The claims journey begins with the first notification of loss, submitted through emails, web forms, or mobile apps.

AI-driven systems powered by NLP automatically extract essential details such as the date, time, and cause of loss. This structured data is then used to create claim records directly within the insurer’s system, reducing manual intervention.

For unstructured data such as images and videos, computer vision tools like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) analyze content to extract critical information.

• Automation at Scale: Microsoft Azure Logic apps and Document Intelligence streamline email handling and structured form processing.
• Dynamic Engagement: Chatbots and virtual assistants, enhanced with sentiment analysis, assist claimants and prioritize urgent cases.
• Agentic AI Follow-up: These systems identify missing information, summarize gaps, and automatically notify claimants—reducing delays and improving responsiveness.

Agentic AI also supports multi-party coordination, enabling seamless communication among claimants, adjusters, repair shops, and third-party experts.

Examples include:

A leading global insurer modernized its Commercial Auto First Notice of Loss (FNOL) process by integrating telematics, photos, and detailed incident data through a mobile-first interface. The new solution streamlines the entire reporting journey, enhancing speed, accuracy, and customer experience.

 AI-driven triage uses predictive analytics to assess claim outcomes and costs, often before a human ever reviews the case. This “one-touch claim” model reduces resolution times from days to minutes, allowing adjusters to focus on high-value, complex cases.

Claims are automatically categorized by severity, complexity, and urgency. AI then verifies coverage limits, orchestrates workflows, and either processes claims straight through or routes them to the appropriate teams, adjusters, investigative units, or repair shops.

Dynamic workflow engines adapt to each insurer’s needs, responding to data, business rules, and events in real time.

Examples include:

• A leading U.S. insurer uses computer vision technology to generate instant repair estimates. Its intelligent models toggle seamlessly between automated and human reviews based on claim complexity.
• Another global insurer leverages an advanced analytics-driven tool to triage commercial property claims efficiently.

Fraudulent claims cost insurers billions of dollars annually, driving up premiums for honest policyholders. Reducing fraud is essential to maintaining customer trust and controlling costs.

Modern AI tools allow insurers to detect fraudulent and exaggerated claims in real time. Historical claims data combined with third-party sources help flag anomalies during the claims process.

Using NLP, machine learning, and network analysis, AI in claims management systems can:

• Identify suspicious behavioral patterns.
• Detect deep-fake images or altered documents.
• Uncover hidden relationships among claimants or third parties.

When human expertise is needed, the system routes flagged cases to a special investigative unit. Advanced techniques like ensemble modeling and copula regression now deliver unprecedented accuracy in fraud detection. Examples include:

• A global insurer uses AI to identify fraud-related keywords in commercial claims.
• Another leading insurer applies social network analysis to uncover organized fraud rings.

 AI transforms litigation management by predicting claim escalation risks and estimating potential settlement values. It also identifies subrogation opportunities, pinpointing third parties who may share liability.

Key benefits include:

• Rapid extraction of facts from large document sets.
• Identification of relevant case laws to support decision-making.
• Recommendations for external attorneys with proven expertise in similar cases.

Explainable AI (XAI) builds trust by providing transparent reasoning behind litigation-related decisions, ensuring compliance with regulations.

Agentic AI continuously monitors litigation progress, detects delays or compliance gaps, and suggests corrective actions. It can also autonomously update clients and legal teams through automated correspondence, reducing administrative burden.

 AI-driven models are redefining claim settlement by delivering faster, more transparent, and explainable outcomes. By leveraging historical and real-time data, insurers can streamline payments to policyholders and vendors through digital gateways, guided by predefined thresholds. AI can also factor in litigation strategies to accelerate settlements, while maintaining compliance with privacy regulations such as PCI and GDPR, ensuring customer identities remain protected.

Continuous training and auditing of AI models are critical to prevent discrepancies, such as underpayments or overpayments, and to maintain accuracy across diverse payment types.

Examples include:

• An insurer that achieved record-breaking claim settlement times—processing simple cases in just seconds.
• Another provider that resolves straightforward travel and auto claims within minutes, enhancing customer satisfaction and operational efficiency.

Several technology partners are enabling this shift. For instance, solutions offered by organizations like LTIMindtree provide seamless integration with both legacy and modern claims platforms, along with role-based dashboards to track claim status, exceptions, and pending actions, all in a single view.

Traditionally, recoveries were pursued only after claim settlement, making the process reactive and labor-intensive. With AI, insurers can begin recovery efforts as early as the First Notice of Loss (FNOL) stage. By correlating large volumes of structured and unstructured data, AI can identify the responsible party, analyze state-specific laws, and even suggest law firms experienced in similar cases.

This proactive, data-driven approach speeds up settlements, minimizes manual intervention, and improves recovery rates.

Examples include:

• An insurer leveraging AI-driven automation to streamline claim reviews and speed up settlements.

• By applying intelligent modeling, another insurer unlocked four times more recoveries, all without added time or expense.

Figure 1 collates all the AI techniques used in the above-mentioned claims processes.

Figure 1: AI techniques used across the insurance claims value chain