Forensics Today

Harnessing AI for claims administration: A how-to guide

March 10, 2026

Administrators of large-scale settlement and victim compensation programs (multidistrict litigation, mass torts, class actions, alternative dispute resolution frameworks, etc.) are often challenged by labor-intensive processes, disparate data sources, and documentation issues, even as they face growing stakeholder demands for increased speed, quality, fairness, and cost savings.
Claims programs that don’t modernize could risk inviting scrutiny and eroding the trust of claimants and other settlement parties.
Meeting this moment will require embracing AI-enabled capabilities that are responsible, secure, and properly supervised. It also requires involving a trusted specialist with a proven track record of AI transformation in claims operations.

Traditional claims processes are increasingly strained by manual workflows and inconsistent documentation. At the same time, administrators face mounting pressure from claimants, insurers, regulators, and other stakeholders to deliver faster and more transparent outcomes, while also maintaining compliance and managing costs.

Administrators are also under increasing pressure to use AI for optimal speed, accuracy and efficiency. Generative AI is already moving from hype to practical adoption, with applications now in use across the claims process from intake to payment. The next wave—agentic AI—will enable autonomous agents to conduct first-pass claim reviews and execute more complex workflows, further accelerating what administrators can achieve at scale.

Programs slow to adapt could risk greater scrutiny and diminished trust. Facing the challenge will require AI-enabled transformation that embraces strong guardrails and human oversight to address concerns around accuracy, fairness, and transparency. To get there, you’ll need support from an external advisor with deep knowledge and proven experience applying AI in claims operations.

For today’s administrators, the question is no longer whether to adopt AI but how to do it responsibly. The opportunities ahead show how AI, when applied thoughtfully, can redefine what’s possible in claims administration.

How AI is helping claims administrators

AI is helping transform claims administration, supporting the overall process through automation, data synthesis, and predictive modeling. By handling routine work and synthesizing data across multiple sources, it can streamline operations, improve decision-making, and free human reviewers for higher-value activities.

Here are seven practical examples where AI can create value across the claim life cycle.

AI helps automate intake by categorizing and tagging thousands of incoming documents such as medical and legal records, claim forms, and supporting documents to check that submissions are complete.

Example: An AI-driven workflow is already in use to intake and classify documents into defined document types according to their content (payment information, medical records, claim form, etc.). Where documents are identified as claim forms, an AI agent performs the initial review to verify that they meet certain requirements. This includes confirming that signature lines are filled, typed names match signatures, and all other required fields are completed. It also verifies that all pages are returned with the signed claims forms and that there are no annotations or other changes to the form.

Oversight and controls: Incorporate human-in-the-loop spot checks, periodic sampling of auto-classified documents, and rule-based validation to confirm accuracy and verify that AI output remains aligned with program protocols.

Benefit: Accelerates the initial screening process or “first-pass review” of claim forms that was previously a manual exercise.

AI can systematically route claims into the right workflows based on claim type, complexity, and/or severity, helping administrators prioritize urgent claims and focus attention where it’s most needed.

Example: An AI-enabled workflow analyzes claim details, supporting documentation, and historical claim data to help determine the appropriate routing path. It verifies that required information is present, flags incomplete or inconsistent submissions, and directs urgent or high-risk claims to specialized teams for expedited handling and/or additional due diligence.

Oversight and controls: Assess routing decisions through routine sampling, audit reports of high-risk claim pathways, and clear escalation rules so reviewers can override AI assignments when claim complexity warrants human judgment.

Benefit: Enables faster, more accurate claim intake and prioritization, helping reduce administrative burden and improve response times for critical cases.

AI can pull key facts, such as policy details, dates, and claimant demographics, from unstructured materials and turns it into structured data for faster review and analysis.

Example: AI helps to quickly summarize key attributes in claim files, significantly expediting the review process. It can also organize information from otherwise unstructured data, making scaled analysis possible.

Oversight and controls: Implement structured quality checks such as review sampling, automated field-level validation, and reconciliation against source documents to confirm extracted data remains accurate, thorough, and consistent with program standards.

Benefit: Provides greater usable data, faster insights, and more consistent review outcomes while adhering to the program’s standards.

For selected steps in the claims process or categories of claims identified through a risk-based assessment, such as lower-complexity claims or steps requiring limited human judgment, AI can perform a structured first-pass review of the claim file to accelerate evaluation while preserving human judgment where it matters most.

Example: An AI-enabled workflow conducts a primary review of the claim file by analyzing information across submitted materials, including claim forms, supporting documentation, correspondence, and extracted data fields. The AI checks for internal consistency (dates, claimant identifiers, injury descriptions, eligibility criteria), flags discrepancies or missing information, and evaluates the claim’s alignment with program protocols.

Oversight and controls: Use a risk-based approach to oversight based on claim type, risk level, and AI-generated flags. Higher-risk or exception cases may be reviewed by a human as a secondary reviewer while lower-risk claims may be monitored through sampling and quality checks. Audit trails and regular model reviews help confirm the AI remains aligned with program rules and fairness standards.

Benefit: Accelerates claim processing by reducing time spent on manual file review, improves consistency across reviews, and enables human reviewers to focus on judgment-intensive decisions, enhancing efficiency and fairness without replacing professional discretion.

AI can triage external inquiries (claimants, attorneys, other third parties), route them to the right team, and draft initial responses.

Example: The AI workflow compares inquiries to a library of FAQs and prior responses to determine the appropriate routing, sending payment questions to the payments team and claim-form inquiries to the review team. It then drafts suggested responses using program protocols and continues to improve as it learns from historical interactions.

Oversight and controls: Maintain an approved response library and establish required human review of drafted responses based on the program’s governance framework considering complex or claimant-impacting inquiries to confirm accuracy and prevent inappropriate automated communication.

Benefit: Enables faster response times and more consistent messaging, reduces manual effort, and helps improve efficiency and lower administrative costs.

AI can highlight anomalies or patterns in claimant submissions that may indicate potential fraud or systemic risk and require human follow-up. This strengthens accuracy and helps maintain program integrity.

Example: AI models can detect indicators of potential bad actors by analyzing metadata such as IP addresses—flagging, for instance, logins originating abroad in a US-only program—and cross-referencing those signals with other data sources to surface suspicious patterns for human review.

Oversight and controls: AI-flagged cases undergo human investigation and secondary review, supported by audit logs, threshold-based escalation criteria, and periodic recalibration to verify that risk indicators remain reliable and free from unintended bias.

Benefit: Accelerates the investigative process without sacrificing thorough research, enabling fraud investigators to quickly assess the allegations’ credibility and focus resources on high-risk claims.

AI and predictive analytics forecast settlement timelines and distribution amounts, helping administrators anticipate total payouts based on eligible and ineligible claims and program duration under certain informed assumptions. This helps guide program planning and resource allocation.

Example: AI workflows can summarize claimant data and produce preliminary eligibility and award estimates, then model scenarios involving staggered funding, multiple distribution cycles, and optimal holdback levels. This helps foster more precise program planning and financial stewardship.

Oversight and controls: Validate model outputs through scenario testing, back-testing against known data, and governance routines that review underlying assumptions, helping to confirm that projections remain transparent, explainable, and aligned with program rules.

Benefit: Enables stronger risk controls, better forecasting, and fund integrity.

Each of these use cases demonstrates how AI automation and intelligence can reduce administrative burden and accelerate payments to claimants. Together, they help programs deliver faster and fairer outcomes at scale.

Responsible AI is foundational

With every new capability comes a new layer of responsibility around reliability, fairness, security, and compliance. Realizing AI’s benefits requires a thoughtful, responsible approach that balances innovation with control. This includes developing a risk framework that calibrates AI use to factors such as claim complexity, claim value, and the sensitivity of decisions involved.

Administrators should work with their trusted advisor to design AI-enabled claims programs, including an AI risk and governance framework, to address risks and embed the right safeguards throughout the process.

Hallucinations: Chatbots or models can produce plausible but incorrect outputs. AI models may generate incorrect summaries, misinterpret claimant statements, or introduce data points that never appeared in the record—creating risks to claim accuracy, auditability, and fairness. Programs should incorporate risk-based, human validation of AI-generated outputs, implement prompt and model testing, and restrict AI use to standardized, well-structured inputs to reduce potential for error.
Bias: Systemic bias embedded in AI models can influence claim prioritization. Without careful design and monitoring, AI may inadvertently favor or deprioritize certain claimant groups, claim types, or documentation patterns—undermining neutrality and consistency across the program. Programs should test models for disparate impact, use balanced training data, document decision logic, and maintain human oversight for sensitive or claimant-impacting determinations.
Compliance: Undisclosed automation, opaque decision logic, or inconsistent application of program rules may conflict with court-mandated protocols, regulatory requirements, or fairness expectations critical to defensible claims administration. Programs should adopt transparent disclosure practices, maintain auditable AI decision logs, and confirm that AI-enabled workflows align with program protocols and legal requirements before deployment.
Cybersecurity: Using claimant data in open or externally hosted AI models can introduce security and privacy risks. Programs should verify the AI’s infrastructure, data-segregation controls, and privacy safeguards before enabling any data exchange.

These aren’t barriers to AI adoption. They’re design requirements to make AI more trustworthy and effective.

Recent incidents in large claims programs have shown how even small data or process gaps can escalate into reputational and operational issues. As AI reshapes claims operations, maintaining quality and human oversight will remain essential, as will a clear roadmap for Responsible AI implementation.

A how-to implementation guide for administrators

Translating AI ambition into action requires a deliberate, structured approach. These steps provide a practical path forward to integrate AI safely and effectively across claims programs.

Choose an experienced, trusted advisor. Work with external specialists who combine deep technical expertise with firsthand experience deploying AI in claims operations. The right collaboration can build confidence, support responsible implementation, and lay the foundation for each subsequent step.
Identify high-ROI opportunities. Pinpoint areas with the highest return, things like repetitive, time-consuming processes where automation can create measurable value. Prioritize low-risk, high-volume activities that are easier to implement and can yield quick wins such as claim intake and document triage.
Break down tasks to design an effective AI workflow. Deconstruct each area into smaller, clearly defined tasks. Establish clear decision boundaries between AI-driven steps and human oversight using verified data sources to reduce error or bias.
Test your progress with pilots. Start with chatbot-enabled document review or triage pilots before expanding or moving to adjudication support. Use the pilot to evaluate how different AI models—machine learning, generative and agentic systems—can enhance claims administration. Include fairness and accuracy reviews in pilot evaluation criteria to detect bias and meet disclosure requirements.
Keep humans in the loop. Require reviewers to assess AI outputs before acting on them. Mandate human review for sensitive or claimant-facing decisions to preserve fairness and empathy, especially where claimants may be coping with loss or are in vulnerable circumstances.
Establish a governance framework. Build a strong risk and control environment for AI-enabled claims operations by defining clear policies, decision boundaries, documentation requirements, and oversight mechanisms. This includes establishing AI and chatbot audit trails as well as developing a broader risk framework that identifies where automation is appropriate, sets error-tolerance levels, outlines escalation paths, and embeds controls for consistency with program protocols, fairness expectations, and regulatory or court-mandated requirements.
Formalize cross-disciplinary oversight. Engage claims specialists, legal, risk, and IT to monitor AI outputs.
Embed Responsible AI principles. Require explainability and traceability of AI recommendations. Communicate AI use clearly and review outcomes for fairness and proportionality.

Choosing a trusted specialist

A successful program depends on who helps deliver it. Choosing tech integration vendors with proven experience in AI-driven claims operations can be key to executing each step effectively and turning plans into measurable results.

When evaluating potential advisors, prioritize those with deep experience implementing AI tools in claims administration, including integrating AI with legacy claims systems. Vet them for data security certifications, explainability, and alignment with regulatory requirements. Confirm they have proven experience in operational, regulatory, and human dimensions that define claims administration.

Consider asking questions such as the following.

Implementation maturity: How and in which cases have you deployed AI and other technology in claims environments? What measurable results were achieved?
Data governance: How is claimant data secured, anonymized, and audited? How is claimant data safeguarded within the AI model environment, including controls to prevent storage, reuse, or cross-program exposure?
Model transparency and accountability: Can you explain how your models reach decisions, and how you monitor their performance over time? What guardrails do you have in place to monitor and mitigate risk? Can you demonstrate your existing technologies?
Integration: How does your solution interface with existing claims platforms and workflows?
Compliance and oversight: How do you confirm alignment with evolving regulatory and ethical requirements?

Advisors with the right experience can bridge the gap between innovation and governance, helping to confirm that technology improves existing operations. Equally important is moving from planning to timely execution to effectively leverage the value of AI.

Bottom line

As AI adoption gains momentum across the industry, programs that don’t adapt can pose risks both operationally and reputationally. When implemented responsibly, AI can streamline processes, reduce administrative costs, and direct more funds to claimants, helping to close the gap between what programs can deliver and what stakeholders expect.

Contact us

Ryan Murphy

Partner, Global Investigations & Forensics Leader, PwC US

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