AI Claims Data Insights for Better Insurance Decisions

How AI Insights Help Claims Teams Analyze Data to Make Better 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.

Leveraging AI to extract 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 claims insights 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.

And AI works with precision to optimally understand documents.

AI-powered vision-language models read and draw contextual connections between written and visual data, summarizing relevant information, highlighting sources, and organizing findings in claims insights reports.

Benefits of AI Insights about Claims Data for Insurance

Research shows that insurance carriers see a 20–300% return within a year when investing in AI for claims processing, inclusive of AI that generates insights from claims data.

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

For compliance and ethics, 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 claims decisions that build customer trust.

What Are Examples of Claims Data?

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

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

1. Internal Data Sources for Insurance Claims

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

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

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 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.

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.

2. External Data Sources for Insurance Claims

External claims data expands the scope for insights beyond data in claims documents to include real-world verification sources to confirm or invalidate claimed circumstances, helping claims teams work efficiently and reduce leakage.

Types of External Claims Data for AI Insights

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

• Arrests or incarcerations
• Income sources
• Marriages or divorces
• Physical activities
• Social-media posts
• Sports activities
• Travel activities
• Vocational activities

Social Media and Digital Activity

Social media reveals claimants’ actual activities and capabilities through posts, photos, and location data.

Digital footprints reveal recreational activities, and social engagements, and travel patterns to qualify claims. For example, a disability claimant posting photos showing physical activities inconsistent with reported limitations is crucial evidence for an evaluation.

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

Financial and Vocational Information

Verifying employment details through AI insights from external claims data 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.

AI insights can reveal gym memberships, job changes, major life events like divorces and more to highlight circumstances that can affect a claim’s validity.

Legal Records

AI insights from legal records not reported in claims documents are also invaluable to claims teams.

Asset information, court & property records, unknown lawsuits, vehicle registrations, and more support broader financial investigations and make tracking claims activity or litigation involvement easier to gain contextual knowledge about a claim.

How to Analyze Claims Data

Internal Claims Data

AI generates insights via automatically processing claims documents. AI digests (receives documents in bulk) and ingests (reads & understands documents with lightning speed) via a custom-curated vision-language model for enhanced accuracy and speed.

OwlVision leverages AI document automation for insurance claim processing to provide powerful insights with citations.

Claims insights are presented both in customizable dashboards with AI document classification for insurance or via Owl.co’s gen-AI tool OwlAssist so adjusters can make unique queries about claims data.

And OwlVision offers AI-powered insurance document generation for creating internal documents about claims data based on adjustable templates, helping investigators quickly collaborate around claims insights.

This level of contextual analysis from document processing across multiple data points for claims insights enables claims teams to act with speed and precision for fast & accurate decisions.

External Claims Data

External claims data creates an independent verification framework to cross-reference narratives presented in internal claims documents.

But gathering and analyzing this diverse external information manually takes time, and not all data is easily discoverable.

OwlSignal automates the data-gathering process of external information, monitoring online sources to provide comprehensive claims insights reports that cross-reference findings with internal claims data.

Coupled with OwlEnrich, which offers manual reviews of external information to augment data enrichment, the AI provides fully comprehensive insights.

Claims teams can also provide user feedback to rate the relevance of findings and priority-rank different types of data so the AI can self-improve the quality of its insights reports.

Leveraging AI Claims Data with Claims Intelligence

As the insurance industry evolves adopting AI for enhanced insights, carriers that master intelligent claims analysis will set new standards for excellence.

These carriers seize Claims Intelligence, which combines powerful AI with precise claims data to empower insurance leaders to transform claims processing and become industry stewards.

Claims Intelligence emphasizes knowledge-based decision-making over statistical speculation, recognizing that every claim has unique circumstances that deserve individual analysis rather than an algorithm making assumptions.

With Claims Intelligence, teams can analyze data insights to better utilize their creativity and intuition for human-centric outcomes, empowering insurance leaders to scale operations with AI that’s accountable, effective, and ethical.

Ready to transform your claims operations with AI claims insights? Book a demo to see how the Claims Intelligence AI toolkit enables claims teams to make better, faster, and fairer decisions.