What Is AI for Insurance?
AI in insurance uses algorithms for smarter underwriting, faster claims, better risk assessment, and personalized customer service. AI helps automate manual tasks, detect fraud with advanced analytics, allows insurers to offer more personalized products, and supports automated customer service.
Key applications of AI in insurance:
Underwriting and pricing: Automatically extracts relevant information from documents at scale and uses machine learning algorithms to compute risk and pricing.
Claims processing: Uses NLP, computer vision, and generative AI to rapidly process documents, images, and videos, speeding up settlement times and automating reports.
Fraud detection: Identifies suspicious patterns and inconsistencies in claims by comparing data against historical records, significantly reducing losses.
Risk management: Predicts trends and identifies potential risks, allowing insurers to proactively adjust strategies and help policyholders mitigate risks.
Customer service: Employs chatbots for instant, 24/7 support and provides agents with "next best action" recommendations.
Personalized products: Enables usage-based insurance (UBI) by analyzing driving behavior (via apps/devices) to reward safe drivers.
Benefits of AI in Insurance
Benefits for Policyholders
AI enhances the insurance experience for policyholders by improving service speed, accuracy, and personalization.
Faster claims resolution: AI speeds up claim approvals by automating damage assessment, document verification, and fraud checks, reducing wait times for policyholders.
Improved service access: Chatbots and virtual assistants provide 24/7 customer support, helping users update policies, submit claims, or get answers instantly.
Personalized coverage: AI analyzes user behavior (e.g., driving habits or health data) to tailor policies, discounts, and recommendations based on individual risk.
Transparent communication: Natural language generation tools create clear, personalized updates, helping customers understand their policies and claim statuses.
Benefits for Insurance Agents and Underwriters
AI frees agents and underwriters from repetitive tasks, enabling them to focus on high-value activities like client advising and risk evaluation.
Reduced administrative work: AI extracts and validates data from documents, eliminating manual entry and reducing time spent on paperwork.
Decision support: Risk scoring models and predictive analytics give underwriters real-time insights, helping them make faster, more accurate decisions.
Standardized workflows: Automated tools ensure consistency across files and formats, reducing rework and simplifying regulatory compliance.
Enhanced client interactions: Agents can use AI-generated recommendations to offer more relevant coverage options or upsell based on client profiles.
Benefits for Insurers
AI supports operational efficiency, profitability, and scalability for insurers across business functions.
Operational cost savings: Automation reduces the need for manual processing in claims, underwriting, and customer service, cutting labor costs.
Fraud reduction: AI systems detect anomalies and flag high-risk activity early, improving loss ratios and reducing exposure to fraudulent claims.
Faster time to market: Insurers can launch new products more quickly by using AI to analyze market trends and simulate underwriting strategies.
Data-driven strategy: With AI-driven insights, insurers can better manage risk portfolios, forecast claims, and adjust pricing dynamically.
Types of AI Technology Used in Insurance
Natural Language Processing
Natural language processing (NLP) enables insurance systems to understand, interpret, and respond to human language. In insurance, NLP is often used for tasks such as extracting relevant information from customer emails, automating claims intake, and supporting chatbot interactions that handle policy inquiries without human intervention. This technology reduces the time agents spend manually sorting communications and allows quicker, more accurate responses to policyholders.
NLP also supports sentiment analysis in customer reviews and communications, offering insights about customer satisfaction and flagging potential issues automatically. By mining unstructured text data from documents or call transcripts, insurers can identify trends, compliance risks, or fraudulent behaviors that may otherwise be missed.
Computer Vision
In claims workflows, computer vision enables automated analysis of visual evidence, such as photos from accident scenes. When integrated with agentic AI, this allows the system to assess damage severity, determine coverage applicability, and initiate payouts without manual review. These AI agents can also flag ambiguous cases or escalate claims requiring expert input, optimizing both speed and accuracy.
For risk assessment, computer vision supports agentic agents by analyzing property or vehicle images to detect potential hazards or assess conditions. Aerial imagery, for example, can help evaluate a home's proximity to flood zones or wildfire-prone areas. This information feeds into automated underwriting decisions, enabling more accurate pricing and faster policy approvals.
Advanced Document Automation
Insurance workflows depend heavily on documents like loss runs, ACORD forms, compliance records, and operational data, all of which are traditionally handled through manual review. AI-powered automation can now extract, validate, and analyze this information with high accuracy, removing the need for manual data entry. This shift dramatically reduces underwriting review times, enabling insurers to move from multi-week evaluations to decisions made in minutes.
Automated document processing improves speed and strengthens compliance and fraud detection. AI systems can scan policy and applicant data to flag anomalies, identify regulatory issues, and highlight patterns that would be difficult to detect manually. These tools also support audit readiness by generating outputs aligned with industry standards and regulatory requirements.
Generative AI
Generative AI introduces new dimensions to insurance operations by creating outputs
(text, code, or visuals) rather than just analyzing inputs. While many industries apply it for generalized tasks like marketing content or customer support chat, insurers are exploring high-value, sector-specific applications. These include generating underwriting narratives, drafting policy documentation, and producing automated client communications.
Generative AI offers flexibility and contextual understanding, enabling more human-like outputs tailored to the insurance domain. By investing in domain-specific models and fine-tuning them with contextual data, insurers can drive gains in profitability, efficiency, and operational intelligence. For example, using generative AI to simplify code development accelerates digital transformation and reduces training overhead.
Agentic AI
Agentic AI refers to AI systems capable of autonomous decision-making, execution, and adaptation within set boundaries. In the insurance industry, agentic AI agents can navigate claims workflows, engage customers, or handle exceptions without human supervision. They can, for example, verify documents, escalate complex cases to human adjusters, or initiate payments on approved claims, helping reduce turnaround times.
AI agents can integrate data from multiple sources (historical, contextual, third-party, and synthetic) to coordinate complex processes such as claims resolution, underwriting, or policy servicing. Claims that once took weeks are now resolved in hours, while underwriting expenses have dropped. These systems also boost customer retention by enabling personalized, responsive service at scale.
Top Applications and Use Cases of AI in Insurance
1. Document Automation
AI-powered document automation tools streamline the extraction, validation, and analysis of insurance documents such as loss runs, ACORD forms, and compliance records. These systems eliminate manual data entry by identifying and pulling relevant fields, significantly reducing processing times and the potential for human error.
In addition to speed, automation enhances consistency across formats by standardizing data from multiple carriers and producing audit-ready outputs. This enables underwriting and claims teams to quickly identify exposures, compliance issues, or operational risks without needing to manually review each file, accelerating core workflows and supporting regulatory readiness.
2. Underwriting and Pricing
AI-driven underwriting systems now automate the review of critical documents such as loss runs, ACORD forms, and compliance records. Instead of relying on manual data entry, AI extracts and validates key information with high accuracy. These tools flag anomalies, standardize formats across carriers, and generate audit-ready outputs, helping underwriters focus on decision-making rather than paperwork.
Advanced systems also analyze underwriting factors like claim history, operational data, and safety protocols to build comprehensive risk profiles. By automating risk scoring and standardizing assessments, insurers reduce turnaround times from weeks to minutes while improving consistency, accuracy, and regulatory compliance across the portfolio.
3. Claims Processing
AI in claims processing eliminates manual delays by automating the analysis of structured and unstructured data, including documents, photos, and reports. Tools like Kolena simplify claims analysis by extracting relevant values, validating data, and detecting inconsistencies with over 99% accuracy. This allows insurers to process claims faster while reducing the risk of human error or oversight.
AI also enhances standardization across carriers by formatting loss run and claims data consistently. With built-in fraud detection and compliance monitoring, these systems flag suspicious entries or regulatory risks early in the process. The result is faster settlements, improved accuracy, and reduced operational overhead in high-volume claims environments.
4. Fraud Detection
AI enables proactive fraud detection by scanning applicant and policy data for irregularities. Systems like Kolena compare information against known patterns, sanctioned-party lists, and historical claims to surface anomalies that may indicate fraud or compliance issues. These automated reviews reduce the chances of missed red flags during underwriting or claims processing.
By integrating fraud detection into document workflows, AI reduces the burden on human reviewers and speeds up identification of suspicious cases. This improves loss ratios and strengthens the insurer’s ability to comply with regulatory requirements, while also allowing underwriters and claims teams to act quickly on flagged risks.
5. Automated Insurance Verification
AI enables instant verification of insurance coverage by tapping into structured databases, public records, and digital documents. Automated verification tools cross-check policy details, endorsements, and customer identities during the application or claims process, accelerating decision timelines. This automation is particularly valuable for high-volume workflows such as auto insurance or health plan verification at healthcare providers.
Errors and omissions are reduced due to systematic cross-validation of information, and suspicious patterns can be flagged for additional review. These capabilities ensure all policies and claims adhere to company and regulatory requirements, lowering fraud risk and minimizing disputes.
6. Risk Management
AI revolutionizes risk management by aggregating and analyzing diverse data sources, such as weather feeds, credit reports, social media, and IoT sensors, to provide a comprehensive risk picture. Predictive analytics can forecast potential claim surges, emerging risks, and changing portfolio exposures, enabling insurers to proactively mitigate threats and optimize coverage options for clients.
Real-time risk scoring and automated alerts support faster, more informed decision making on new business, renewals, and claims. Underwriters and risk managers can adjust pricing, exclusions, or coverage amounts dynamically based on up-to-date insights. This continuous risk monitoring not only improves resilience but also helps insurers innovate with new, data-driven products.
7. Customer Service
AI-powered chatbots and virtual agents provide around-the-clock support to policyholders, answering questions, updating records, and guiding customers through policy changes or claims submissions. Natural language understanding allows these bots to handle many queries autonomously, with escalation to human agents for more complex issues. This instant, responsive service improves client satisfaction and operational efficiency.
AI personalizes customer service interactions by analyzing previous contacts, demographic data, and service preferences. Proactive engagement, such as customized alerts or policy renewal reminders, anticipates client needs and strengthens retention. Insurers benefit from reduced call center volumes, quicker query resolution, and enhanced customer loyalty.
8. Personalized Products
AI enables insurers to design and deliver customized insurance products that align with individual risk profiles, behaviors, and preferences. By analyzing granular data, such as driving habits, wellness indicators, or home sensor information, machine learning models create hyper-personalized offerings that can adapt over time as customers' needs or risk appetites change.
This personalization supports usage-based insurance, flexible policy options, and dynamic discounts that foster customer loyalty and satisfaction. AI also automates ongoing adjustments, ensuring coverage remains relevant and competitive without manual intervention. For insurers, this means higher conversion rates, better retention, and more profitable books of business.
The Future of AI in the Insurance Industry
According to a recent McKinsey report, artificial intelligence is no longer a supporting technology in insurance: It is becoming the foundation for how insurers operate, compete, and deliver value. As AI capabilities advance, especially in generative and agentic forms, insurance organizations must rethink their operating models, workflows, and technology stacks to stay relevant and drive sustainable growth.
Generative and Agentic AI Will Reshape Core Insurance Functions
The next wave of AI adoption in insurance will be driven by generative AI and agentic AI. Generative AI offers capabilities in reasoning, creativity, and language understanding. This allows insurers to automate the creation of policy documents, summarize claims histories, and generate empathetic, personalized communications at scale. Agentic AI builds on this by enabling autonomous, goal-oriented systems that can execute complex workflows without human intervention.
These systems can now function as intelligent agents within a multiagent environment. For example, in underwriting, different AI agents can collaborate to extract data from documents, evaluate risk, propose pricing, verify regulatory compliance, and route applications, all within a single end-to-end automated workflow. This approach is not limited to underwriting; claims, customer service, and policy servicing can all be transformed by similar agentic architectures.
Modernizing the Tech Stack for AI at Scale
Realizing AI's full potential requires a modern, modular technology stack that supports rapid deployment and scaling. This includes investment in cloud infrastructure, machine learning pipelines, and high-quality data platforms, or relying on providers that use these technologies. AI-native insurers are already shifting toward architectures that enable distributed, agentic AI systems to collaborate securely and autonomously across functions.
According to McKinsey, the tech stack must support four layers: AI-powered decision-making, scalable infrastructure, integrated data platforms, and reimagined customer engagement.
Change Management Will Be a Deciding Factor
Even with the right technology and strategy, AI transformation can stall without strong change management. Adoption challenges, including employee resistance, skills gaps, and unclear ownership, can prevent AI from becoming embedded in daily workflows. Insurers must treat adoption as a core part of the transformation effort, investing in training, communication, and culture change.
Successful insurers encourage shared ownership of AI tools. Employees see AI as a co-pilot, not a threat, and take responsibility for tuning models and improving results. This shift in mindset is crucial as AI becomes increasingly central to how insurance work gets done.
How Kolena Powers AI for Insurance Workflows
Kolena helps insurers operationalize AI for insurance by turning complex, document-heavy workflows into automated, auditable processes. Insurance organizations deal with enormous volumes of unstructured data—loss runs, claims files, policy documents, endorsements, compliance records, and supporting evidence. Kolena’s AI agents are purpose-built to ingest these materials, extract structured insights, and apply validation logic at scale, without requiring teams to build or maintain custom machine learning infrastructure.
What sets Kolena apart in AI for insurance is its focus on accuracy, transparency, and control. AI agents don’t just extract data; they explain why a value was extracted, cite source evidence, and surface confidence scores. This is critical for underwriting, claims, and compliance teams that must justify decisions to regulators, reinsurers, and internal audit teams. Whether automating loss run analysis, standardizing claims documentation, or validating underwriting submissions, Kolena enables insurers to reduce manual review time while maintaining trust in AI-driven outcomes.
Kolena also fits seamlessly into existing insurance operations. Kolena's AI agents can be connected to email inboxes, cloud storage, or internal systems to process new documents automatically as they arrive. Outputs can flow directly into downstream tools such as policy administration systems, underwriting platforms, or analytics dashboards. As insurers adopt more advanced AI for insurance—including generative and agentic AI—Kolena provides the foundation to scale safely, ensuring AI-driven automation delivers real operational impact without sacrificing governance or reliability.