Insurance Use Case: Detecting Fraudulent Claims

A large insurer was facing a surge in suspicious claims, many including photos of "damaged" electronics and forged receipts. With the rise of generative AI, claimants could now create convincing fake evidence for less than the price of a coffee. Traditional fraud filters and manual reviews were no longer enough — leading to unnecessary payouts, higher risk exposure, and frustrated claims teams.

The Challenge

• Fake claim photos showing "damage" that never occurred
• Forged receipts generated with AI to look like legitimate receipts
• High manual review workload slowing down genuine claims

The Solution

The insurer integrated DeepXL's fraud detection API directly into their claims workflow. Every submitted photo and receipt was automatically scanned using advanced forensic checks:

• Image forensics to flag AI-manipulated damage, AI generated objects, and lighting inconsistencies
• Document authenticity verification to detect forged receipts, template re-use, and hidden edits
• Metadata analysis to confirm timestamps, device IDs, and image origins
• Explainable AI with heatmaps and evidence for reviewers to trust decisions

The Results

• Fraudulent claims identified within minutes instead of weeks
• Over 4% of suspicious submissions flagged for further review
• Faster processing for genuine claims, improving customer satisfaction
• Significant reduction in manual review workload for staff, allowing them to focus on complex cases

Impact

By deploying DeepXL, the insurer not only reduced false payouts but also improved operational efficiency and strengthened compliance with explainable, auditable decisions. The result: lower risk exposure, higher trust in claims handling, and measurable ROI within weeks.