AI Fraud Detection: What to Look for to Grow Insurance Profits

How AI Fraud Detection for Insurance Benefits Claims Teams

Fraud costs insurance carriers billions every year, consuming resources, disrupting operations, and draining profitability.

AI for insurance helps claims & investigations teams detect fraud.

But AI that "predicts" fraud is risky. That's why AI-based fraud detection that's deterministic is key; it analyzes claims data to deliver clear, defensible insights, rather than speculative risk scores.

With advanced AI that studies claims data as facts and not guesses, insurance leaders can reduce claims leakage with confidence and adopt AI for fraud detection that empowers accountability, works effectively, and enables ethical outcomes.

So is AI good at detecting insurance fraud? What does that AI look like and how can you assess it?

In this article, we'll explore:

• Insurance fraud's cost and complexity;
• What is AI insurance-fraud detection and what makes it uniquely powerful;
• How AI fraud detection enhances compliance and ethics in insurance; and
• How AI for detecting insurance fraud benefits your enterprise, your teams, your policyholders, and the future of the industry.

Why Insurance Fraud Is an Escalating Challenge

Insurance fraud costs over $308 billion annually in the U.S., a huge burden that increases policyholder premiums across all lines of business. And fraud exposure is 20x in insurance over banking, making the industry especially vulnerable.

Fraud incurs investigation costs, legal expenses, and the opportunity costs from delayed processing of legitimate claims.

Risks of Manual Fraud Detection in Insurance

Human Error

When investigators sift through vast amounts of complex data, fraud detection becomes slow, labor-intensive, and overwhelming to adjusters with high caseloads, increasing the likelihood of missing fraud or flagging false positives.

More Sophisticated Schemes

The rise of convincing and scalable attacks compounds the challenge of fraud detection without AI.

In fact, 35% of adjusters note a recent annual increase in fraudulent life & disability claims because of emerging schemes, helping to explain why 78% of carriers have dedicated fraud-investigation teams, an increase from 63% in the last few years.

Wasted Resources

With fraud rising—affecting about 10% of P&C claims—insurers are struggling to keep up.

Manual investigations or even ones supported by basic automations force claims teams to work on mundane tasks, slowing them down, wasting their expertise, and shrinking operational capacity. The end result? Backlogs, higher costs, and dissatisfied policyholders.

Policyholder Churn

Slow and inefficient claims operations exacerbated by fraud frustrate policyholders, eroding customer trust, and driving churn.

Industry leaders can optimize operations by adopting AI for insurance-fraud detection and AI for claims-processing, freeing up resources and reducing churn.

What Is AI Fraud Detection?

AI fraud detection for insurance uses document-processing and machine-learning to identify suspicious activity from claims data to help teams make better & faster fraud determinations.

Many common AI systems for insurance fraud analyze vast datasets to find deviations from "normal behavior," speculating fraud based on those deviations. But speculations are too risky for an industry as highly regulated as insurance.

Instead of relying on historical data to make risky predictions, AI insurance-fraud detection provides key insights about claims based on hard data independent of data from other claims.

AI for insurance fraud should be uplifting, empowering claims teams with autonomy over their decisions by enabling them to focus on leveraging their creativity and intuition.

Hence, good fraud-detection AI is human-centric; AI does the tedious work of uncovering hard-to-find claims data and translating complex details into helpful insights, freeing claims adjusters to use their human judgment.

How Predictions Shape AI Fraud Detection in Insurance

Quality AI models built specifically for insurance carriers designed to avoid speculative predictions use AI based on predetermined rules, such as rules that instruct the AI to only assess a claim on its own merits.

This AI eliminates randomness and, in the context of identifying insurance fraud, avoids bias.

Advanced AI can and should still utilize predictiveness to help augment certain claims insights, but only with responsible limits can AI support ethical accountability to benefit both policyholders and insurance teams.

Owl.co can predict, for example, when an employee with an injury claim should be able to return to work based on data in the claim from medical & vocational files—but AI that determines final outcomes or predicts fraud based on outside data can jeopardize insurance carriers.

How Can AI Detect Insurance Fraud?

AI detects fraud by automatically processing claims documents and analyzing the data to generate insights for adjusters to help them make potential fraud decisions.

Owl.co's AI for investigations teams, for instance, comprises multiple AI tools for optimal data precision and regulatory compliance:

• AI document automation for insurance that processes data with a custom-curated vision-language model to accurately capture structured & unstructured data and provide claims insights to help detect fraud;
• Generative AI that enables claims teams to time-efficiently research claims, ask questions, and generate documents for improved fraud investigations; and
• AI that analyzes external intelligence from diverse sources to enrich claims data with information about physical activity, income sources, major life events, vocational updates, and more.

Key differentiators of insurance-fraud detection using AI are document-processing precision, AI document classification for insurance, and AI's ability to adapt & learn for higher-quality insights.

For example, Owl.co's document-processing AI for insurance-fraud detection, OwlVision, complements technologies like optical-character recognition (OCR) with machine-learning and a vision-language model for enhanced accuracy.

And claims teams can prioritize how they value categories of fraud insights, rank the impact of different data sources, and provide relevancy feedback on individual insights to help train the AI for continuous improvements.

Such feedback empowers governability in AI fraud detection, helping to ensure compliance and fairness for claims decisions about insurance fraud.

Insurance-Fraud AI Applications Across Teams

AI fraud detection benefits every department involved in claims processing, delivering targeted solutions for each team's challenges and workflows, helping carriers offering coverage for long-term disability, property & casualty, workers' compensation, and more.

Claims-Processing Teams

Instead of manually sorting through, organizing, and researching complex claims data, frontline adjusters can better detect insurance fraud with AI to instantly glean and comprehend insights about a claimant's medical & vocational details.

For example, OwlAssist answers complex queries about claims with conversational AI, produces graphs & medical charts using non-textual gen-AI for insurance, and generates insurance documents for claims teams to augment internal collaboration.

Special-Investigation Units (SIUs)

AI is especially useful for special-investigation units researching potential insurance-claim fraud not just because of document-understanding benefits but also data enrichment.

Owl.co's AI insights tool, OwlSignal, and external-data research tool, OwlEnrich, support SIU teams by processing diverse data sources, including social media, public records, and traditionally unreachable context to deliver actionable insights about insurance fraud.

AI-powered data enrichment enables investigators to act while evidence remains fresh as the AI operates in real time, minimizing the need for constant review.

And since Owl.co's insights are deterministic and not predictive, SIUs receive a clear, data-backed evidence trail for every insight, getting the facts needed to build a strong, auditable case.

Legal and Compliance Teams

AI for detecting insurance fraud benefits lawyers by creating detailed claim-audit trails and practicing explainable AI in insurance.

This means AI explains itself with citations and evidence. Every datapoint and insight is documented and traceable, with insights backed by real knowledge, ensuring airtight, defensible cases and claims decisions.

Explainable AI eschews "black-box systems," creating the transparency required to withstand legal scrutiny and reduce the risk of discrimination & regulatory violations that might arise from inconsistent manual processes.

In short, AI-based tools for identifying, researching, and analyzing insurance fraud are ethical and explainable, supporting faster, fairer, and more accurate decisions.

AI Fraud-Detection Benefits for Insurance Carriers

While organizational commitment and choosing the right solution determine success, evidence strongly supports AI's effectiveness in fraud detection.

Insurers who use Owl.co's AI toolkit for fraud detection report a 42% on-average increase in catching ineligible claims. And Deloitte predicts that AI will save P&C insurers 20–40% in the overall cost of fraud.

Simply put, AI is good at detecting insurance fraud because it creates efficiency gains and generates helpful insights for investigators, freeing them up for higher-level work to critically analyze complex claims.

Claims Intelligence for AI Fraud Detection in Insurance

AI-based claims-fraud detection is transforming how insurance carriers combat fraud, combining accuracy, efficiency, and ethical governance.

By avoiding speculative predictions and focusing on deterministic, data-backed insights, AI enables claims teams, investigators, and compliance leaders to make faster, more defensible decisions.

Solutions like Owl.co's enterprise-level AI process claims documents, generate insights, and automate data enrichment, with transparent, auditable evidence trails, to drive results that positively impact the bottom line.

This approach leverages Claims Intelligence, which combines precise knowledge with powerful AI, empowering insurance leaders to get the appreciation they deserve by equipping their investigators with technology to combat fraud and make better claims decisions.

Book a demo to see how the Claims Intelligence toolkit for AI fraud detection delivers claims outcomes for insurance carriers that are accountable, effective, and ethical.