AI Claims-Data Insights for Better Insurance Decisions

Claims teams spend countless hours sifting through medical records, social posts, workplace documents, and more to piece together an insurance claim.

With so much disorganized & disparate data to uncover and comprehend, teams struggle to find time to grasp critical claims insights, increasing leakage and frustrating claimants.

Meanwhile, these bottlenecks leave insurance executives wondering how to streamline operations to satisfy policyholders and keep up with competitors.

Leveraging AI to extract claims-data insights liberates claims teams to harness their creativity and intuition. AI can parse raw data into precise, contextualized intelligence, enabling faster, more accurate, and more defensible decisions.

That’s the benefit of AI insights from claims. Rather than trade human judgment for efficiency, it frees adjusters and investigators to focus on what they do best, applying their experience to understanding complex situations.

This article about insurance-claims data covers:

• The operational benefits of digital insights for claims-processing;
• Types of internal & external claims-data sources that AI can handle;
• How AI delivers actionable claims-data insights at scale; and
• A framework for human-centric AI in insurance to analyze claims data.

How Insurance Teams Use Claims-Data Insights

AI produces measurable improvements across every aspect of claims operations.

Digital insights for claims benefit insurance teams by leveraging AI built for accuracy and speed. Document automation for insurance-claims processing analyzes data from medical records, doctor notes, employment documents, and more in bulk within seconds.

AI works with precision too to optimally understand documents. AI-powered, custom-curated vision-language models read and draw connections between every piece of context, written or visual, summarizing relevant information, highlighting sources, and organizing findings in easy-to-digest claims-insights reports.

Benefits of AI Insights about Claims Data for Insurance

The top benefits of claims-data insights powered by AI include:

• Boosted fraud detection to lower claims leakage
• Improved claims-processing for better & faster decisions
• Enhanced compliance & ethics for fair outcomes

Fraud Detection

Claims-data insights support enhanced AI fraud detection to empower insurers to enhance fairness and enable them to boost the bottom line.

AI claims insights reveal patterns like inconsistencies in employment or medical histories or external data that contradicts submitted info—all with citations for defensibility—to help claims teams quickly determine ineligibility and mitigate false positives.

Claims Processing

Using AI for claims processing frees up claims teams, giving them time to think critically about complex details and apply their professional intuition where it matters most.

AI data insights enable teams to focus intellectual & mental resources on nuanced decision-making, complicated determinations, and claimant interactions instead of spending most of their time on administrative tasks.

Compliance and Ethics

From a compliance and ethics perspective, AI-powered insights provide the explainability and audit trails that regulatory environments demand.

Explainable AI in insurance ensures every decision point, data source, and analytical insight is documented and traceable, supporting defensible claim decisions that build customer trust.

What Are Examples of Claims Data?

Claims data falls into two primary categories: internal data directly contained within claim documents and external data gathered from outside sources.

Understanding examples of claims data reveals the breadth of information that modern AI systems can analyze and transform into actionable insights.

Types of Internal Claims Data for AI Insights

Examples of internal claims data from which AI can generate fast and precise insights include (but are not limited to):

• Doctor-appointment findings
• Employer & employee submissions
• Hospital-record findings
• Insurance-coverage details
• Job duties & roles
• Medical treatments/diagnoses
• Prior injuries
• Rehab details
• Third-party liability considerations
• Vocational assessments

1. Internal Data Sources for Insurance Claims

Internal insurance-claims data includes information in all the traditional documents that accompany a claim submission. Let’s review some examples and how AI can quickly extract accurate & helpful insights from this data.

Medical Documentation

Medical documentation is the core of many claims analyses. Treatment records from hospitals, clinics, and individual practitioners provide detailed diagnostic information, prescribed treatments, and therapeutic interventions.

These records explain the current medical condition, pre-existing injuries, ongoing health issues, and the effectiveness of treatment over time. Notes on recovery progress are useful for tracking timelines and functional improvements.

Insurance teams often bring in specialists to offer expert opinions on long-term prognosis and disability assessments. Together, this medical evidence establishes both the extent of the current injuries and the claimant’s baseline health status prior to the incident.

AI claims-data insights about medical records can clarify injury details with precision, visually map injury timelines or updates on body-part diagrams, confirm or question test results, highlight inconsistencies between doctor notes & treatment plans, and more.

Employment Documentation

For workers’ compensation & disability claims, employment and vocational information is critical. Job descriptions detail specific physical requirements, such as lifting capacity, hours spent standing, repetitive motions, or hazardous exposures related to claimed injuries.

Plus, workplace-safety records, incident reports, and supervisor statements establish the circumstances surrounding workplace accidents, while salary records and employment history verify income claims and establish earning capacity for disability calculations, supplemented by assessments from experts, like occupational therapists, who can help gauge how long it might take to get back to work or long-term impacts.

AI claims insights reports can quickly summarize career histories or job circumstances, explain relationships between vocational & medical details, predict return-to-work timelines based on injury & rehab statuses, and more.

How to Analyze Internal Claims Data

AI generates insights via document automation for insurance-claims processing. AI digests (receives claims documents) and ingests (reads & understands claims documents) via a custom-curated vision-language model for enhanced accuracy and speed.

OwlVision excels at handling this complexity by leveraging AI-powered document automation for insurance claim processing.

Unlike simple text extraction, the system processes handwritten doctor notes and images of injuries with the same precision as text on digital PDFs. Then, it draws relevant connections and structures the data from every source in one place.

Consider a workers' compensation claim involving back injury. OwlVision can:

• Automatically cross-references the claimant's job description (requiring "frequent lifting up to 50 pounds") against medical restrictions ("no lifting over 15 pounds")
• Flag discrepancies between the employee's injury report ("slip and fall while lifting boxes") and the supervisor's incident statement ("no specific incident reported")
• Identify that MRI findings show degenerative changes predating the claimed injury date.

AI-powered insurance document generation presents these insights in organized summaries with direct citations to source documents, helping adjusters quickly focus on the most critical inconsistencies.

This level of contextual analysis across multiple document formats and sources, from interpreting cursive handwriting in medical charts to extracting timestamps from digital employment records, is far beyond traditional document processing, achieving both speed and accuracy.

2. External Data Sources for Insurance Claims

External claims data expands the analytical scope beyond submitted documents to include real-world verification sources that provide independent confirmation or contradiction of claimed circumstances.

Social Media and Digital Activity

Social media platforms reveal claimants' actual activities and capabilities through posts, photos, and location data.

Digital footprints reveal travel patterns, recreational activities, and social engagements that help verify or challenge claimed restrictions. For example, a disability claimant posting vacation photos showing physical activities inconsistent with reported limitations is crucial evidence for claim evaluation. In 2024, an injury case made headlines when a claimant’s $820,000 insurance claim was dismissed in court after pictures of her winning a tree-throwing competition surfaced.

Professional networking profiles, too, might display current employment status, job responsibilities, and career developments that may contradict disability or unemployment claims.

These digital insights for claims often provide timestamped, location-verified evidence of actual behavior rather than self-reported limitations.

Financial and Vocational Information

Employment verification through multiple sources helps identify discrepancies in reported earnings, job status, or work capacity.

Tax records, payroll databases, and professional licensing information provide objective measures of income and employment that may differ from self-reported data. Memberships for gyms, clubs, and recreational activities can be a financial trail to information about physical capabilities.

Major life events, such as marriages, divorces, job changes, or relocations, often correlate with claim timing and can reveal motivations or circumstances that affect a claim’s validity.

Legal Records

Property records, vehicle registrations, and asset information support broader financial investigations. Adverse conditions such as legal troubles, arrests, bankruptcies, or financial difficulties provide context for claim motivations or suggest organized fraud schemes. Similarly, court records, lawsuit histories, and legal judgments help track claims activity or involvement in litigation that may indicate professional claimants or organized fraud.

How External Data Sources Intersect

Together, external verification sources create an independent verification framework that either supports or challenges the narrative presented in internal claim documents.

However, gathering and analyzing this diverse external information manually would require weeks of investigation per claim. Since external sources are open-ended, there’s a much higher chance of missing key evidence.

OwlSignal automates this external data gathering process, monitoring internet-based sources to provide comprehensive claims insights reports that cross-reference findings with internal claim information.

Consider the same workers' compensation back injury claim. OwlSignal can:

• Identify social media posts showing the claimant engaged in recreational softball two weeks after claiming inability to lift objects.
• Discover employment records indicating concurrent work at a second job requiring physical labor.
• Flag financial pressures from a recent bankruptcy filing that occurred two months before the injury claim.
• Cross-reference the claimant's stated income against tax records showing significantly higher earnings.

Unlike predictive AI models, there is no guesswork. OwlVision and OwlSignal build cases based on facts.

These AI systems know where to find and how to analyze claims data, presenting external findings alongside internal document analysis, ultimately helping identify contradictions, verify claims, and build transparent cases.

The most effective AI document classification for insurance creates intelligence that would be virtually impossible to achieve through manual processes alone, let alone at the same speed.

Scientific & Industry Research Supports AI-Driven Claims Data Analytics

Scientific and industry research consistently demonstrates the value of systematic AI insights from claims.

Rigorous technical research on healthcare insurance fraud detection, published in Risks (2023), demonstrates AI models achieving 98.21% accuracy and 100% recall.

Industry reports further support the efficacy of AI claims analysis, with recent research by Accenture finding that underwriters spend up to 40% of their time on non-core and administrative activities. Insurers stand to gain up to $160 billion by 2027, through using AI to reduce underwriting operating costs.

PwC’s implementation of explainable AI in auto insurance showed 29% efficiency savings during vehicle damage assessment, measurable improvements in customer experience, and reduced rework.

However, the benefits don’t end there. In the case of Compensa Poland, the company implemented an AI-based system with the explicit goal of improving customer satisfaction, but were surprised to find another benefit: a 10% improvement in claims accuracy.

Rather than generic solutions, insurance leaders can capitalize on a new wave of innovation by adopting a dedicated AI tool for claims data insights.

Transforming Operations Under A Claims Intelligence Framework

As the insurance industry evolves toward more sophisticated risk assessment and customer service, organizations that master intelligent claims analysis will set new standards for excellence.

During this shift, how you approach claims data analytics makes all the difference.

A Claims Intelligence framework emphasizes knowledge-based decision making over statistical speculation. It recognizes that every claim has unique circumstances that deserve individual analysis rather than an algorithm making assumptions. The system analyzes specific facts to build evidence within each claim, supplemented by relevant internal and external data.

This methodology aligns perfectly with regulatory expectations regarding AI for claims processing. Decision-makers can understand exactly why the system flagged specific elements, traced particular connections, or recommended certain actions. Its transparency proves essential for legal defensibility and building policyholder confidence.

As adjusters interact with the system, their insights refine analytical capabilities while providing human empathy and judgement. Rather than being replaced, their position becomes all the more vital as the “humans in the loop”.

Modern claims data insights aren’t just faster and more accurate, but more complete and transparent. The Claims Intelligence approach ensures these technological capabilities serve human goals: fairer claim decisions, more efficient operations, and stronger relationships with policyholders.

Ready to transform your claims operations with intelligent AI insights from claims? Book a demo to see how our comprehensive AI toolkit can empower your team to make better, faster, and more defensible decisions.