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Re-Engineering Insurance Solutions: Build vs Buy in the Age of AI and Automation

The insurance industry is undergoing one of the most profound shifts in its history. Competition is no longer defined by products or pricing alone but by how intelligent systems can think, connect, and evolve. Modernization has become inevitable; what remains uncertain is how insurers choose to achieve it.

Some carriers are rebuilding their digital core from within, investing in bespoke architectures that mirror their legacy workflows and preserve institutional knowledge. Others are adopting ready-made platforms that offer scalability, regulatory compliance, and faster deployment cycles. Each route leads toward modernization but demands a fundamentally different philosophy of control, ownership, and pace.

This divergence has created a defining question for the modern insurer: Should the intelligence that drives the business be owned or outsourced? Building modern insurance solutions internally offers complete sovereignty over data, integration, and differentiation. Buying, on the other hand, accelerates innovation but introduces new dependencies and limits flexibility.

Artificial intelligence and automation have intensified this decision. Claims can now be processed in minutes, underwriting models to adapt in real time, and predictive analytics to redefine how policies are priced. However, beneath these advances lies a deeper strategic dilemma. According to McKinsey, insurers that align technology investment with long-term business outcomes—rather than reacting to platform trends, achieve up to 20% higher revenue growth than their peers.

The path forward is not about adopting technology for its own sake. It is about understanding what to build, what to buy, and how to orchestrate both without losing strategic intent.

What’s fueling the Build vs Buy Debate in Insurance?

The “build versus buy” dilemma isn’t new in insurance. What’s changed is the urgency and complexity driving that decision. The industry no longer moves at an annual policy cycle pace, it moves at the speed of algorithms.

Legacy systems, once the operational backbone, are now the biggest constraint. They struggle to support real-time analytics, omnichannel claims, and regulatory agility. Meanwhile, new compliance mandates, digital-native competitors, and customer expectations are reshaping what modernization truly means for insurers.

Consider the contrast across segments. In Property & Casualty (P&C), predictive models and claims automation are now baseline capabilities. Mid-sized carriers that once relied on manual workflows are competing against popular wholesalers, and MGAs transitioned to Insurtech for near-instant settlements.

In Life and Annuities, AI-driven underwriting models assess risk within seconds, learning continuously from contextual data — a sophistication most legacy architectures cannot emulate.

To visualize these mounting pressures, Exhibit 1 highlights the dominant forces influencing insurers’ modernization paths. Legacy constraints and regulatory velocity emerge as the most critical pressures, while AI, automation, and talent shortages exert moderate yet persistent influence.

Exhibit 1 – Forces Driving the Build vs Buy Decision

Ultimately, this debate extends beyond procurement or technology preference. It represents a strategic identity choice — whether insurers view technology as an asset to own or an intelligence layer to orchestrate. How they answer that question will define their competitiveness in the decade ahead.

The Build Approach — Creating Insurance Solutions In-House

Three years into a modernization program, a European P&C carrier found its custom-built underwriting engine only half complete. The intent was right, to own the algorithm that powered its core differentiation, but execution proved costly. Fragmented legacy data, changing compliance mandates, and the shortage of AI engineers stalled progress. The carrier eventually retained its proprietary risk model but embedded it into a vendor platform to recover speed.

This example reflects a common reality: insurers build when they want control, but control alone rarely ensures agility. Building modern insurance solutions internally requires not just capital but also a sustained operating model that aligns technology, governance, and talent.

Why Insurers Choose to Build and How It Works in Practice

  1. Workflow Fidelity through Custom Design
    Internal teams map underwriting, claims, and policy lifecycles before writing code. Every module is designed to preserve established workflows and avoid business disruption — a key priority for mature insurers that cannot afford downtime.
  2. End-to-End Data Governance
    Building in-house lets data teams define how information flows across systems. Encryption, storage, and lineage controls are architected internally to ensure compliance across jurisdictions. This is critical when dealing with multi-market operations where data residency laws differ.
  3. Embedded Differentiation
    When pricing engines or fraud-detection algorithms are part of a carrier’s IP, internal development enables tighter coupling between business logic and technology. It’s how large multiline insurers convert underwriting expertise into a digital advantage.
  4. Integration Control
    Proprietary architectures allow direct connection to CRMs, finance systems, and analytics layers. This flexibility helps avoid vendor roadmap delays — a frequent pain point for carriers that evolve faster than their software partners.

Where the Model Breaks Down: The ‘How’ Behind the Gaps

  1. Engineering Bandwidth and Cost Overruns
    Each new compliance update or feature request goes through internal backlog cycles. This slows release velocity and stretches IT budgets, particularly when teams depend on external contractors for specialized modules.
  2. Complexity of System Interlinkages
    Integrating new builds with policy admin, claims, and reinsurance modules often leads to dependency loops. Even minor code changes require full regression testing, increasing both risk and time-to-market.
  3. Talent and Knowledge Continuity
    When key engineers leave, institutional knowledge walks out with them. Few insurers have versioning discipline or documentation maturity equal to tech firms, resulting in rebuilds that start from scratch every few years.
  4. Operational Debt
    Over-customization eventually limits flexibility. Each local tweak becomes a long-term maintenance commitment, making modernization efforts heavier, not leaner.

Building in-house, therefore, is less about coding software and more about building capability ecosystems. It suits insurers that can operate like technology firms with continuous release pipelines, product ownership culture, and governance that anticipates rather than reacts. Without these foundations, the build approach often transforms a strategic advantage into a structural constraint.

The Buy Approach: Leveraging Existing Insurance Solutions

In 2022, a mid-tier life insurer in Southeast Asia replaced its legacy policy administration system with a modular SaaS platform. Within eight months, the company had digitized new policy onboarding, automated claims validations, and integrated compliance reporting. What once required 60 manual touchpoints now took 10? Yet, six months later, the insurer’s CIO raised a new challenge — data portability and customization limits. The platform scaled beautifully, but its rigidity left little room for product innovation.

This experience captures both the promise and the paradox of buying insurance solutions. For many carriers, adopting third-party or SaaS systems is the fastest route to modernization. Vendors deliver pre-built modules, tested compliance frameworks, and continuous upgrades that align with evolving regulatory landscapes. However, outsourcing core system intelligence also means accepting new dependencies and shared control over the pace of innovation.

Why Insurers Choose to Buy and How It Works in Practice

  1. Speed of Deployment
    SaaS and cloud-native solutions allow go-live timelines within months rather than years. Insurers typically follow a phased rollout — beginning with non-core modules like claims intake or billing — to minimize risk before scaling enterprise-wide.
  2. Predictable Cost and Scalability
    Subscription-based pricing models convert capital expenditure into operating costs. CFOs can forecast IT budgets with precision, and scalability is often achieved instantly through vendor-managed infrastructure.
  3. Continuous Upgrades and Regulatory Alignment
    Vendors automatically release compliance and security updates, ensuring readiness for IFRS 17, Solvency II, and data protection mandates. This reduces the internal monitoring burden and audit overhead.
  4. Ecosystem Integration
    Modern SaaS platforms are API-first. They integrate easily with third-party analytics, payment gateways, and reinsurance networks — enabling insurers to assemble connected ecosystems rather than monolithic systems.

Why It Often Falters: The Operational Reality Behind the Trade-offs

    1. Limited Customization Flexibility
      SaaS systems are designed for broad market applicability. Carriers seeking product-level differentiation often hit configuration ceilings, where deeper customization incurs additional costs or delays in vendor approval.
  1. Vendor Dependency and Contractual Rigidity
    Service-level agreements dictate release schedules and data access rights. Insurers may face “vendor lock-in,” in which exiting the ecosystem requires rebuilding integrations from scratch.
  2. Data Portability and Ownership
    transferring historical data or merging datasets for analytics can be constrained by proprietary schemas. This complicates migration to alternate platforms later.
  3. Integration Gaps and Legacy Coexistence
    During hybrid transitions, legacy modules often continue to coexist with SaaS layers. Without meticulous integration, governance, data reconciliation, and process alignment become recurring pain points.
  4. Hidden Change Management Costs
    User training, workflow redesign, and stakeholder adoption require as much investment as the platform itself. Many insurers underestimate this human-operations layer, leading to slow utilization and project fatigue.

The buy approach is therefore most effective for insurers who prioritize speed, compliance, and scalability over deep architectural control. It enables modernization without heavy internal engineering investment — but also introduces a dependency rhythm that must be actively governed, not passively accepted. Successful adopters treat vendor management as a strategic discipline, ensuring agility is not achieved at the expense of autonomy. Let’s see an overview of build vs buy in exhibit 2:

Exhibit 2: Build vs Buy Comparison Matrix

Parameter Build (In-House Development) Buy (SaaS / Third-Party Platform)
Deployment Speed Longer timelines due to design, testing, and integration cycles Go-live in months through pre-built modules
Cost Structure High upfront investment; continuous maintenance cost Predictable subscription pricing; lower initial outlay
Data Control Full ownership and governance over proprietary data Shared access and vendor-managed compliance
Customization Flexibility High; architecture aligns closely with internal workflows Limited; restricted to platform configuration options
Compliance Agility Manual updates for new regulations and frameworks Automated upgrades and built-in compliance tracking
Scalability Requires engineering resources to expand infrastructure Instantly scalable through cloud-native architecture
Innovation Velocity Dependent on internal development capacity and talent Driven by vendor roadmap and release cycles
Risk Exposure High operational and delivery risk Shared risk with vendor under service-level agreements

The Hybrid Middle Path

For many insurers, the choice between building and buying has evolved into something more nuanced — a hybrid operating model. Instead of anchoring entirely on in-house or external solutions, insurers now design architectures where proprietary systems coexist with vendor platforms. The result is a balance between control and speed, building what defines them and buying what accelerates them.

In 2023, a global composite insurer adopted this hybrid philosophy while modernizing its claims and policy administration systems. It retained its proprietary risk and pricing engine but integrated it with a cloud-based claims platform. The insurer now owns the logic that differentiates its products while leveraging the vendor’s ready-made automation and compliance modules. This mix reduced development effort by 45 percent and cut claim turnaround times by nearly 30 percent within a year.

How the Hybrid Model Works

  1. Modular Architecture Design
    Core intelligence layers, pricing, underwriting rules, fraud detection are built in-house, while peripheral layers such as claims intake, billing, and document management are sourced externally.
  2. API-Driven Interoperability
    Integration through API gateways allows seamless communication between internal and external systems. This ensures data integrity while keeping technology stacks loosely coupled and easier to scale.
  3. Selective Ownership of IP
    Insurers identify which parts of their technology stack directly contribute to market advantage and retain ownership there, outsourcing non-differentiating functions.
  4. Adaptive Governance Frameworks
    Hybrid models require stronger vendor oversight, joint release calendars, and clear accountability structures to prevent dependency sprawl.

Before deciding which layers to build or buy, insurers often map their technology stack against ownership value and operational agility.

Exhibit 3 illustrates a typical distribution in a hybrid model and the rationale behind each decision.

Exhibit 3: The Hybrid Model in Action

Component Built In-House Bought / Outsourced Key Benefit
Core Underwriting Engine ✅ Full control over pricing logic and proprietary IP
Claims Management Platform ✅ Faster deployment and automation at scale
Data Warehouse / Analytics Layer ✅ Centralized intelligence and predictive insights
Policy Administration System ✅ Vendor-managed compliance and scalability
Customer Portal and UX Partial Partial Custom branding with shared framework flexibility

The hybrid model reflects a pragmatic maturity. Insurers no longer see modernization as a binary choice but as a portfolio of ownership decisions. What matters most is not who builds the technology, but whether every system — built or bought — advances a unified business objective.

The Decision Framework: Questions That Matter

Choosing whether to build, buy, or blend insurance solutions is ultimately a strategic design decision — not a technical one. The most successful insurers approach it through a structured diagnostic lens, asking a few non-negotiable questions before committing capital or capability.

The framework below helps insurers evaluate which path aligns best with their goals, resources, and risk appetite.

1. What Problem Are You Trying to Solve?

Every modernization initiative begins with a business pain point — not a technology gap.
If the challenge lies in slow claims turnaround, the solution may rest in workflow automation or in document processing tools that can be easily bought off the shelf.
If the issue is inaccurate risk assessment, the answer may involve proprietary analytics models that must be built internally to preserve a competitive edge.

Framing the problem correctly helps avoid the common trap of adopting technology for its novelty rather than its relevance.

2. Which Path Solves the Problem Without Adding Risk or Complexity?

Modernization often introduces hidden dependencies — compliance exposure, third-party integrations, or resource strain.
Insurers should assess each route for operational friction:

A simple rule of thumb: the right path reduces risk faster than it adds features.
According to PwC’s 2024 Digital Insurance Outlook, nearly 60 percent of modernization failures stem from underestimated integration complexity — not software limitations.

3. What Is the Cost Difference Between Building and Buying?

The total cost of ownership extends far beyond initial investment.

The table below illustrates how insurers typically evaluate financial implications across both models.

Exhibit 4: Comparative Cost Considerations

Initial Investment High (development, infrastructure) Low (subscription or license)
Maintenance Continuous internal resources Vendor-managed upgrades
Customization Fully controlled; resource-heavy Limited; often chargeable
Integration Complex with legacy systems Easier via standardized APIs
Long-Term ROI Higher if adoption succeeds Faster but capped by vendor roadmap

4. Do You Have the Internal Expertise to Sustain What You Build?

Even the most sophisticated platform fails without capable stewardship.
Building in-house requires sustained engineering talent, architecture governance, and iterative release cycles. Many insurers underestimate the cultural shift required to operate like a software company.

In contrast, buying transfers some of that responsibility to vendors — but it also means ceding direct control over innovation velocity.

The right approach depends on whether the insurer’s core strength lies in technology execution or in product and market strategy.

A mature insurer’s modernization of roadmap is rarely defined by a single answer. It’s a balance of these four diagnostic dimensions — business need, risk profile, financial model, and capability depth.
The objective is not merely to pick between build or buy, but to design an ecosystem that scales intelligently without compromising strategic control.

Future Outlook — Flexibility as the Ultimate Solution

The insurance industry’s modernization journey no longer revolves around a binary choice. The future belongs to insurers who can adapt their technology to posture as markets, risks, and regulations evolve. Flexibility — not architecture — will define resilience.

In the coming years, AI, automation, and data ecosystems will continue to blur the lines between building and buying. Insurers may build today and outsource tomorrow or integrate vendor modules now and reclaim ownership later through customization. The real differentiator will not be the system itself but the fluidity with which insurers reconfigure it to match emerging realities.

Global research has already reflected this shift. McKinsey’s 2024 Digital Insurance Study notes that over 70 percent of top-performing insurers now operate within modular, API-first architectures — combining in-house intelligence with vendor-driven scalability. This model enables them to swap components without disrupting the enterprise core.

To stay ahead, insurers must view modernization as an ongoing design discipline, not a one-time project. Governance teams will need to make continual decisions about what to own, what to rent, and what to retire — ensuring that technology evolves in lockstep with business ambition.

Ultimately, whether built or bought, the strength of any insurance solution lies in how seamlessly it aligns with the organization’s purpose. The winners will be those who see technology not as an endpoint but as a living ecosystem — one that learns, scales, and transforms along with their enterprise vision.

Whichever path you choose, the strength of your insurance solution will always depend on how seamlessly it serves your core business goals.

The Operational Problem Holding Back Insurance Agency Growth

Why do small insurance agencies stall as they scale, even when demand is strong and teams are capable?

In the early years, growth remains manageable because familiarity compensates for the absence of formal structure. Account Managers carry a working knowledge of their books. Client history is scattered across inboxes, shared folders, and informal notes, and most gaps are addressed as work progresses. Volume is low enough for correction to occur without visible consequence.

As the agency expands, this operating model begins to strain.

Consider a small P&C insurance agency. A commercial submission arrives in the inbox and is routed to an Account Manager. The ACORD forms appear complete during the initial review, and data entry begins in the insurance agency management software (AMS). As the process moves forward, missing exposure details and an absent supplemental form surface, forcing intake to pause while clarifications are obtained. The interruption is routine and rarely escalated, as nothing appears overtly broken.

What changes over time is not the nature of the work, but the cumulative impact of volume on the operating model. Each additional account increases documentation, servicing activity, and carrier interaction, while the underlying workflows remain unchanged. The same processes that once absorbed small corrections now require repeated intervention.

Growth begins to stall not through a single failure, but through missed renewals, accumulating backlogs, and reactive staffing decisions.

At the center of this slowdown lies a structural absence. The agency has scaled volume without establishing a way to consistently govern how work enters the organization and progresses as complexity increases.

This gap remains unnoticed for long periods because individual effort masks it. Scale eventually exposes it.

The Overlooked Operational Constraint for Agency Management Growth

As the agency management grows, daily operations begin to absorb a level of complexity the original workflow was never designed to manage. Account volume increases, servicing activity expands, and documentation accumulates steadily. Yet the internal processes guiding this work were designed for earlier conditions and now carry a significantly heavier operational load.

Pressure does not build in a single area. It emerges simultaneously across multiple dimensions of the operation, as illustrated in Exhibit 1.

Exhibit 1: Areas Where Operational Pressure Builds as Agencies Scale

Area What Increases Operational Impact
Data Volume Exposure schedules, underwriting details, and client information entered in inconsistent formats Loss of control, repeated validation, delayed processing
Documentation Load ACORDs, endorsements, loss runs, renewal files, and supplemental forms Lack of uniform structure, slower retrieval
Task Volume Submissions, follow-ups, certificates, bind requests, and carrier inquiries Higher daily workload, dependency on manual effort

Each of these pressures appears manageable in isolation. When they accumulate, however, agency management owners begin to experience a level of operational strain that the original workflow cannot absorb. The team expends more effort, yet throughput does not increase proportionately.

At this point, the slowdown is not caused by volume alone. It reflects a structural absence. The agency has expanded without establishing a consistent way to govern how work enters the operation and progresses as complexity increases.

How Operational Control Is Restored as Agencies Scale

As agencies grow, sustaining performance requires more than individual effort or familiarity with accounts. What becomes necessary is operational control that governs how work enters the organization, how it moves across roles, how systems interact, and how documentation is maintained over time.

Without this control, execution depends on manual intervention and individual judgment. With it, work progresses through a defined structure rather than personal workarounds.

The nature of this control becomes evident when examining the areas it must regulate.

When these controls are absent, intake delays become routine; duplication increases, manual reconciliation expands, and document retrieval slows. These are not separate issues. They are different expressions of the same underlying absence of operational control.

This form of control rarely exists in small agencies. Early growth relies on convenience, informal actions, and individual knowledge. These approaches function at low volume and do not appear risky. They become constraints only when scales expose their limits.

The Impact: What Changes When Operational Control Is Established

With operation control, agencies do not experience a sudden leap in performance. The change is structural rather than dramatic. Day-to-day work stops depending on individual intervention and begins to move through defined controls. Information is available when required, tasks progress in sequence, and records remain accessible over time. The result is not speed in isolation, but operational stability.

The first change appears at the point where work enters the agency. Submissions, renewals, and service requests arrive with the required information in place. Intake no longer pauses mid-process, and downstream teams are not forced to correct incomplete inputs before work can proceed.

As this structure becomes embedded, its effects extend across the operation.

  1. Quoting and renewal cycles stabilize
    Progress no longer depends on individual familiarity with specific accounts. Renewals are initiated earlier, submissions reach carriers in usable form, and turnaround times become consistent across the team.
  2. Operational capacity expands within existing resources
    Account Managers and CSRs spend less time locating documents, validating data, or repeating entries across systems. Higher volumes are absorbed without extending work hours or adding staff reactively.
  3. Revenue continuity improves
    Renewal slippage declines, endorsements are processed accurately, and opportunities for additional coverage are easier to identify because account records are complete and up to date.
  4. Carrier interactions become more predictable
    Underwriters receive organized submissions supported by consistent documentation. Clarification cycles shorten, underwriting timelines improve, and working relationships stabilize.
  5. Management gains operational visibility
    Workloads, processing timelines, and constraints become visible. Growth decisions are based on observed capacity and process readiness rather than assumptions.

In practical terms, volume is converted into control. Growth no longer strains daily execution. It is absorbed within an operating structure designed to support it.

How Insurance Support World (ISW) Operates as a Scalable Middle-Layer Partner

Establishing an operational control layer requires sustained discipline across data intake, workflow governance, system coordination, and documentation management. For many small and mid-sized agency management owners, building and maintaining this discipline internally competes directly with daily servicing priorities. As a result, structural work is deferred in favor of immediate execution, even as scale continues to increase.

ISW addresses this gap by implementing and operating a system-governed middle layer built on its ADIS framework, supported by insurance-trained LLM models. This layer does not replace existing AMS platforms, rating tools, or carrier portals. Instead, it governs how workflows across them, enforcing consistency, validation, and control at scale. The objective is not to add tools or headcounts, but to impose structure and governance on operations that have grown organically.

ISW’s role centers on four operational responsibilities.

Designing and maintaining the execution backbone
Using ADIS framework, ISW defines how work is triggered, validated, routed, and completed across insurance workflows. Submissions, renewals, compliance checks, reporting, and financial operations move through system-enforced sequences rather than relying on individual memory or ad hoc intervention.

Establishing standardized workflow governance
Intake requirements, task sequencing, ownership, and handoffs are formalized through ADIS workflows. Existing systems such as AMS platforms, rating engines, carrier portals, email, and document repositories continue to be used, but operate under consistent rules that reduce fragmentation, duplication, and manual reconciliation.

Embedding data and documentation discipline
Insurance-trained LLM models are applied within ADIS to extract, structure, and validate information from unorganized emails, ACORDs, PDFs, and supporting documents. Data accuracy and audit-ready documentation become enforced operating conditions rather than periodic clean-up efforts, supporting renewals, endorsements, compliance checks, and carrier interactions.

Enabling controlled and scalable growth
Because ADIS governs execution at workflow and data level, agencies are able to absorb higher volumes without scaling exceptions, rework, or operational noise. Quoting, renewals, servicing, compliance, and reporting proceed with greater predictability as finances expand.

The outcome is not operational complexity, but operational clarity. Many agency management owners function within defined controls, deliver consistent service, and maintain clean, well-governed operations that support sustained growth and acquisition readiness.

Conclusion

Scaling an insurance agency management is commonly treated as a question of volume. In practice, volume simply exposes the condition of the underlying operation. Where structure is limited, growth reveals inefficiencies. Where structure is in place, growth reinforces stability.

The middle layer is that structure. It determines how information enters the agency, how work progresses across roles, how systems interact, and how records are maintained over time. In its absence, complexity is managed through individual effort. In its presence, complexity is absorbed through defined controls.

If you are looking for the right agency management partner, Insurance Support World can help you. Get in touch with us today.

The Future-Ready US Insurance Broker: How AI Cuts Complexity and Boosts Growth

In 2025, insurance brokers are under pressure from every direction.
Manual processes, complex policy requirements, and constant regulatory changes are slowing operations. Profit margins are tightening, and clients now expect personalized quotes and answers within minutes. Add a shortage of skilled staff, and many brokerages are struggling to keep up—risking slower service, lost deals, and declining customer trust.

This is where AI for insurance brokers changes the game. It clears bottlenecks, such as scanning documents in seconds, analyzing risks with more precision, and turning data into decisions in real-time. The broker stays in control, without being overwhelmed by administration.

The goal isn’t to replace brokers. It’s to replace inefficiency, so you can quote faster, advice smarter, and grow without burning out your team or your margins. In a market moving this fast, the brokers who act now will lead. The rest will be catching up.

Before we get into exactly how AI delivers this edge, let’s take a closer look at the pressures US brokers are up against right now because solving these is where the real value lies.

What Challenges Do Traditional Insurance Brokers Face in the US Market?

US brokers aren’t just competing on service—they’re navigating one of the most complex regulatory and competitive environments in the world. The pressure comes from every side: carriers, clients, regulators, and new digital-first entrants.

  1. State-by-State Regulatory Maze
    Unlike many countries, US brokers deal with a patchwork of state regulations. Licensing requirements, continuing education rules, and policy form approvals vary by jurisdiction. Missing a single state-specific update can result in delays, fines, or even the loss of a license.
  2. Carrier and Commission Pressure
    Major carriers have been tightening commission rates and shifting to fee-based models. For mid-size and independent brokers, this means less buffer for inefficiencies. Losing a key carrier appointment or missing placement quotas can have an immediate impact on profitability.
  3. Talent Crisis in the Brokerage Workforce
    In the US, more than 25% of licensed insurance professionals are over the age of 55. Retirement rates are outpacing recruitment, and younger talent often prefers roles in fintech or insurtech. This leaves many agencies short-staffed during peak renewal seasons.
  4. Compliance and Documentation Load
    Between NAIC data privacy standards, CMS rules for health brokers, and Department of Labor fiduciary requirements for certain life and retirement products, compliance isn’t optional—and it’s getting more expensive. For many firms, compliance-related administrative tasks consume 15–20% of staff time.
  5. Client Expectations in the Amazon Era
    Commercial and personal lines clients alike expect quotes and bindable offers within hours—not days. Large national brokers with AI-powered quoting and CRM systems are setting this benchmark, forcing smaller firms to either match the speed or risk losing accounts.
  6. Rising M&A Pressure
    Industry consolidation is accelerating. Large brokerages, such as Gallagher and Brown & Brown, are acquiring smaller firms to increase their market share. Independent brokers that can’t compete on tech-enabled efficiency risk becoming acquisition targets—or worse, irrelevant.

Let’s validate our challenges with real-world numbers. Below report showcase that these challenges aren’t hypothetical.

US Market Trend– Big “I” 2025 Report
  • Independent agencies placed 61.5% of all P/C insurance in 2024 (down from 62.2% in 2023).
  • They control 87.2% of commercial lines written premiums and 39% of personal lines.
  • Average commission rate for all P/C lines: 11.5%, with wide variation by state and product line.
  • The market remains hard: 2024 saw $113 billion in insured catastrophe losses in the US (Aon).

How Does AI-Driven Insurance Broking Compare to Traditional Broking?

Think about a typical day for a US commercial broker handling a mid-sized client renewal:

  1. Traditional way: You spend half the morning rekeying ACORD form data into multiple carrier portals, chasing missing COIs, and double-checking policy terms. Then you wait for underwriters to send back quotes, often with questions that require even more paperwork.
  2. AI-powered approach: The client sends you the renewal packet. OCR ingests all documents in minutes, NLP maps key data into your AMS, and risk models run instantly. You review pre-filled, carrier-compliant submissions the same day and send quotes out in hours, not days.

The core truth: AI doesn’t replace brokers—it replaces inefficiency. Here’s the side-by-side analysis in the Exhibit 1:

Exhibit 1: Traditional vs AI-Powered Insurance Broking: A Side-by-Side Comparison

Function Traditional Broking AI-Powered Broking
Document Processing Manually entering data from ACORD forms, emails, and scanned PDFs. Error-prone and slow. OCR + NLP extract, validate, and upload data to AMS/CRM in minutes, reducing errors by up to 80%.
Claims Assessment Adjusters manually sift through images, notes, and forms—slowing settlements. Machine learning triages claims, flags anomalies, and suggests settlements 30–40% faster.
Customer Insights Limited to historical account data and occasional carrier reports. Real-time behavioural and coverage gap analysis to trigger cross-sell/upsell opportunities.
Risk Evaluation Based on static questionnaires and subjective judgment. Predictive models combine client history, industry data, and external risk indicators for precision.

When brokers see the before-and-after difference in their process, it stops being “new tech” and starts being a competitive edge.

How Does AI Simplify Complex Insurance Operations?

Let’s go back to our mid-sized Illinois commercial broker. Last year, their biggest pain points were renewal season backlogs, policy data scattered across systems, and clients frustrated by slow turnaround times. After adopting AI-powered tools, here’s how their operations changed:

  1. Fast, Accurate Data Intake
    Instead of spending hours rekeying client details into carrier portals, OCR now scans all renewal documents — ACORD forms, loss runs, certificates — and maps the data directly into their AMS. What once took two staff members a full day now takes less than 30 minutes?
  2. Smarter Risk Assessment
    Predictive analytics combine the client’s historical loss data with industry-specific trends. For a manufacturing client, the broker now receives AI-generated risk profiles highlighting coverage gaps and suggesting limits before the underwriter even reviews the file.
  3. Continuous Policy Optimizations
    Machine learning monitors claims frequency, endorsement usage, and client feedback. The system flags underutilized coverages and recommends adjustments at renewal, helping the broker improve retention rates and upsell relevant products.
  4. Proactive Client Engagement
    AI automates routine communication — from renewal reminders to claim status updates — allowing account managers to focus on advisory calls. The broker now touches each key account more frequently without adding headcount.
  5. Faster, Fraud-Safe Claims Processing
    Claims are automatically screened for anomalies before they reach the adjuster. In one case, the AI flagged inconsistencies in a property damage claim, allowing the broker to work with the carrier to verify details and avoid a costly payout error.

What Impact Can AI Have on an Insurance Broker’s Business?

For our Illinois commercial broker, AI adoption didn’t just speed up operations — it changed the economics of the business. Based on their experience and industry benchmarks, here’s what AI can deliver for US brokers:

These kinds of results can feel out of reach if you’re already stretched thin with day-to-day client demands. Many brokers like our Illinois firm ask the same question: “Where would we even find the time, tech skills, and resources to set this up?”

The answer, for them, wasn’t building an AI department from scratch. It was partnering with an AI-driven outsourcing provider. That decision gave them instant access to proven tools, trained specialists, and processes tailored to brokerage workflows — without the delays, trial-and-error, or expense of hiring permanent staff and managing large IT projects.

For many US brokers, this hybrid approach — keeping client-facing relationships in-house while outsourcing repetitive, high-volume back-office tasks to AI-enabled teams — is the fastest and lowest-risk way to achieve the same transformation.

Conclusion: The Brokers Who Move Now Will Win Later

The insurance market in 2025 isn’t slowing down client expectations, regulatory demands, and competitive pressure will only keep rising. AI provides brokers with the speed, precision, and scalability needed to thrive in this environment, but technology alone isn’t the answer. It’s about applying it in a way that works for your business, your team, and your clients.

The Illinois broker didn’t wait for the “right time”, they acted, and the payoff was faster quotes, happier clients, stronger compliance, and healthier margins. That same opportunity is available to every US broker willing to adapt.

Whether you build in-house capabilities or partner with an AI-driven outsourcing provider, the takeaway is clear: the brokers who integrate AI into their core workflows now will set the standard for service and profitability in the years ahead.

If you are looking for strategic partners who can help you digitalize your internal processes using advanced technologies, Insurance Support World is here to assist you.

The Future of Marine Underwriting: From Legacy Complexities to AI-Driven Precision

Marine insurance underwriting remains one of the most complex yet indispensable pillars of global trade. For U.S. insurers and brokers, it’s not just about insuring ships and cargo. It’s about protecting profit margins while steering volatile risks and intricate compliance regimes. Yet, underwriting operations are still hindered by legacy processes, manual document handling, and fragmented systems that drain efficiency.

This complexity is more than an operational drag; it’s a barrier to competitiveness. Outdated workflows expose insurers to compliance risks, margin leakage, and slower turnaround times in a market that demands speed and efficiency.

The opportunity ahead lies in digital-first transformation — powered by AI, real-time data integration, and smart automation — combined with underwriting policy support services that keep operations lean and compliant. Together, these approaches offer a path to operational excellence, enhanced accuracy, and sustainable profitability.

Why Marine Insurance Underwriting Is Uniquely Complex

Marine underwriting has never been straightforward. U.S. carriers operate under overlapping regulatory frameworks, such as the UK Marine Insurance Act of 1906, alongside American maritime rules, creating constant challenges for cross-border compliance.

The risks themselves are equally multifaceted: piracy in high-risk zones, rogue waves, mechanical breakdowns, and increasingly unpredictable climate events. Each factor influences pricing, claims volatility, and underwriting decisions.

Then comes the General Average principle, where cargo owners collectively share the costs of salvage operations, leaving shippers surprised with unexpected liabilities.

Additionally, open cover policies provide flexibility by attaching future voyages to a master policy. But they also make exposure management and premium adjustments a moving target, straining policy administration teams.

The Top Operational Challenges Facing Marine Underwriting Today

The path forward is a new operating model that integrates technology, data, and support services into a unified framework. To bring these capabilities into focus, exhibit 1 outlines the digital-first playbook for marine underwriting excellence.

Exhibit 1: The Digital Playbook for Next-Gen Marine Underwriting

Together, these challenges drive up costs, slow turnaround, and weaken competitiveness. For many U.S. insurers, the bottleneck isn’t risk knowledge; it’s the operational drag of underwriting processes that haven’t kept pace with modern demands.

The Current Technology Landscape — and Its Gaps

Marine insurers aren’t short on tools. Platforms like SICS, MARINS, and Sequel Marine manage core underwriting tasks, while AIR Worldwide and RMS models assess catastrophe risk. AIS tracking and satellite feeds provide vessel visibility, and blockchain pilots such as Insurwave have showcased transparency gains.

But these tools often operate in silos. Policy management systems rarely integrate with catastrophe models. Vessel data is collected but not embedded into underwriting workflows. Legacy platforms, such as Guidewire and Duck Creek, which are widely used in the U.S., still heavily rely on manual data handoffs. Blockchain, though promising, has yet to see mainstream adoption.

The result? A patchwork of technology that solves individual problems but doesn’t deliver end-to-end underwriting efficiency. Underwriters are left stitching together systems instead of focusing on risk analysis and decision-making.

A Digital-First Playbook for Marine Underwriting Excellence

The path forward is a new operating model that unites technology, data, and support services into one coherent framework:

For U.S. insurers, the takeaway is clear: digital underwriting transformation is no longer optional. But technology on its own isn’t enough. Success depends on combining these tools with specialized underwriting support services that manage the manual, repetitive, and compliance-heavy tasks. That’s what frees underwriters to focus on judgment, not paperwork.

Conclusion: A Future-Ready Operating Model

Marine insurance underwriting is too complex, too costly, and too critical to remain bound by legacy practices. For U.S. carriers, the imperative is clear: embrace AI-driven precision, integrate real-time data, and adopt underwriting support services that bring scale, compliance, and operational excellence.

Takeaway:

The insurers that succeed will be those who blend advanced technology with streamlined operational models. By doing so, they gain competitive agility, mitigate risk more effectively, and protect profit margins in an increasingly volatile market.

Marine insurers and brokers that partner with expert BPM providers, such as Insurance Support World (ISW), will be best positioned to build future-ready insurance models — resilient, compliant, and primed for growth.

How Surplus Lines Insurers Can Modernize Without Disruption

Surplus lines insurance is at a turning point. Facing intensifying regulatory oversight, volatile specialty risks, and rising demands for real-time responsiveness, traditional underwriting and servicing models are under strain. Insurers relying on fragmented processes and manual interventions confront escalating costs, compliance exposure, and slower go-to-market times. The pressure faced by surplus lines insurers are diverse, but several operational challenges are especially acute (see Exhibit 1).

Exhibit 1: Core Operational Pressures in Surplus Lines

Challenge Operational Impact Market Pressure/Industry Shift
Manual underwriting triage Slows quote-to-bind, drives up rework Brokers demand rapid, tailored responses as the E&S market expands
Fragmented workflows Creates data silos, inconsistent service, delays Rising regulatory oversight at state and federal levels
Compliance tracking gaps Increases audit exposure and risk of penalties Compliance complexity intensifies with new and shifting requirements
High processing costs Shrinks margins, limits reinvestment in growth Competitive pressure requires efficiency and scalability

What sets surplus lines apart? Surplus lines insurance covers risks that standard (admitted) carriers won’t insure—often because those risks are highly complex, newly emerging, or unique to a particular geography or industry. This distinct market role brings higher service expectations and ongoing operational scrutiny.

Today, however, the path forward is clearer than ever. Structured operational models are enabling surplus line insurers to better manage complexity, streamline compliance, and deliver the agility required in a rapidly changing market.

This article examines the operational landscape, highlights emerging solutions, and showcases outcomes achieved by forward-thinking insurers.

Quick Insight: Technology alone cannot fix broken processes. True operational gains require process-first integration, not just automation layered on legacy workflows.

Without integrated, future-ready processes, these pressures inevitably drive fragmentation, increase error rates, and undermine scalability.

Why “Structural Upgrades” Matter:

Point solutions—such as adding a compliance module or automating a single step—address symptoms, not causes. Lasting improvement demands a holistic rethink of the operational backbone: connecting workflows end-to-end, embedding regulatory intelligence at every stage, and designing processes that can adapt as markets evolve. Without such redesign, operational strain only intensifies.

Moving Beyond Quick Fixes: Rethinking the Surplus Lines Model

Recognizing that incremental upgrades rarely solve systemic operational strain, some leading surplus line insurers are taking a fundamentally different approach. Instead of simply layering new technology onto existing processes, they are rethinking their operating models from the ground up—integrating workflows, embedding regulatory logic, and prioritizing scalability.

For instance, consider a U.S.-based insurer managing over $1 billion in annual premiums. Despite recent investments in quoting and document automation, the firm continued to face persistent operational bottlenecks:

The solution wasn’t more automation but a redesign of the operational backbone. By mapping each step of the intake, triage, underwriting, compliance, and servicing processes, as well as benchmarking for turnaround times, error rates, and compliance lags, this insurer was able to identify and address the structural drivers of inefficiency.

Key levers in this transformation included:

  1. Reengineering workflows: Eliminated redundancies, enabled parallel processing, and established a single source of operational truth across teams.
  2. Embedding regulatory logic: Integrated compliance directly into each operational stage, ensuring real-time adherence to evolving requirements.
  3. Applying an adaptive, digitally intelligent framework: Orchestrated automation, domain expertise, and process design to manage surplus lines complexity at scale.

This process-first redesign delivered rapid, structural gains across the insurer’s core business metrics. The result? Rapid, measurable gains, as shown in Exhibit 2

Exhibit 2: Quantifiable Impact—Results from the First 12 Months

Operational Metric Before Redesign After Redesign % Improvement
Claims Resolution Legacy, fragmented data Centralized sharing, faster closure +30%
Policy Rework Rate Manual, high error frequency Rules-driven, automated exceptions –35%
Cost per Transaction High, fragmented processes Unified workflow, fewer touchpoints –20% to –30%

What’s the Next Chapter for Surplus Lines?

As the surplus lines market continues to grow in both complexity and importance, carriers will need more than incremental fixes to stay ahead. Those that treat operational clarity not as a technical upgrade, but as a strategic discipline, will redefine what’s possible in underwriting, compliance, and client experience.

What distinguishes tomorrow’s leaders?

The playbook is shifting: Operational transformation is no longer about keeping pace; it’s about setting the pace.

For surplus lines insurers, the mandate is clear:

Invest in the integration operations now—and build resilience, agility, and confidence into every layer of your business.

If you’re rethinking your approach to operational excellence, let’s start a conversation about what structured transformation could look like for your organization.

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