Guide to AI for Insurance

For insurance leaders, the debate over AI's potential is settled; the race to deploy agentic AI in insurance is now underway. While industry investment is projected to surge from $10.24 billion to over $35 billion by 2029, capital allocation alone will not define the winners. The defining advantage of the next decade belongs to carriers that move beyond experimentation to deploy reliable AI agents for insurance that fundamentally reshape claims, fraud, and underwriting.

However, realizing this ambition reveals the critical challenge defining the current era: building reliable generative AI for insurance is difficult. While the majority of insurers are actively experimenting with AI solutions, a recent report indicates that fewer than 22% of insurance leaders have successfully deployed them at scale. The primary barriers are proving to be multifaceted, including both operational and technical hurdles.

Many initiatives fail to deliver tangible ROI because they are unreliable, producing inconsistent outputs, breaking when inputs deviate from ideal conditions, or requiring frequent human intervention to correct errors, and focus on low-impact use cases, like simple customer-facing chatbots, instead of targeting complex, data intensive, and high-margin back-office processes. Operationally, even well-aimed projects falter when they confront the core challenges of enterprise AI:

• Data is Not AI-Ready: Agentic systems require data in specific formats to perform well. For example, while a raw collection of contact center transcripts is useful, a structured set of enriched call data is the critical differentiator that allows agents to move from simple retrieval to complex reasoning.
• Models Don’t Understand Insurance Processes: Off-the-shelf agents possess only a theoretical understanding of claims processing, which is insufficient for actual operations. They lack the critical, institutional knowledge, such as specific employee skills, proprietary tools, and risk guidelines, required to execute a claim within a specific insurer's unique workflow.
• Building Reliable AI Agents is Hard: Defining a simple agent is easy, but building reliable, impactful agents at enterprise-scale is difficult. It requires expertise, cross-organizational commitment, and the right infrastructure to build ecosystems where AI agents can be continuously improved via techniques like context engineering and reinforcement learning based on employee feedback.
• Infrastructure to Support Complex Agents is Missing: A common pitfall is the "build trap," where teams deploy isolated agents without a unified foundation. This leads to a fragmented collection of tools that struggle to handle the complex, long-running, and asynchronous nature of enterprise insurance workflows reliably.

This guide provides a strategic roadmap for building reliable AI agents for insurance by addressing these specific challenges, detailing how insurers can move from pilot projects to the scaled, value-generating AI transformation that will define the industry's future leaders.

McKinsey estimated that AI could add up to $1.1 trillion in annual value for the global insurance industry. This value is derived from multiple streams, including an estimated $400 billion from technology-driven upgrades in pricing and underwriting and another $300 billion from AI-powered customer service. Looking at the broader economic impact, analyses suggest Generative AI's contribution could range from $2.6 trillion to $4.4 trillion annually, with the insurance sector being a primary beneficiary.

This value is being unlocked today in core operations. An analysis by Bain & Company quantifies the opportunity in Property and Casualty (P&C) claims handling alone as more than $100 billion globally. This is driven by a projected 20% to 25% reduction in loss-adjusting expenses and a remarkable 30% to 50% decrease in claims leakage.

Leading insurers partnering with Scale to activate this potential are already validating these projections. By combining expert services with an industry-leading platform to translate insurance documents into actionable intelligence, these carriers are realizing a 60% increase in employee productivity, a 90% improvement in accuracy, and 70% overall cost savings.

Early adopters are realizing these significant, measurable gains because they have moved beyond simple automation. These advancements are supercharged by a convergence of generative AI for insurance, real-time data from the Internet of Things (IoT), and advanced predictive analytics.

• Driving Efficiency and Customer Experience: Early adopters have achieved transformative results using traditional AI and automation, setting a powerful baseline for the industry.
  • Liberty Mutual developed, through Solaria Labs, an AI-driven Auto Damage Estimator that uses computer vision to generate repair estimates in minutes. Broader AI integration at the carrier has delivered a 4x increase in annual recoveries and a 20x improvement in fraud detection. To scale these efficiencies, they strategically invested in Snapsheet, whose virtual claims platform has enabled carriers to cut operating costs by 15% and deliver payments 40% faster than traditional models.
  • Similarly, Lemonade has leveraged automation to set global benchmarks for speed and efficiency. While they set a world record by settling a claim in just 2 seconds (using AI to pay out a stolen bike claim instantly), their broader impact is defined by consistency at scale. Today, their AI architecture handles over 55% of all claims with zero human intervention. This model has driven their Loss Adjustment Expense (LAE) ratio down to roughly 7% (nearly half the industry average).

These successes, achieved with the last generation of AI, underscore the even greater transformative potential of today's Generative and Agentic AI systems.

• Enhancing Fraud Detection: The global giant Allianz introduced an AI-powered platform that led to a 29% increase in the rate of detected fraud, directly protecting the company's loss ratio.
• Improving Underwriting Accuracy: French insurer CNP Assurances applied AI to automate the analysis of health questionnaires, increasing the automatic acceptance rate for policyholders by 5% and significantly accelerating the onboarding process.

Conversely, deciding not to apply AI at scale poses a significant risk to insurers, creating uncompetitive operational inefficiencies and preventing them from keeping up with changing customer needs. These challenges include:

• Inability to Process Unstructured Data: Legacy processes are slow because they depend on manually reading unstructured documents like PDFs, emails, and call transcripts. GenAI agents can ingest, summarize, and extract critical data from these sources. Insurers using this technology can understand a complex claim file in seconds, creating an insight and efficiency advantage.
• Vulnerability to Fraud: Insurance fraud costs insurance companies at least $40 billion dollars per year, and rising. Sophisticated fraud is now also being created with GenAI, leading to increases in fraud rates. This includes synthetic documents, deepfake evidence, and AI-generated claim narratives that are indistinguishable from reality to a human reviewer. Traditional, pattern-based fraud detection systems are not equipped to fight this new wave of AI-powered attacks, leaving insurers critically exposed.

Inability to Manage AI-Specific Regulations: New regulations like the NAIC Model Bulletin and the EU AI Act require extensive documentation on AI model fairness and transparency. GenAI is a critical tool for managing this complexity, capable of automatically generating compliance reports, summarizing regulatory changes, and drafting customer communications. Attempting to manage AI governance manually is no longer a viable option.

Today, applications of AI have matured from narrow, task-specific automation to the sophisticated, end-to-end reimagination of business processes. The most impactful use cases are being supercharged by advancements in GenAI, real-time data from Internet of Things (IoT) devices, and advanced predictive analytics. These technologies are fundamentally reshaping the core insurance functions of claims management, fraud detection, and underwriting.

AI-Powered Claims Management

The claims process, a critical touchpoint for customer satisfaction, has become a primary focal point for insurance claims automation, augmenting the capabilities of human claims adjusters and automating routine tasks at an unprecedented scale. AI Agents can help manage the immense volume of unstructured data in a claim file.

AI systems can now ingest, comprehend, and summarize a wide array of documents, including adjuster notes, detailed medical records, and official police reports. This provides claim agents with a comprehensive, synthesized case overview in seconds, freeing them to increase their processing efficiency or concentrate on high-value tasks like nuanced decision-making and empathetic customer interaction. This is often operationalized through enterprise-grade AI agents that provide a secure, conversational interface to the insurer's proprietary knowledge base.

In practice, these systems increasingly take the form of internal GenAI copilots for claims adjusters and underwriters. Unlike consumer chatbots, these copilots are embedded directly into enterprise workflows, allowing employees to query policy rules, summarize complex claim files, retrieve precedent cases, and reason across institutional guidelines in real time. These copilots are agentic systems grounded in proprietary data, governed by explicit rubrics, and continuously improved through human feedback. The value of GenAI is amplifying expertise inside high-stakes operational workflows.

Computer vision technology also continues to mature, drastically accelerating damage assessment. AI agents can now analyze images and videos of damaged property to programmatically evaluate the severity of damage. Paired with deep research agents, these systems can estimate repair costs from appropriate vendors. This capability is a key driver in reducing claims cycle times from weeks to mere minutes.

Beyond processing current claims, AI is also enabling a more forward-looking approach through AI-Powered Claims Intelligence Platforms. These systems use predictive models to forecast the future outcomes of incoming claims, allowing insurers to proactively allocate their most experienced adjusters to the highest-risk files and improve outcomes.

Advanced AI Fraud Detection and Prevention

AI has become the most powerful mechanism for combating insurance fraud, enabling a shift from reactive investigation to proactive, real-time intervention at scale. Industry deployments now demonstrate that modern AI-driven fraud systems can materially reduce claims leakage, with leading implementations cutting overpayment rates from roughly 10% to low single digits and increasing fraud detection accuracy by more than 40%. As a result, fraud prevention is no longer a marginal efficiency gain—it is rapidly becoming a baseline operational capability for competitive carriers.

Modern AI platforms can score millions of claims in real-time at the first notice of loss (FNOL) using a combination of automated business rules and adaptive machine learning algorithms. The accuracy of these systems is enhanced by their ability to synthesize insights from a diverse array of data sources in a multimodal analysis. This includes:

• Semantic Text Analysis: AI Agents, powered by LLMs, go beyond simple keyword matching. They analyze the full semantic context of claim narratives and medical reports to identify subtle inconsistencies, contradictions in causality, and narrative structures that are highly indicative of fraudulent activity.
• Multimodal Evidence Analysis: Increasingly, this is also the domain of multimodal LLMs, which process visual and textual data simultaneously. These models analyze photos and videos to detect sophisticated digital alterations (like deepfakes) and assess the physical context of the damage, cross-referencing visual evidence against the written claim narrative to flag inconsistencies.
• Network Link Analysis: One of the most powerful techniques, this involves using AI to map the hidden relationships between claimants, medical providers, and repair shops across thousands of claims. By identifying unusual clusters, the system can uncover sophisticated, large-scale fraud rings that would be virtually impossible for human investigators to detect manually. A powerful new strategy emerging from this is the network effect, which involves the creation of trusted partner exchanges where multiple carriers can securely share intelligence to disrupt systemic fraud rings that exploit information gaps between companies.

Intelligent Risk Assessment and Dynamic Underwriting

Underwriting, the core function of assessing and pricing risk, is being fundamentally reinvented by AI and the availability of new, dynamic data sources. The industry is rapidly moving away from traditional, static underwriting models toward a more precise, personalized, and real-time approach. This evolution is being dramatically accelerated by the data goldmine provided by the Internet of Things (IoT) and telematics devices. The influx of real-time data from these connected sensors is enabling entirely new underwriting paradigms:

• Usage-Based Insurance (UBI): This model represents a paradigm shift in personal lines insurance. Telematics devices in vehicles can track actual driving behaviors such as speed and hard braking events. This continuous stream of data allows insurers to offer dynamic, personalized premiums that directly reflect an individual's actual risk profile, rewarding safer drivers with lower rates.
• A Shift to "Predict and Prevent": The availability of real-time data facilitates a fundamental change in the insurer's role from the traditional, reactive model of simply paying for a loss after it occurs to a proactive "predict and prevent" model. By analyzing telematics data, an insurer can now alert a commercial fleet manager to patterns of risky driving, or by monitoring smart home sensors, they can notify a homeowner of a small water leak before it becomes a catastrophic flood. This approach not only prevents losses but also delivers tangible value to the customer, transforming the insurer-insured relationship.

Ultimately, the convergence of real-time data and advanced AI is doing more than just optimizing an old process; it is fundamentally altering the core business model of insurance. The industry is transitioning from a static, pooled-risk model to a dynamic, personalized risk management partnership with each customer, transforming the insurance product from a simple financial instrument into an ongoing service.

The defining challenge for today's insurers is operationalization: moving beyond isolated pilots to deploy reliable, high-impact AI agents at scale. Bridging this implementation gap requires a holistic strategic framework that unifies organizational structure, data foundations, and development methodology.

To achieve this, leading insurers are adopting the Scale GenAI Platform (SGP), the Agentic Infrastructure designed to enable the entire lifecycle of reliable AI deployment. SGP solves the core operational challenges through two distinct capabilities:

• Agent Operations (Solving the Trust Gap): A centralized command center that turns raw, unstructured data into AI-ready knowledge. This environment allows teams to build, train, and rigorously evaluate agents against enterprise ground truth, ensuring they are safe and accurate before deployment.
• Agent Execution (Solving the Scale Gap): An execution infrastructure designed for resilience. It orchestrates long-running, asynchronous workflows—like a multi-week claims process—ensuring agents persist through delays and system interruptions without requiring IT to manage the underlying servers.

With this infrastructure in place, insurers can execute a proven roadmap to value:

1. Translate: Data Foundation & Rubrics Insurance runs on vast amounts of unstructured data, from complex contracts and emails to multimedia evidence files. Building transformational agents first requires translating this noise into AI-ready structured formats using SGP’s data transformation tools. This process involves supporting Subject Matter Experts (SMEs) in ground-truth data labeling to establish high-quality training datasets. During this phase, rubrics are developed. These rubrics serve as the grading key for agents, defining exactly what a correct decision or output looks like before an agent ever attempts a task.
2. Build: Expert Engineering for High-Complexity Workflows The trap of low-impact chatbots is avoided by focusing on high-margin complexity, such as claims adjudication and commercial underwriting. To operationalize this focus, Forward Deployed AI Engineers work directly with insurer teams to build, integrate, and scale domain-specific agents. These experts ensure that agents are not standalone models, but are deeply integrated with enterprise tools and workflows, capable of completing job-specific tasks with the reliability required for core operations.
3. Train: Frontier Reinforcement Learning Off-the-shelf models do not understand an insurer’s specific underwriting guidelines or claims philosophy. Agents are therefore trained on unique institutional standards using frontier reinforcement learning techniques. Through SGP, insurer employees act as Human-in-the-Loop (HITL) supervisors, providing specific feedback and process guidance on agent outputs. This continuous feedback loop allows agents to learn an insurer’s risk appetite, steadily improving precision and reliability based on real-world corrections.

Evaluate & Red Team: Safety and Observability In a regulated industry, reliance on a black box is not viable. Before and during deployment, Enterprise AI Red Teaming is used to simulate real-world agent misuse and aggressively test for vulnerabilities, including bias in pricing or hallucinations in coverage analysis. Once live, SGP’s observability features allow every agent activity to be traced. This tracing capability provides a visible, step-by-step decision tree—a digital paper trail that is essential for auditing performance and maintaining regulatory compliance.

Building reliable AI agents is hard. Scale’s process is designed to equip insurance organizations with the skills, tools, and infrastructure required to move from pilot projects to reliable, production-grade agents. Scale is trusted across industries where dependable agent behavior matters most to deliver measurable operational and financial outcomes.

Ready to build? To learn more about how to develop reliable Enterprise AI systems for insurance, visit https://scale.com/genai-platform, or book a demo below to speak with our team.

1. The Business Research Company – Artificial Intelligence (AI) For Insurance Global Market Report
2. Roots Automation – State of AI Adoption in Insurance 2025
3. McKinsey – Insurer of the future: Are Asian insurers keeping up with AI advances?
4. Bain & Company – The $100 Billion Opportunity for Generative AI in P&C Claims Handling