on expertise spanning underwriters, actuaries, claims staff, data scientists, and IT engineers. Each group offers insights that enhance the system's relevance and accuracy.

Upskilling and Culture

Adjusters and underwriters need fundamental knowledge of large language models, prompt engineering, and the review process. Workshops and scenario-based training help staff understand how to engage with Gen AI outputs and why their feedback is critical.

5. Implementing Generative AI in Daily Operations

A structured approach ensures seamless AI implementation in daily workflows.

Change Management and Workflow Redesign

Shifting from conventional writing or manual quoting to AI-generated drafts can unsettle employees. Without clear planning, staff may see Gen AI as a threat.

  • Defined Objectives and Key Performance Indicators (KPIs): Targets, such as cutting average drafting time or enhancing policy consistency, should guide adoption and help measure progress.
  • Phased Rollout: Testing generative solutions in one product line or geographic region yields user feedback. This approach refines the system and builds internal trust before a full-scale launch.

Continuous Model Lifecycle

As new policy endorsements, regulatory edicts, or market conditions emerge, Gen AI requires fine-tuning.

  • Version Control: Tracking each model iteration, including training data updates and performance metrics, ensures stability and supports audits.
  • Retraining and Retirement: Outdated coverage forms or legislative changes may necessitate retraining. Retiring old models avoids conflicting language that could mislead policyholders.

6. Addressing Ethical and Reputational Considerations

Addressing ethical considerations ensures responsible AI usage and maintains public trust.

Kahneman's Lessons for Generative AI

Daniel Kahneman's "Thinking, Fast and Slow" highlights cognitive biases that can surface in human decisions. These biases may slip into prompts or reflect in historical data, prompting carriers to audit model outputs diligently for skewed or unjust assumptions.

Customer Trust

Clear, accurate communication builds confidence. Demonstrating that humans review critical outputs—especially denials or complex coverage decisions—reassures policyholders that the process is fair and empathetic, rather than purely automated.

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Making It Real: AI-Powered Policy and Claims Automation

A regional P&C carrier introduced a generative platform to automate policy drafts and claim correspondence. The system was trained on curated underwriting manuals, coverage forms, and anonymized customer emails.

Phase One—Policy Drafting

  • Underwriters and data scientists created "prompt outlines" that included required clauses and references to state-specific endorsements.
  • A brief pilot in auto policies achieved consistent language while minimizing "hallucinated" terms. Human-in-the-loop reviews identified ambiguous outputs, leading to refined prompts and additional training data.

Phase Two—Claims Communication

  • Buoyed by the auto-policy success, the carrier extended generative drafting to homeowners' claims. A cross-functional "Generative Oversight Council" reviewed outputs for fairness and clarity, and set up alerts for any coverage interpretations beyond established guidelines.
  • Staff surveys showed a 30% cut in writing time for settlement explanations. Customer feedback cited better readability and consistency in communications.

Key Insights

  • Controlled Scope: Focusing on auto policies first helped the carrier perfect prompting techniques before tackling more complex homeowner's lines.
  • Prompt Engineering: Fine-tuning prompt details and references to recognized coverage forms guarded against inaccurate text.
  • Iterative Governance: Frequent oversight meetings allowed stakeholders to reconcile new regulatory updates or product changes quickly.

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7. Aligning AI Strategy with Business Goals

AI implementation should align with overarching business goals to maximize impact.

Linking Gen AI to Business Goals

Carriers are more likely to endorse Gen AI projects that directly advance corporate strategies—be it entering niche markets or boosting renewal rates. Showing how automation improves customer satisfaction or reduces operational costs encourages sustained support.

Demonstrating Return on Investment (ROI) and Broader Benefits

Beyond labor savings, generative models can provide consistent brand voice, reduce training overhead, and strengthen regulatory relationships through clearer documentation. Tracking these intangible advantages helps justify continued investment.

Conclusion

Implementing Generative AI in P&C insurance involves more than model deployment. Carriers must ensure data readiness, carefully validate outputs, and adopt governance structures that balance speed with compliance. By coupling cross-functional expertise with robust oversight, P&C insurers can harness Gen AI to modernize policy generation and claims communications without sacrificing regulatory standards or customer trust. Adhering to clear key performance indicators (KPIs), refining processes through pilot programs, and acknowledging ethical considerations pave the way for a more agile insurer—one positioned to thrive amid evolving market demands.

 


Upendra Belhe

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Upendra Belhe

Dr. Upendra Belhe is president of Belhe Analytics Advisory.

With over 30 years of experience in the P&C insurance sector, he has been a catalyst for innovation, driving the adoption of AI, advanced analytics, and data-driven strategies to transform insurance operations. Dr. Belhe has held senior leadership roles in global insurance organizations, shaping best practices in underwriting, claims, and risk management.  In recent years, he has been at the forefront of introducing Generative AI and agentic AI to the industry, helping insurers unlock new efficiencies and capabilities. Through his advisory practice, Dr. Belhe collaborates with insurers, insurtech firms, and investors to develop and implement transformative analytics strategies that create sustainable competitive advantage.

Tackling the Disconnect Between Insurers and Insureds

The insurance industry has focused on price, not service, for far too long, producing a failure to communicate with customers. 

Three black old fashioned phones with cords against a grey solid background

The state of the insurance industry’s reputation in 2025, in a word, is disastrous. 

Last year, insurance claims exceeded $100 billion, and the biggest takeaways for most insurance consumers centered on:

  • Coverage cancellations or non-renewals
  • Lack of transparency due to complex policy language or fine-print legalese
  • Denial of claims or complex processes complicating the recovery process
  • Rising premiums, along with either limited or no coverage options available
  • An education gap between what consumers think their policies cover versus actual coverages 

This is not to say that last year's CAT events were the deciding factor in how consumers perceive the industry. There has been a decades-long process as insurance has increasingly undercut its reputation by continuing to commoditize itself, competing on price versus service. Last December’s murder of United Healthcare’s CEO and the resulting public swell of support for the alleged killer helped bring the broader insurance industry’s reputation into focus, both for the public and for insurance professionals. 

Why the Bad Rep?

The way the insurance industry operates is hard for most consumers to understand. 

Those who go direct believe they are getting better rates and coverage. However, most insurance policies are difficult to comprehend, and the buy-direct model can frequently expose insureds to increased risks because the focus—of the insured as well as the insurer—is on price, not service or comprehensive coverage. When a claim does occur, most policyholders only then realize they were underinsured or not covered at all.

However, even those who purchase their insurance products through the traditional means of agents and brokers can run into complicating and confusing factors that include:

  • Lack of transparency on coverage and pricing
  • Manual, time-consuming and duplicative application processes
  • Limited or insufficient digital or self-service options

An understanding of the critical fundamentals of insurance is key to changing this perception. Consumers need to be informed on how premiums are calculated or why coverage fluctuates with certain policy changes. The first time most insureds learn of a change to their premium is during the renewal period. Even then, there is little explanation of why their costs have increased when coverage has stayed the same. 

One factor that has helped to push consumers to the buy-direct model is the complexity of the insurance application process itself. Many agents still follow antiquated, paper- or PDF-based processes that require significant manual input of duplicative information and extensive supporting documentation. For entire generations of Americans who appreciate the click/buy/instant gratification model of ecommerce platforms, the old-fashioned insurance application process is anathema, which drives them to the buy-direct model at their peril. Even for insurance agencies that offer digital applications, many of their online platforms are limited or must be supplemented by some form of the traditional application model. 

What Do Customers Want?

Consumers are looking for affordability and value, transparency, convenience and personalization. 

I call the commoditization of insurance the Walmart effect. Consumers want reliable products at the lowest possible price. However, focusing exclusively on the lowest-possible insurance premium can expose consumers to significant risks and, potentially, much more devastating long-term costs. This is one of the key education issues the industry must undertake. Consumers must better understand the inherent value of their policies and that higher premiums are not a scam for insurers to make money, but rather a practice to ensure the consumer has the necessary coverage to avoid a potential claims disaster. 

Transparency in insurance isn’t just about pricing—it’s about trust. When buyers clearly understand their policies, they see how coverage choices affect their premiums. A transparent application process shifts the focus from cost alone to the real value of coverage, helping insureds assess their risks more effectively. But true transparency extends beyond numbers. Consumers need to know exactly what they’re paying for and how deductible options affect them. To rebuild trust, the industry must simplify its language, cut the jargon, and ensure every policyholder can make informed decisions with confidence.

In terms of convenience, insurance simply must do better identifying opportunities to incorporate technology to elevate the consumer experience. Consumers expect on-demand quick policy quotes, minimal data input and rapid responses. This is the real-world experience for consumers when they go online to book appointments, select event or travel experiences, buy groceries, shop for cars and make dozens of other purchasing decisions from their smartphones or computers. They expect no less from insurance. 

Insurtechs are driving innovation, equipping the insurance distribution channel with the tools to streamline purchasing and quickly adapt to changing consumer behavior. Beyond simplifying transactions, these technologies enable insurers to introduce new products faster and stay competitive in a rapidly evolving market.

Insurers that successfully deliver the personalization consumers expect will set themselves apart. They won’t just earn customer loyalty—they’ll gain the respect of industry professionals who recognize a better way to do business. But true personalization is no easy feat. AI and machine learning are valuable tools, but they’re only part of the equation. Insurers must invest time, resources and skilled personnel to fully understand their customers’ unique circumstances and risk portfolios. By providing tailored recommendations to mitigate risk, insurers can not only improve coverage but also help lower premiums, creating real value for policyholders.

What Is the Solution?

For many insureds, experienced independent insurance agents with a view to the future offer a first line of defense against the further commoditization of insurance. However, this requires a fundamental rethinking of the agent role. 

Insurance will continue to be a relationship business. Agents as advisers is a model long discussed but executed haltingly over the years. This is where technology and the solutions offered by insurtechs can play a role. The facilitation of better matching experienced agents with risk-focused consumers, as we do at Freshquote, we hope is one step in the right direction to solving not only the industry’s reputation problem but also the business development and coverage challenges of agents and insureds, respectively.

Automation and digital enablement can reduce the friction in the quoting and buying process, both for consumers and agents. Properly applied, automation can easily collect data, obtain and evaluate quotes, and even bind and pay. This frees the agent to focus on the role of trusted adviser, giving them time to help consumers understand what is important from a coverage perspective. It also shifts the agent’s focus from the transaction to better understanding and helping to mitigate risk. Additionally, the agent can spend more time advising consumers on various events or trends that may impact their coverage or premiums.

Changing how we approach insurance — by insurers as well as those we insure — will help us all. For consumers, it means looking beyond premium, seeing insurance as a service and protective shield against adverse events and experiencing an easier, better-informed, and more convenient insurance purchase experience. For agents, it presents an opportunity to recast their roles, potentially expanding their books of business and establishing greater trust and partnership with their clients. It will also help to build back trust in one of the world’s most crucial industries at a time when its reputation is so significantly misunderstood and misrepresented.


Colleen O’Hara

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Colleen O’Hara

Colleen O’Hara is a senior executive with Freshquote, which serves as a personal shopper for insurance consumers. 

O’Hara has two decades of experience advising insurance businesses on data-driven growth tactics and strategy.

The Blind Spots in Catastrophe Models

Traditional catastrophe models fall short as climate change intensifies natural disaster risks, demanding smarter approaches to assessment.

Red and White Windsock Against Clear Sky

To outsmart the uncertainties posed by complex and related physical climate risks, organizations need to consider whether their current approach to modeling and assessing natural catastrophe is fit-for-purpose.

As the world heads toward global warming potentially beyond two degrees Celsius by 2050, we're already seeing greater volatility in weather-related natural catastrophe events, as well as an increased impact of chronic hazards, such as heat and cold stress. This is leading to greater uncertainty in economic losses and insurability.

By better understanding and quantifying the true cost implications of climate-amplified natural catastrophe risks, organizations can better prepare for the risks. This may mean checking they are not over-reliant or misinterpreting catastrophe risk models to ensure they avoid gaps in their organization's protection.

Traditional models leaving businesses exposed

Some traditional models for quantifying natural catastrophe risk are leading businesses to potentially miscalculate or underestimate their exposure to catastrophic events. Due to the lack of data and functionality limitations, traditional natural catastrophe modeling typically struggles to capture the wider financial impact due to external value chain interdependencies and operational disruption.

For example, during the 2021 flood in Western Europe, water utility companies authorized water management interventions on several major rivers. This prevented catastrophic dam failure as part of the emergency response procedures for severe/low likelihood events but increased the severity of flooding further downstream. We understand some private sector organizations did not factor these amplifying issues into their risk management and risk financing strategies, having based their decision-making predominantly on theoretical models and their own operational resilience.

Such cases illustrate the importance of moving away from relying solely on theoretical models and instead using a combination of "what-if" severe event scenario stress testing, risk engineering and theoretical modeling that looks beyond organizational boundaries. Organizations should also be prepared to review publicly available emergency response procedures of utility companies to enhance the modeled loss perspectives for flood risk of the theoretical models.

Getting these wider perspectives can enhance a company's ability to understand, quantify and manage the impact of severe events that are becoming more frequent due to climate change. This may also involve revisiting recent and historic natural catastrophe events, claims histories and the lessons learned to better evaluate and scrutinize the theoretical models and their underlying uncertainties, potentially in collaboration with academic or other external partners where an organization does not have the skills sets required internally.

Smarter modeling means harder-working risk spending

Outsmarting natural catastrophe exposures exacerbated by climate change isn't just about closing protection gaps. An evolved natural catastrophe modeling approach that is bespoke to an organization and better reflects the potential impact of different climate scenarios, puts decision makers in the driver's seat of what to spend on protection. By moving away from using a single natural catastrophe model to a more nuanced, multi-method approach, organizations are able to optimize their risk spending.

That's because a wider, clearer view on a company's risks will clarify what does and doesn't represent good value on insurance markets. Organizations will have better insight on questions like: Is my risk worse or better than my peers and, if so, why? They will also know how to better attract capital to their risk.

In a fragile insurance environment, evolving a company's modeling approach puts them in a much firmer position than those organizations that lack a clarified, data-driven view of their risks.

Secondary perils and the amplifying effects of climate change

A 'secondary' peril is a natural hazard that typically leads to small or mid-size damages compared with primary perils such as earthquakes or hurricanes. However, secondary perils, such as landslides following heavy rains or flooding, can often be as damaging as the primary events, meaning organizations need to factor these into how to assess their natural catastrophe risk.

In fact, we're seeing more organizations needing to address how perils such as landslides can be triggered by primary events like earthquakes, floods and tsunamis. Such hazards introduce additional layers of risk that traditional catastrophe models often don't capture.

For instance, a primary event like heavy rainfall may not only cause immediate structural damage but lead to landslides that block access routes, disrupt supply chains and prolong business interruptions. This can lead to further damage to the critical infrastructure and hinder recovery efforts.

That's why strengthening physical climate risk resilience means incorporating scenario testing and stress testing beyond traditional catastrophe modeling to gain that crucial, more comprehensive view of a company's risk exposures, including the potential impacts of secondary perils.

By understanding these compounded threats, organizations can better prepare and build resilience, ensuring they can maintain operations even when faced with complex and connected challenges.

To get ahead of natural catastrophe and physical climate risks today, scenario testing has a valuable role to play. By combining traditional catastrophe and climate analysis with additional stress testing, catastrophe risk engineering and scenario testing, an organization can get a more robust risk management view based on a deeper understanding of their risk profile and impacts across their value chain.

In some cases, this can lead to the business prioritizing non-insurance risk mitigation controls and action plans such as business continuity plans, recovery plans and crisis management readiness, rather than relying on traditional insurance, to improve resilience.

Advanced modeling approaches can also help inform conversations with insurance markets, help address coverage gaps and optimize decisions on risk financing and transfer. This could lead to alternative risk transfer and parametric solutions, depending on a company's risk tolerance, particularly when sufficient capacity is a challenge.

Risk managers looking to take a more strategic role can also leverage methodologies that quantify the current and future value of their company's assets and explore how investors view the organization. Quantifying the financial impact of climate-related risks in this way can enable a better response to climate risks and opportunities (while also potentially meeting certain climate disclosure requirements) and inform strategic conversations on the business's future ability to achieve targets, realize organic growth and access capital.

Pet Insurance Business Thrives

Pet insurance growth drives inland marine market changes as carriers begin reporting it separately in 2024.

A Vet Checking a Sick Rough Collie

For the first time in 2024, insurance company quarterly and annual financial statements started separating pet insurance from the rest of the inland marine premium and loss data. Because several pet insurers offer no other additional coverages that are considered inland marine, prior statement data for these identified insurers can provide insight on pet insurance.

Pet owners have been dramatically increasing purchases of pet health insurance over the last several years. Through the first three quarters of 2024, pet insurance premium came to just over $3.4 billion, meaning the full-year total could be over $4 billion—possibly even $4.5 billion. Based on reporting from the North American Pet Health Insurance Association (NAPHIA), pet health insurance premium more than doubled in the five-year period to 2023, to $3.9 billion from $1.6 billion, with at least 20% growth per year. 

The loss ratio for pet insurance through the first nine months of 2024 was higher than for the rest of inland marine insurance, due possibly to growing demand for insurance to help cover rising veterinary costs. Including pet insurance, the inland marine line's loss ratio for the period remained in the post-pandemic 44%-to-49% range.

The top 10 pet insurers account for 90% of the pet insurance market, making it a highly concentrated market. At the group level, the market is even more concentrated, because National Casualty (No. 2) and Veterinary Pet Insurance (No. 9) are both part of the Nationwide Property & Casualty Group. American Pet Insurance is the No.1 U.S. pet insurer, with $819 million in direct premium through third-quarter 2024.

Top 10 Pet Insurance Writers, by 3Q2024 DPW

Five of the top 10 write no other inland marine coverage, and for two other companies in the top 10, pet insurance accounted for more than 97% of the inland marine premiums. This concentration provides credible insight into the historical underwriting performance of their pet health coverage. Direct combined ratios are mixed, with close to an equal number on either side of the 100.0 breakeven point.

Inland Marine Outlook

For the rest of the inland marine market, results have been very consistent over the past 10 years, with 2020 being an anomaly owing to the pandemic. Event cancellation and travel insurance are two of the catch-all classes captured under the inland marine line of business, and the business line has its roots covering goods in transit, a good measure of which is the U.S. Freight Transportation Services Index.

A rising index indicates that more goods are in transit, which suggests a growing need for insurance to cover those goods. The index has fluctuated moderately the last five years, except for the severe drop early in the COVID pandemic and the subsequent rebound when operations restarted. Even if the amount of goods in transit remains flat, sustained inflationary pressures on the value of those goods could result in higher premium.

Transportation Safety Administration checkpoints at airports also can indicate the amount of travel in the U.S. and provide insight into premium volume specific to trip cancellation. During 2020, when travel was limited due to COVID restrictions, TSA throughput dropped substantially. Total inland marine direct premium written (DPW) thus experienced its only decline since at least 2011, and possibly even before that. For the first time in 2024, TSA throughput exceeded pre-pandemic levels, signaling that travel is back to normal and supporting the likelihood that trip cancellation coverage will increase for the year.

Overall, inland marine remains profitable, outperforming the entire property/casualty insurance industry by a wide margin and doing so with steady growth, buoyed by growing construction and increasing travel. The line's direct loss ratio was more than 20 points better than that of the property/casualty industry in 2023 and has been worse than the P/C industry's only once in the past 14 years—in 2020, when contingency claims (i.e., event and travel cancellations) spiked due to the pandemic shutdowns.

Given that pet insurance accounts for about 10% of inland marine insurance and pet insurance results have been only marginally profitable, inland marine results excluding pet insurance are even more favorable.

Rethinking Data Management in Commercial Lines

Automated data ingestion transforms commercial insurance operations, driving efficiency and revenue growth while laying the groundwork for AI advancement.

Group of Businesswomen Having a Meeting

The digitization of the insurance submission, quoting, and claims processes is prevalent in personal insurance, but commercial capabilities have been slower to adopt due to the complexity of the risks and the data required for underwriting. Those who master the complexities and transform these processes will be cracking the code for faster growth in the highly competitive commercial lines of insurance.

There is growing demand in the market from both retail and wholesale brokers for streamlined interactions with carriers that result in timely and accurate quotes, policies issued, and claims settled. In response, large P&C carriers such as Nationwide, Markel, and Arch Insurance have adopted digital capabilities wherein a user can upload unstructured documents to pre-populate a complex submission, generate a quote on flow business, or adjudicate a claim in a matter of seconds. In markets where speed matters, such as the excess and surplus (E&S) market, this capability can be a differentiator and give competitive advantage to carriers that prioritize data ingestion.

Taking the approach of automating the process for ingesting data has gained traction in the commercial insurance industry. It is deemed a keystone capability for carriers' and brokers' paths in their digital evolution. Additionally, as GenAI capabilities are increasingly being developed to assist underwriters and claims adjusters in their assessment of risks and claims, the breadth and accuracy of data ingested will have a strong correlation with the efficacy of the GenAI tool and become increasingly valuable.

Understanding Data Ingestion: Challenges

Data ingestion refers to the process of extracting, validating, curating, and processing data from various third-party sources into a system of record or centralized data repositories/data lakes. In the context of the insurance industry, where massive amounts of data are generated daily, the manual handling of such a task can be overwhelming, error-prone, and time-consuming. This data can span an array of use cases from risk assessment, quoting and issuing insurance policies, to filing/settling claims, or even booking premium from insurance policies that have been delegated to third parties.

The insurance industry has had challenges getting to this level, starting with the diversity of data types, including structured and unstructured data from sources such as policy documents, bordereaux forms, claims forms, and insured statement of values.

  • Structured data typically refers to system/database data that is organized in a specific and predefined manner, typically following a schema or a data model, or "semi-structured" document formats such as CSV, JSON, or XML. Structures and definitions are typically pre-determined between the two parties exchanging the data for ease of processing.
  • Unstructured data can take the form of emails (e.g. broker submission), Word/PDF documents (insured statement of values), images (e.g., car collision), financial documents (e.g., delegated premium), etc. and pose a greater challenge for organizing, processing, and synthesizing meaningful insights. New technologies are making it easier to unlock the potential within this enormous trove of untapped insights.

The sheer volume of data generated in the insurance sector is staggering—efficiently handling and processing this data is a daunting task, burdening staff whose time would be better spent on other activities. Additionally, getting to sufficient data velocity is an issue, as real-time data processing and speed to market are crucial to driving premium for commercial insurers. Processing claims in a timely manner can affect client retention and revenue from recurring business. Finally, ensuring data quality— the accuracy and reliability of data—is paramount for optimizing commercial insurers' portfolios and protecting their bottom line. Manual data entry, which even some major carriers still practice, is error-prone and can lead to inaccurate insights and decisions. Automating this process can address key challenges by streamlining the collection and integration of data from diverse sources.

Digital Maturity and the Advantages of Automated Data Ingestion

The digital maturity for deploying a data ingestion solution in the insurance industry typically progresses through several stages as the organization evolves its operational and IT capabilities. These include ad hoc data ingestion, basic automation, standardization and optimization, real-time data ingestion, advanced analytics, AI integration, and predictive and descriptive analytics.

A cornerstone capability to build into each of these ingestion maturity phases is natural language processing (NLP). NLP plays a critical role in data ingestion by helping to extract, understand, and process unstructured textual data. In the commercial insurance industry, NLP enables policy/claims document parsing, analyzing exposure documents for underwriting assessment, fraud detection, and more.

In the final stage of digital maturity, the organization is able to leverage fully ingested, curated, and enriched data, not only for descriptive and diagnostic analytics but also for predictive and prescriptive purposes. Advanced machine learning models can be trained on ingested data to forecast trends, identify potential errors or risks, and recommend optimal remediation actions. Eventually, the data ingestion approach becomes an integral part of the organization's decision-making process, driving business growth and profitability and lowering expenses.

Different insurance carriers, brokers, and intermediaries are at different stages of this data ingestion maturity curve, and some insurtechs provide unique capabilities to achieve such maturity. As these capabilities eventually come to be a necessary part of effective business practices, it is imperative for commercial insurers to make investment decisions to remain competitive in the marketplace.

Automated data ingestion solutions bring a number of advantages to the insurance industry and is a necessary prerequisite for the broad set of data needed for GenAI capabilities in the future. Having a "generative assistant capability" requires that an insurer can ingest the appropriate data and feed it into that capability.

Other benefits in the here-and-now include time-saving efficiencies that allow underwriters, operations, and claims professionals to focus on higher-value tasks; greater accuracy and reducing the risk of human error; and real-time processing that allows for up-to-the-minute insights for better decision-making, fraud detection, and customer service. In terms of adoption, automated data ingestion solutions can seamlessly integrate data from a variety of sources, including legacy systems, external databases, and IoT devices.

Once the foundation of a data ingestion tool is established, insurance organizations can scale this solution across their various geographies and product offerings, particularly in high-volume insurance programs. As the volume of data grows, machine learning kicks in to make automated solutions more robust and able to handle the increased data loads, ensuring that insurers can adapt to changing business requirements.

The sustained success of deploying an automated data ingestions solution is limited in the absence of data governance. Data governance is a vital protocol for enabling data consistency, accessibility, quality, and flow through the organization, while also delivering compliance for industries like insurance. In the context of deploying automated data ingestion solutions in the insurance industry, data governance becomes crucial for several reasons, such as: 

  1. Ensuring data quality to officiate accurate underwriting/claims decision making
  2. Abiding by state-by-state data privacy and security laws to avoid reputational risk
  3. Managing potential risks of data breaches or cyber-attacks
  4. Ensuring business continuity/data standardization for interoperability across the organization. 

Without a data governance framework in place, it becomes increasingly difficult for an insurance organization to effectively manage and leverage their data to drive business value, mitigate risks, and maintain a competitive edge in the dynamic insurance marketplace.

Case Study: Data Ingestion Grows Premium Revenue

A consulting firm worked with a leading P&C insurance carrier in the U.S. that had extremely long onboarding times—previously, it could take them six to 12 months to onboard one new customer, due to their manual data ingestion process and onerous data requirements. This sub-par data system inhibited their ability to drive incremental revenue, adopt standardized data protocols, and gain visibility into their portfolio. It also posed various operational, IT, and downstream data challenges to the organization.

In response, the company introduced a tool to automate their data ingestion process. Soon after implementing it, the carrier was able to automate manual, error-prone tasks associated with the extraction, mapping, curation, and piping of the data into downstream systems. Automating the management of this data served to optimize the carrier's operational processes.

Further, the carrier was able to grow premium revenue because it was able to onboard subsequent customers more rapidly. This led to a stronger reputation in the market, attracting additional business. The company's loss ratios were enhanced by better portfolio transparency, gained from the improved data being leveraged for actuarial analyses and risk assessment. The associated benefits with deploying this approach to data ingestion significantly outweighed the cost—$5.2 million—generating a net present value of $20.4 million for the company over the span of five years. Possibly even more important in the long term, the initiative established a foundation for revolutionizing the company's digital ecosystem.

Automating Can Be Transformational

The adoption of automated data ingestion solutions represents a revolutionary shift in the insurance industry. By harnessing the power of automation, insurers can overcome the challenges posed by the increasing volume, variety, and velocity of data. The benefits extend beyond operational efficiency to improved decision-making, enhanced customer experience, and a more competitive position in the market. As the insurance landscape continues to evolve, embracing automated data ingestion is not just a technological choice – it's a strategic imperative for success.


Brian Nordyke

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Brian Nordyke

Brian Nordyke is a vice president in the financial services practice at SSA, a global management consulting firm.

He leads teams as an engagement manager in areas such as organizational and operational model redesign, cost-to-serve and market profitability analysis, consolidation and relocation strategies and portfolio optimization and resource allocation. 


Billy Jernigan

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Billy Jernigan

Billy Jernigan is managing director, leading insurance engagements, and associate director in the financial services practice of SSA & Co., a global management consulting firm. 

Trump Tariff Uncertainty Will Whack Insurers

The "will-he-or-won't-he" debate has created an "uncertainty economy" that will raise costs for insurers no matter what Trump ultimately decides.

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tariff wrecking ball

Just when it seemed that auto insurers had caught up with the surge in car prices that followed COVID-19's disruptions, what I'm calling "the uncertainty economy" has tossed a hand grenade into the industry. Even if everything about U.S. tariff policy became crystal clear tomorrow — and it won't — major damage has already been done. 

The president's threats of double-digit tariffs on all imports, followed by all the waffling on what he'll do and when, is freezing the world of business. What to invest, where to invest, whom to hire, where to hire, what geographies to sell into: All those decisions depend on the rules of the road, and nobody knows what they are.

Trump could, of course, clear up the situation by disavowing his bold tariff plans entirely. The stock and bond markets are certainly signaling that he should. But backing away from tariffs would be an unfathomable loss of face.

Anything short of a total repudiation of a transition to a tariff-based economy will leave us in this uncertainty economy. Prices for cars and parts will float higher even before underlying costs increase, simply because of the possibility that Trump's tariffs could make prices soar. Prices for lumber and other imported supplies that are major factors in home insurers' replacement costs will climb, too. Insurers will face losses on policies currently in force, and already restless policyholders will be shocked when they see what a new policy or a renewal will cost them. 

And those are just the disruptions happening in the near term. If Trump follows through on the extreme form of his tariff plan that he often broadcasts, he will reorder the global economic system that has existed for the past century, in the process disrupting for years the supply chains that insurers rely on. 

To sort through all the variables here, I sat down on Friday with Michel Leonard, the chief economist at the Insurance Information Institute.

Michel says the current situation should have some parallels with the COVID-19 pandemic, given the effect it had on supply chains, especially for autos. 

"For motor vehicles, we will inevitably see a significant drop in underlying growth, even if tariffs are suspended indefinitely, because the uncertainty is already present," Michel said. "During COVID, replacement costs for motor vehicles — both personal and commercial — rose around 60%, largely due to used auto prices. I could see this happening again, following a similar timeline.

"Ultimately, I believe we'll... end up with a cosmetic renegotiation — remember, the current North American free trade agreement was negotiated by President Trump. The real question is timing — whether it takes one month, three months, or six months. Insurance underwriters and industry professionals should be prepared for increases that could approach those COVID-era numbers for motor vehicles, the longer this situation persists.

"And these double-digit increases in tariffs on lumber, auto parts and other materials don't just mean higher replacement costs – they mean many materials aren't available through the supply chain."

Michel said he remains an optimist about the economy, in general — though, given how fast things have been changing, we noted for this version of our quarterly chats that we were speaking at 1pm EST on Friday, March 7. 

"As of this first quarter, I believe we still have enough GDP momentum to weather what we're seeing now, even if these conditions continue for a full quarter," he said. "However, the risk of GDP contraction rather than growth is certainly present.... My current concern is market sentiment."

He sees inflation "hovering between 2.5% and slightly above 3%" but says the Fed won't be able to do much about inflation driven by tariffs. "Higher interest rates can drive down demand," Michel says. "However, when inflation is driven by tariffs or other factors related to consumption issues, raising interest rates doesn't work."

He cautions that other countries seem to be taking Trump's threats much more seriously and reacting much more negatively than they did during Trump's first term, even though much of his language about trading partners is similar. 

"I've been shocked by how seriously other countries are taking these developments on protectionism," Michel said. 

When I asked if he saw any historical parallels to Trump's attempt to reverse a century of increasingly free trade, Michel said:

"What we're facing is potentially comparable to Margaret Thatcher's first two years as prime minister in the U.K. [which began in 1979]. When Thatcher came in, she cut government spending, privatized industries, and moved extremely rapidly. She was actually on track to lose her position until the Falklands War came about and allowed her to recover politically. During this period, the U.K. experienced one of the deepest recessions since World War II.

"That's the kind of economic pain we could be talking about here, but potentially on an even larger scale because it's the U.S. The agenda we're seeing now is even more transformative."

He says there is the potential for surging unemployment and a major market correction. 

"I haven’t given up," Michel says. "I’m still an optimist. But the potential implications go far beyond simple replacement costs, and that's what makes this situation particularly challenging."

I'll add that I believe Trump will have to back off his tariff plan. He just doesn't have the support for it at any level outside of the circle he controls in Washington, DC. 

His rich backers during the campaign certainly heard him talking about putting tariffs on every country, but they say they thought he was either blustering or was simply staking out an extreme position to gain an edge in negotiating. 

Right-wing economists don't support him. The Wall Street Journal editorial page has run headlines about Trump's "Dumb Tariffs" and "Dumbest Possible Tariffs." Even Stephen Moore, a longtime ally whom Trump once nominated for a seat on the Fed, said recently that the tariffs aren't a good idea for now

Polls show that the public at large mostly dislikes tariffs... and that's just at the theoretical level. To the extent that tariffs are imposed, they will raise prices and cause supply disruptions and bring the costs home to people. Trump's supporters argue that tariffs will encourage manufacturers to move production to the U.S., and that's surely true, at least to an extent, but it takes an awful lot longer to build a plant and staff it up than it does to raise a price. Besides, the U.S. isn't the only country that can raise tariffs; the U.S. will lose overseas markets as other countries retaliate. In any case, I don't see how Trump can sustain support for tariffs for many months or even quarters while waiting for any benefits to kick in. 

He's certainly winning the public relations battle at the moment. He's benefiting from the normal surge of enthusiasm from supporters in the early days of a term. He's also unleashed a barrage of appearances on television, and he's benefited from the stunning pace of activity by Elon Musk and DOGE to cut government programs that Trump supporters dislike. But I think that PR wave is cresting. 

DOGE has had to back off on many of its cost-cutting claims. Cabinet members are pushing back on Musk's slash-and-burn tactics when their departments are involved. Judges are ruling in some cases that Musk has overstepped the bounds set by the Constitution. Musk himself has lost some of the Iron Man mystique now that Tesla, the main source of his wealth, has seen its stock price fall 50% since its post-election peak. And, increasingly, we'll all see what the DOGE cuts do to service at federal agencies, to recipients of the aid that is no longer being provided, to the tens of thousands or hundreds of thousands who have been fired (many of them Trump voters) and so on. 

If someone doesn't pay their mortgage one month, they may save a few thousand dollars, but that's just the first part of the story. So far, we've just seen claims about the DOGE savings. Some will be welcomed, at least by Trump voters, but some will not. There is another part of the story coming. 

And, of course, the stock market has been plummeting. The Dow Jones Industrial Average is down 2,700 points since Feb. 19, or 9.4%, including a nearly 900-point drop on Monday, almost entirely because of the uncertainty about Trump's trade war and the related possibility of a recession. The market is maybe the most important point, because Trump seems to care deeply about how the stock market reacts to him.  

But how does this all end? I simply don't know. I'm quite sure the trade war isn't sustainable politically, but I don't see how Trump can back away. 

We'll just have to wait and see. In the meantime, we'll have to keep swimming in all the uncertainty.

Cheers,

Paul 

 

Strategies for Meaningful Healthcare Reform

Healthcare reform's cost burden shifts to employer plans, driving the need for innovative solutions on pricing and risk allocation.

Man in Gray Sleeveless Shirt Riding Bike

Fifteen years after landmark health reforms were introduced under the Affordable Care Act, the financial strain on employer-sponsored healthcare plans remains significant. Reforms primarily expanded coverage through taxpayer-subsidized programs like Medicaid and public exchanges. Those actions shifted costs both directly to taxpayers and indirectly to participants in employer-sponsored plans—leading to increased premiums, higher deductibles and growing employer burdens.

Looking ahead, sustainable solutions must address persistent inefficiencies and create a more accurate distribution of healthcare costs. By embracing innovative approaches such as reference-based pricing (RBP) with participant representation, rethinking risk allocation with safeguards against excessive out-of-network charges and balance billing, employers can reduce costs while maintaining quality coverage.

The Persistent Challenges of Health Reform

Health reform aimed to increase access and affordability. However, employer-sponsored plans—covering more than 160 million Americans—bear an ever-increasing share of healthcare costs incurred elsewhere under taxpayer-subsidized programs:

  • Premium Increases: Employer-sponsored plan premiums surged over 50% in the last 12 years.
  • Higher Deductibles: Plans with deductibles exceeding $2,000 tripled, from 10% to 32%.
  • Cost Shifting: Covered charges for employer-sponsored plans average over 250% of Medicare rates for identical services, by the same provider, at the same facility.

Meanwhile, publicly subsidized coverage expanded rapidly. Medicaid enrollment increased from 54 million in 2010 to 91 million in 2022, a 68% rise. Public exchange enrollment now stands at 24 million, with over 90% receiving taxpayer subsidies.

While these programs helped reduce the uninsured rate from 48 million to 25 million, they introduced taxpayer burdens by employers and their workers in the private sector—contributing to annual deficits and our $36 trillion national debt. Almost all of the cost of providing access through the exchanges and Medicaid was shifted to employers, workers and taxpayers—today and tomorrow.

Rethinking Health Reform: Toward a Sustainable Model

True reform requires addressing inefficiencies and creating a balanced framework that allocates risk more equitably among individuals, employers and society. Below are key areas of opportunity:

1. Transparency and Reference-Based Pricing

One of the most pressing issues is the lack of pricing transparency, which enables medical overbilling and excessive charges. Employer-sponsored plans often pay inflated rates because of opaque pricing structures. RBP offers a promising solution:

  • How It Works: RBP sets reimbursement rates for medical services based on a reasonable percentage (e.g., 120%-150%) of Medicare rates.
  • Benefits: Employers can reduce costs by aligning pricing with those rates, while participants benefit from lower out-of-pocket expenses.

To succeed, RBP requires robust participant advocacy to help individuals navigate disputes with providers. Employers should partner with third-party administrators (TPAs) and auditors to ensure accurate pricing and billing, safeguarding participants from excessive out-of-network charges and balance billing. This empowers participants while reducing unnecessary costs.

2. Innovative HSA-Capable Coverage Options

High-deductible health plans (HDHPs) paired with Health Savings Accounts (HSAs) are another avenue for cost control. HSAs encourage consumerism in healthcare by giving participants financial responsibility for their choices:

  • Consumer Engagement: Participants with HSAs are more likely to shop for cost-effective services, reducing overall spending.
  • Tax Advantages: Contributions to HSAs are tax-free, and funds roll over year-to-year, helping participants save for future medical expenses.

"High deductible" is actually a misnomer, because the required minimum deductible to be eligible to contribute to HSAs is less than today's average deductible for employer-sponsored coverage.

Employers can enhance adoption and worker preparation for out-of-pocket expenses by offering HSA matching contributions, integrating HSAs with wellness programs and providing tools to help employees make informed decisions about healthcare utilization.

3. Allocating Risk Equitably

A sustainable healthcare system must balance individual responsibility with societal safety nets:

  • Individual Risk: Individuals should bear financial responsibility for healthcare costs tied to preventable conditions or risky behaviors. This provides incentives for wellness and preventive care.
  • Societal Risk: For catastrophic expenses (e.g., costs exceeding $25,000 a year), society should assume responsibility through broad-based stop-loss or reinsurance mechanisms.

This model would not only promote healthier behaviors but also improve financial resiliency when managing anticipated and unforeseen medical expenses.

Challenges From Cost Shifting and Policy Decisions

Despite well-intentioned reforms, policy decisions have unintentionally worsened cost disparities in healthcare. Legislation such as the Inflation Reduction Act will increasingly shift prescription drug costs from Medicare beneficiaries to employer-sponsored plans.

Additionally, significant gaps in reimbursement rates between Medicare and Medicaid exacerbate these challenges. Medicare rates are approximately 40% higher than Medicaid, while employer-sponsored plans often pay as much as 300% of Medicaid rates for identical services, disproportionately affecting employers and employees alike.

Reforms have successfully increased access by reducing the cost of coverage for those enrolled in taxpayer-subsidized programs. Comparatively, the new mandates, taxes, and other requirements have increased the cost of employer-sponsored plans. Rising premiums and deductibles have eroded wage gains, leaving many workers without meaningful financial relief.

Empowering Participants Through Behavioral Economics

Behavioral economics provides powerful tools to enhance decision-making and reduce inefficiencies in healthcare. One such approach is default enrollment, including both enrollment in coverage and enrollment in and contributions to a Health Savings Account.

Another effective measure is the use of advanced explanations of benefits (EOBs), which offer clear, upfront cost estimates to participants. These estimates empower individuals to make more informed choices, reducing the likelihood of surprise medical bills.

Comprehensive reporting tools that track claim status, disputed amounts and recovery rates can further enhance transparency, providing participants and employers with actionable insights.

Additionally, employers can play a vital role by investing in educational initiatives that improve healthcare literacy. By helping participants better understand their coverage options and the implications of their healthcare decisions, these programs foster greater engagement and more thoughtful healthcare utilization. Together, these behavioral economics strategies create a more efficient and participant-centered system.

Charting the Path Forward: Strategies for Meaningful Healthcare Reform

The past 15 years have made it clear that incremental reforms are insufficient to overcome the systemic challenges posed by rising healthcare costs. To create meaningful change, employers, policymakers and participants must adopt innovative solutions that target inefficiencies and promote equity within the system.

A key priority is the adoption of transparent pricing models (such as RBP with participant representation), safeguards against overbilling and support through customizable reporting tools.

Post-payment reviews and recovery processes help mitigate these billing challenges by identifying and recovering overpayments made to medical providers, correcting billing errors and recouping funds.

Expanding the use of Health Savings Accounts (HSAs) fosters consumer-driven healthcare, lowering costs while improving financial preparedness for participants.

Rethinking risk allocation is equally critical, as a balanced approach to sharing responsibilities between individuals and society builds a more resilient system.

Addressing these challenges demands swift and decisive action. Employers and policymakers can create a healthcare framework that reduces financial strain, enhances efficiency and ensures equitable access for all stakeholders. This can be achieved by embracing strategies that involve collaboration with TPAs, stop-loss carriers and cost-containment companies, while leveraging data-driven insights.

The time to act is now. Employers and policymakers must lead the charge toward meaningful reform.


Jack Towarnicky

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Jack Towarnicky

Jack Towarnicky is an ERISA/employee benefits compliance and planning attorney with over 40 years of experience in human resources and plan sponsor leadership roles. 

This includes 25 years as the leader of a Fortune 100 corporation’s benefits function.