May 22, 2026
AI in Insurance: Building Production-Ready Products for Claims, Underwriting, and Customer Experience
This blog breaks down what it takes to build production-ready AI in insurance across claims, underwriting, and customer experience. It covers the gap between AI pilots and live deployments, the architecture and governance requirements that determine whether a system holds up at scale, and what insurers need to get right across data infrastructure, compliance, and human oversight before going live.
Author
Apoorva PathakContent Writer
Subject Matter Expert
Jani Hardik SanjaySenior Business Analyst
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Table of Contents
Key Takeaways
- AI in insurance at scale requires workflow integration, compliance architecture, and data infrastructure aligned from day one.
- Production-ready AI in claims, underwriting, and customer experience delivers measurable cost and efficiency gains, but only when governance is built into the system from the start.
- Claims, underwriting, and customer experience each need their own AI architecture, with governance built for one becoming the foundation for the next.
- The gap between a working AI pilot and a system that performs at production scale is where most insurance AI programs stall.
AI in Insurance Has Moved From Strategy to Execution
Insurance operations have carried a structural weight that few industries share. The gap shows up across every part of the operation: claims that take too long, underwriting that moves at the pace of manual review, fraud that goes undetected, and infrastructure that was never built to keep pace.
AI in insurance is integrating at a rapid and widespread scale, but the distance between a working pilot and a system embedded in live insurance workflows is where most programs lose momentum. Despite AI’s necessity in the industry, the vast majority of carriers have yet to successfully scale AI beyond the pilot stage.
The gap between a working AI initiative and a system that functions within live insurance workflows, at scale, under regulatory requirements, is where most programs stall. Although Insurers have taken the first steps, translating early implementations into production-grade systems remains the defining challenge of this moment.
AI insurance software development, done at a production level, demands workflow integration, governance architecture, data infrastructure, and organizational alignment working together. This blog covers what that looks like in practice across claims processing, underwriting, and customer experience, and what it takes to build AI systems that hold up beyond controlled environments.
Is AI the Future of Insurance, or Its Present Reality?
The insurance industry is under pressure from multiple directions at once. Rising claims volumes, increasingly coordinated fraud, and shrinking margins are exposing the limits of how carriers have traditionally operated. The cost of staying with legacy workflows is now measurable across every core function.
The Operational Pressure Facing Modern Insurers
The financial environment facing insurers has grown more demanding across several fronts. Fraud alone costs the global insurance market an estimated $308.6 billion annually, with industry-wide fraud losses climbing between 10% and 15% each year. Fraudsters have become more coordinated, with 71% of fraud and risk leaders reporting that organized operations were responsible for the majority of attacks in 2024.
Margin pressure runs alongside the fraud problem, as deteriorating conditions across both personal and commercial lines continue to squeeze combined ratios, leaving insurers with less room to absorb the operational costs that manual workflows carry.
The technology is rarely the issue. We've had clients come to us after a failed deployment and nine times out of ten, the model was fine. The problem was everything around it, the data, the workflows, the compliance layer. Nobody had thought about what production actually looks like.
Kunal Kumar
CRO, GeekyAnts
From Rule-Based Automation to Intelligent Decision Systems
Insurers have so far built for fixed conditions that handled straightforward cases but required constant manual updates and produced no insight beyond what the rules already anticipated. When claims data arrived as handwritten notes, fragmented records, or documents with non-standard formats, those systems either failed or defaulted to human review.
AI-driven systems, on the other hand, are trained on insurance-specific data to assess context, identify patterns across large volumes of information, and route decisions with a level of consistency that rule-based systems cannot replicate at scale.
The progression from rule-based to AI-driven decision systems marks a shift from handling volume to handling complexity. Unstructured documents, call transcripts, images, and workflows that span multiple functions across claims, underwriting, and customer communication can now run through a single architecture, with human oversight built into defined points across the workflow.
How is AI in Insurance Reshaping Claims, Underwriting, and Customer Experience?
AI has made its greatest impact in claims, underwriting, and customer experience, and the gap between early adoption and production performance is most consequential across these three functions.
AI in Claims Processing
Claims processing holds the highest share of AI adoption in insurance across the three core functions. AI handles document intake, coverage verification, damage assessment, fraud scoring, and routing. For straightforward claims, this happens without adjuster involvement. For complex ones, AI prepares the file and flags relevant information so that human review focuses on decisions that require expertise.
AI-Powered Underwriting
AI is gaining ground fastest in underwriting, driven by the volume of structured and unstructured data that risk decisions depend on. The models drawing from claims history, behavioral data, and third-party sources are producing risk assessments that manual review has historically struggled to match for consistency. Pricing reflects current risk data, and every decision comes with documented reasoning that holds up under both regulatory and policyholder scrutiny.
AI in Customer Experience
Insurance customers increasingly reach their carrier through multiple channels. Policy inquiries, claims updates, and first-level support move through those channels without every interaction requiring a human agent. During live calls, copilots surface relevant policy details and suggested responses in real time. Personalization models analyze customer data to deliver relevant recommendations and renewal messaging. Retention prediction systems identify dissatisfaction signals well before a policyholder leaves, giving account teams time to act. Insurers using AI-powered tools across service and operations report measurable gains in team productivity and cost efficiency across every customer-facing function.
What Does Production-Ready AI in Insurance Look Like?
Scaling AI in insurance demands workflow integration, governance architecture, data infrastructure, and organizational alignment working together from the very beginning. Workflow integration, governance architecture, data infrastructure, and alignment across teams all have to be in place before the system goes live.
Moving Beyond Proofs of Concept
Most carriers have AI initiatives underway, but the share with fully deployed production solutions remains significantly smaller than those still in testing or pilot phases. Unlike pilots, live deployments must connect with existing infrastructure, handle inconsistent data, and operate within compliance boundaries at volume, and that is where most programs stall. Deferred governance planning invites regulatory delays at the point of deployment. Poor data quality surfaces as unreliable outputs once the system is live. And without redesigning business workflows around the AI system, automation produces results that still depend on manual follow-through to create value.
Characteristics of Production-Grade Insurance AI Systems
Reliability across claim types, geographies, and data conditions is the baseline. Every decision the system produces must carry documented reasoning. Decision logs must stay accessible for regulatory review at any point. Tracking model performance on an ongoing basis is what catches degradation before it starts affecting outcomes. The architecture also needs to hold up as volume grows, jurisdiction by jurisdiction, without requiring structural rebuilds each time.
Human-in-the-Loop Architecture
Across most jurisdictions, regulators require demonstrable human oversight in AI systems that affect insurance decisions. In production, defined thresholds determine which decisions a qualified reviewer handles and which move through without intervention. This escalation architecture is a structural requirement of compliant AI deployment and must be designed into the system from the start.
How to Build an AI-Powered Claims Platform That Works in Production
The biggest bottleneck AI solves in claims is all the manual sorting and document review that happens before any real assessment begins. Earlier, a claim would come i and the ops team would go through everything by hand, check for missing details, and route it manually. With AI, the system reads the claim, pulls out the key information, identifies the claim type, flags anything missing, and passes only the cases that actually need human attention.
That is usually where AI gives the quickest value, and claims tends to deliver the fastest return on an AI investment for exactly that reason. The volume is high, the work is repetitive, and the efficiency gains show up quickly. That said, it only holds if the data is reasonably clean, the workflow is well-defined, and there is a proper human fallback for cases that need judgment.
Jani Hardik Sanjay
Senior Business Analyst, GeekyAnts
Claims processing holds the highest concentration of active AI deployments in insurance, and the architecture required to support it in production is built around that operational and regulatory weight.
The Modern Claims AI Architecture
Claim information arrives across multiple channels and formats, and OCR and NLP layers read, extract, and organize that information before any assessment begins.
Extracted data moves into fraud scoring before reserves are set, where AI models assess each claim against historical fraud patterns, behavioral signals, and network-level relationships across claimants and third parties. Claims that clear this stage enter a decision layer where coverage is verified, severity is assessed, and a routing determination is made. Cases within defined parameters proceed to settlement. Those outside them move to a human reviewer with the file, coverage summary, and relevant history already prepared. Workflow orchestration connects each stage to the insurer's existing systems, and automated communication keeps the policyholder informed throughout without requiring adjuster involvement at every step.
AI Agents for Claims Operations
Three categories of AI agents are changing how claims teams allocate their time.
Triage agents evaluate incoming claims for complexity, coverage eligibility, and fraud risk within minutes of submission, handling the sorting process that precedes any substantive review. On complex cases, adjuster copilots surface relevant policy details, flag inconsistencies between the claim narrative and supporting documents, and recommend settlement ranges drawn from comparable historical cases. Claims that require additional documentation are handled by automated investigation agents, which reach out to claimants and cross-reference third-party records without manual coordination.
Fraud Detection Systems Using AI
AI fraud detection addresses this across three areas. Pattern analysis scores incoming claims against historical fraud cases at a volume that manual review cannot replicate, and does so before reserves are committed. Behavioral anomaly detection identifies deviations from established claimant or provider patterns, surfacing inconsistencies that rule-based systems are not built to catch. Network fraud detection maps relationships across claimants, providers, and third parties to expose organized rings that individual claim review would never surface.
How does AI Underwriting Software Transform Risk Decisions at Scale
A lot of insurers get the pilot right, but the trouble starts when real business data, exceptions, compliance checks, and operational pressure come in. The gaps usually show up in the small but important things: poor handling of edge cases, weak explainability, missing audit trails, or a workflow that does not fit how teams actually work. Audit trails are the one I flag most consistently, because without them, a decision cannot be explained to a regulator after the fact, and that is when the difference between a pilot and a production system becomes a real business problem.
Jani Hardik Sanjay
Senior Business Analyst, GeekyAnts
Underwriting decisions carry the highest stakes in the insurance value chain, and the infrastructure required to support AI at that level demands a different standard of data quality, speed, and governance than most carriers currently have in place.
Real-Time Risk Intelligence Pipelines
Underwriting decisions have long been constrained by how long it takes to gather and assess risk data. AI underwriting platforms address this by ingesting risk data as the submission arrives. AI has reduced the average underwriting decision time for standard policies from three to five days to 12.4 minutes, while maintaining a 99.3% accuracy rate in risk assessment.
Third-Party Data Integration
The accuracy of an underwriting decision depends on the breadth of data behind it. AI underwriting platforms connect to external sources, including property records, environmental risk models, credit data, motor vehicle records, and loss history, drawing on information that a standard application would not include. This allows the system to build a more complete risk profile before a submission reaches an underwriter.
Predictive Models and Explainable Decisions
Predictive models draw from historical loss data, third-party signals, and portfolio context to produce risk assessments that are more granular and consistent than manual review, with documented reasoning built in as a standard component.
Documented reasoning behind each decision has become a compliance requirement across a growing number of jurisdictions. Production underwriting systems carry this as a standard component, generating decision trails that satisfy regulatory review without requiring manual reconstruction after each audit.
Underwriter Copilots and Decision Support
AI changes what underwriters spend their time on. Submissions that fall outside the insurer's risk criteria are identified before an underwriter opens the file. For submissions that require human judgment, the underwriter receives a prepared file with risk scoring, data sources, flagged concerns, and pricing recommendations already assembled. For a mid-size insurer, this shift means a leaner underwriting team can manage submission volumes that would previously have required a substantially larger one, without compromising decision consistency across the portfolio.
How Is AI in Insurance Redefining the Customer Experience
The real issue in customer experience deployments is rarely the AI itself. It is the workflow around it. Teams often forget to define the escalation triggers, what context gets passed to the human agent, and what the agent is supposed to do when the case arrives. The customer ends up repeating everything, the agent starts from scratch, and whatever time the AI saved gets lost in the handoff.
In insurance, the moment an interaction becomes personal, complex, or policy-specific, the AI needs a clearly defined path forward. When that path is missing, the business starts saying the AI is not helping, when the actual problem is the transition design.
Jani Hardik Sanjay
Senior Business Analyst, GeekyAnts
The standard policyholders apply to their insurer is shaped by every digital experience they have had elsewhere, making customer experience a competitive variable that insurers can no longer treat as secondary.
Why Customer Experience Has Become a Competitive Differentiator
Insurance has a customer experience problem that predates AI. Policyholders now evaluate their insurer against the same standard they apply to their bank or retailer: fast responses, clear communication, and service that does not require repeating information across channels.
The retention stakes are significant. According to J.D. Power's 2026 industry analysis, 29% of insurance customers switched their insurer in 2025, with rate pressure and poor communication cited as the primary drivers. Carriers that have invested in AI-driven personalization report measurable gains in products held per customer compared to those at earlier stages of digital customer experience development.
Conversational AI for Insurance Support
Effective conversational AI in insurance requires deep integration with policy administration, claims management, and billing systems. A support system that cannot access a customer's actual policy or check a live claim status produces responses that create more friction than they resolve. Multilingual support extends the reach of these systems across diverse policyholder bases. Coverage questions that carry regulatory weight, and interactions that need a licensed professional, are routed to a human agent through escalation points that are built into the system from the start.
AI-Powered Personalization Across the Insurance Journey
Behavioral patterns, transaction history, and interaction signals feed into policy recommendations and renewal messaging built around each customer's specific situation. When life events or behavioral shifts point to a coverage gap, the system surfaces relevant products at the point where the customer is most likely to act. Next-best-action systems track where each customer sits in their relationship with the insurer and bring forward the most relevant response, a coverage upgrade, a renewal prompt, or a service touchpoint, timed to when it will land.
Voice AI and Connected Insurance Experiences
Voice remains the dominant channel for insurance customer service, handling the largest share of contact center interactions across claims, billing, and policy inquiries. AI voice agents take on claims intake, policy inquiries, billing questions, and renewal conversations across those calls. On live interactions, copilots give agents the policy details and suggested responses they need without breaking the flow of the conversation. When a customer picks up where they left off on a different channel, the context travels with them.
Predictive Retention and Customer Intelligence
Churn signals rarely appear overnight. AI models reading billing patterns, interaction frequency, sentiment, and renewal behavior can identify a policyholder drifting toward cancellation months out, giving account teams a window to act. Renewal intelligence systems track lifecycle data to find the right moment for outreach. Sentiment analysis runs across calls, emails, and digital interactions, catching dissatisfaction early enough to inform how the team responds.
Human-in-the-Loop Customer Support Systems
A response that provides incorrect coverage guidance exposes the insurer to liability and damages policyholder trust in ways that are difficult to recover from. Production customer experience systems are built with defined points at which interactions transfer to licensed professionals. AI handles volume, consistency, and routine service. Human agents handle judgment, empathy, and the interactions where a qualified representative is a compliance requirement.
How Are AI Agents Automating Insurance Workflows End to End?
AI agents in insurance execute multi-step workflows across claims, underwriting, and policy servicing autonomously, within defined boundaries that keep every action auditable and compliant
What Are Insurance AI Agents?
AI agents in insurance are systems that execute multi-step workflows across claims, underwriting, and policy servicing by reasoning through tasks, making decisions, and triggering downstream actions without a human initiating each step.
Multi-Agent Workflow Orchestration
Production insurance workflows involve multiple specialized agents working in sequence. A claims workflow moves through intake, validation, coverage assessment, fraud scoring, routing, and settlement, with each stage handled by a dedicated agent passing outputs to the next.
AI Copilots for Internal Teams
AI copilots work alongside underwriters, adjusters, and customer service teams. For underwriters, copilots surface risk data and assemble pre-populated files before the underwriter opens the case. For adjusters, they highlight inconsistencies in claim documentation and recommend settlement ranges based on comparable cases. For customer service teams, they surface policy details and suggested responses during live interactions.
Risks of Autonomous Insurance Decisions
Autonomous AI systems in insurance introduce risks that require deliberate governance. Global trust in fully autonomous AI dropped from 43% to 27% in 2025, according to the AURA Agent Autonomy Risk Assessment framework, and fewer than 10% of organizations report having governance frameworks capable of managing AI agents at scale.
An incorrectly denied claim triggers regulatory scrutiny and policyholder disputes. A fraud scoring error either passes fraudulent claims or flags legitimate ones, both of which carry financial and reputational costs. Insurers are required to document AI systems thoroughly, maintain decision records accessible for regulatory review, and validate that agent behavior stays within defined boundaries as systems scale.
What Does Compliance, Security, and Responsible AI in Insurance Require?
Regulatory Challenges in AI Insurance Systems
As of early 2026, over half of all U.S. states have adopted the NAIC Model Bulletin on the Use of AI Systems by Insurers, establishing documented governance, bias testing, and audit requirements as baseline expectations for carriers. In the EU, the AI Act classifies risk assessment and pricing AI in life and health insurance as high-risk under Annex III, with full compliance obligations taking effect from August 2026, according to the EU AI Act official text.
Model Governance and Auditability
Insurers retain full responsibility for the data and models they use, including those built by third-party vendors. Every AI decision must be logged with its inputs and outputs in a format accessible to regulators, and model performance must be monitored on an ongoing basis so that degradation is identified before it affects outcomes.
Data Privacy and PII Protection
HIPAA governs data handling in health insurance. GDPR applies to insurers operating in or serving customers in the EU. State privacy laws in California, Virginia, and Colorado impose additional requirements on how personal data is collected, processed, and retained across AI workflows.
Bias Detection and Explainability
New York's DFS Circular Letter 2024-7 requires insurers to demonstrate that AI systems do not produce disproportionate adverse effects against protected classes, according to Buchanan Ingersoll's regulatory analysis. Regulators expect bias testing, documented reasoning behind adverse decisions, and policyholder access to explanations on request.
What Makes Data Infrastructure Critical for AI in Insurance?
The performance of any AI system in insurance is determined before a single model runs, by the quality, structure, and accessibility of the data behind it, and most production failures trace back to infrastructure that was not built to handle both at scale.
Structured vs Unstructured Insurance Data
Structured data covers policy records, premium transactions, and claims fields in defined database formats. Unstructured data includes claim narratives, medical reports, legal correspondence, photographs, and audio recordings. Production AI systems require both, and the infrastructure must handle them together.
Claims Documents, Emails, and Policy PDFs
Claim narratives, policy PDFs, adjuster notes, and correspondence contain the context that determines coverage, liability, and fraud risk. Processing this content at scale requires document extraction capabilities that read meaning and relationships within documents, not just the text they contain.
Real-Time Data Pipelines
Batch processing is incompatible with AI systems that make decisions at the point of submission. Real-time data pipelines allow claims systems to receive and process information as it arrives, underwriting platforms to pull third-party data at submission, and pricing models to reflect current risk conditions. Carriers deploying real-time pipelines can adjust risk profiles continuously rather than at renewal.
Vector Databases and Retrieval Systems
Vector databases store document content in a format that supports relevance-based retrieval across policy wordings, claims histories, and regulatory guidance, allowing AI systems to surface the most contextually relevant information rather than relying on keyword matching.
Data Quality and Model Reliability
Inconsistent records across legacy systems, missing fields, and poorly standardized data produce AI outputs that degrade over time. Data validation at ingestion, standardization across sources, and ongoing monitoring are prerequisites for AI systems that perform reliably in production.
How Does AI in Insurance Translate Into Measurable ROI?
Claims Cost Reduction
AI-enabled carriers report measurable reductions in cost per claim and resolution time as automation removes manual touchpoints from intake through settlement, allowing claims teams to focus on complex cases rather than administrative processing.
Faster Underwriting Turnaround
McKinsey's analysis found that AI has reduced quoting times from several weeks to a matter of days in commercial lines, with some specialty lines moving from multiple days to a few hours.
Fraud Prevention Metrics
AI fraud detection identifies network-level relationships, behavioral anomalies, and cross-line patterns before reserves are committed, addressing organized fraud at a scale that rule-based systems cannot match.
Customer Retention Improvements
Insurers that deploy AI across customer-facing operations see measurable gains in policyholder satisfaction and retention, as faster resolution and proactive communication address the primary reasons policyholders leave.
Operational Efficiency Gains
Insurers that deploy AI across core operations report measurable gains in productivity and cost efficiency, with the impact most visible in functions where manual processing previously created the highest volume of administrative work.
How Should Insurers Build, Buy, or Hybridize AI Insurance Software?
When Off-the-Shelf AI Works
Vendor AI solutions work well for standardized, high-volume functions where the insurer does not need a competitive edge from the technology itself, by offering faster deployment and proven performance. Top-performing insurers operate within modular, open architectures that combine in-house intelligence with vendor-driven scalability, according to McKinsey's analysis of AI adoption patterns across the sector.
When Custom AI Products Become Necessary
Custom AI becomes necessary when the function is a source of competitive differentiation. Underwriting models trained on proprietary loss data and pricing models that reflect a carrier's unique risk appetite cannot be replicated by off-the-shelf tools.
Risks of Vendor Lock-In
Service agreements dictate release schedules and data access rights. Exiting a vendor ecosystem requires rebuilding integrations from scratch. Data portability is constrained by proprietary formats, complicating migration to alternative platforms.
Hybrid AI Architecture Strategies
A hybrid approach allows insurers to purchase capabilities that accelerate standard operations while building the systems that drive differentiation in underwriting and claims. This requires an internal coordination layer that connects both, managing how proprietary and vendor systems interact across the enterprise.
What Does a Successful AI in Insurance Implementation Look Like?
A production-ready AI insurance system is the result of decisions made well before any model is selected, starting with a precise definition of the problem, the workflow it sits in, and the data required to support it.
Discovery and Workflow Mapping
Define the problem precisely before building anything. Precisely scoped use cases, measurable success criteria, and documented system dependencies are the foundations of a deployment that holds up beyond the pilot stage.
Data Infrastructure Preparation
Audit existing data sources for quality and consistency across systems before model development begins.
AI Model Selection
Select models based on the specific function, data type, and regulatory requirements of each use case rather than general capability benchmarks.
Compliance-by-Design Architecture
Retrofitting governance, explainability, and audit capabilities into a live system costs significantly more than designing them up front.
Human Review Systems
Escalation thresholds and human review workflows must be defined before deployment, as systems that go live without them create regulatory exposure and operational bottlenecks.
Monitoring and Continuous Evaluation
Model performance against defined metrics must be tracked on an ongoing basis, with retraining cycles built into the operational plan.
Scaling Across Insurance Operations
Once one function reaches target performance, the architecture and governance framework built for it becomes the foundation for the next.
How Does GeekyAnts Turn AI in Insurance From Concept to Reality?
GeekyAnts is an AI-powered product engineering and consulting firm with 550+ engagements across banking, finance, and insurance since 2006. Recognized as a Top 15 AI and software development company in the US by TopDevelopers.co, and carrying an ISO certification for information security management, a Cyber Essentials certification backed by the UK Government, and a 4.8-star rating across 112 verified Clutch reviews, GeekyAnts brings validated, audit-ready credentials to every engagement.
We've worked with enough insurers to know that a successful demo means very little. The real test is six months after go-live, when the data is inconsistent, the edge cases are piling up, and a regulator wants an explanation for a decision the system made last quarter. That is what we build for.
Kumar Pratik
CEO, GeekyAnts
In financial services, the firm's work spans AI-powered fraud detection that delivered a 200% reduction in false alerts, a 56% latency reduction for a US banking client, and 400M+ global payments processed annually through a single production platform. Insurance clients include Goosehead Insurance and ICICI, alongside global institutions across retail banking, lending, and wealth management.
For insurers building AI into claims, underwriting, and customer experience, GeekyAnts offers end-to-end capabilities: AI consulting and transformation, AI-powered product engineering from prototype to production, enterprise system modernization for legacy infrastructure, and digital customer experience design. Every engagement is structured around production-grade standards, with compliance built into the architecture from day one, not added after deployment.
Where Does the Gap Between AI Insurance Pilots and Production Lead?
AI in insurance has moved past the question of whether it works. The challenge now is building systems that hold up in production, across claims, underwriting, and customer experience, under real data conditions, regulatory requirements, and operational volume. The insurers that close the gap between pilot and production will define the next standard for the industry. Those who do not will find the distance between where they are and where they need to be growing harder to cover with each passing cycle.
Frequently Asked Questions
Claims automation delivers faster, more visible returns because volume is higher and the cost per transaction is immediate. Underwriting ROI compounds over time, but claims automation funds the broader transformation.
Sources and Citations
- https://www.mckinsey.com/industries/financial-services/our-insights/the-future-of-ai-in-the-insurance-industry
- https://www.mckinsey.com/industries/financial-services/our-insights/ai-in-insurance-understanding-the-implications-for-investors
- https://www.mckinsey.com/capabilities/tech-and-ai/how-we-help-clients/rewired-in-action/aviva-rewiring-the-insurance-claims-journey-with-ai
- https://www.jdpower.com/business/resources/rate-pressure-customer-retention-and-digital-engagement-top-insurance-industry
- https://artificialintelligenceact.eu/annex/3/
- https://content.naic.org/article/statement-national-association-insurance-commissioners-naic-ai-executive-order
- https://www.bipc.com/when-algorithms-underwrite-insurance-regulators-demanding-explainable-ai-systems
- https://www.cbh.com/insights/articles/ai-in-insurance-how-to-build-a-compliant-governance-framework/
- https://arxiv.org/pdf/2510.15739
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