Accelerating Claims Investigations with AI
A Tier 1 insurance carrier wanted to accelerate fraud detection and decision-making using AI - improving analytical accuracy and compliance while reducing false positives and investigation time across high-volume transactions.
Outcomes
3x
Faster Investigation Turnaround
40%
Fewer Manual Document Reviews
20%
Cut in Fraudulent Payouts
$100k
Average PoC Cost Saving Per Vendor
Technology Vendors Suited to Evaluation
Business Problem
The insurer’s fraud investigation process was heavily manual, slow, and resource-intensive, making it difficult to keep pace with rising claim volumes and evolving fraud patterns. With global insurance fraud costs exceeding $25 billion annually and loss adjustment expenses climbing, legacy systems limited the ability to deploy AI-driven detection tools.
The organisation needed a faster, more scalable way to identify fraudulent claims, reduce operational costs, and modernise its fraud prevention capabilities.
Challenges
- Manual, slow review of large and diverse claim documents
- $25 B annual global cost of insurance fraud in P&C lines
- Rising Loss Adjustment Expenses (LAE) and loss ratios
- Legacy tech hindering AI adoption & innovation
From Idea to Evidence with NayaOne
The insurer used NayaOne’s secure sandbox to validate AI-powered fraud detection tools quickly and safely, replicating real-world investigation conditions without exposing live data.
- Vendor Showcase: Multiple GenAI-based fraud investigation vendors were evaluated side by side in a controlled environment.
- Hands-On Testing: Synthetic, multi-format claims packs and historical fraud scenarios were used to simulate real investigations.
- Compliance Safe: NDA-secured vendor access was granted on Day 1, ensuring zero exposure of production or customer data.
- Rapid Requirements Gathering: Focused on anomaly detection, fraud pattern identification, and claims triage optimisation.
- Model Training & Evaluation: Models were trained on synthetic claims data labelled with known fraud patterns and assessed for accuracy, explainability, integration effort, and customer experience impact.
The sandbox enabled rapid, compliant experimentation and clear evidence of which AI solutions could most effectively enhance fraud detection speed, precision, and transparency.
Impact Metrics
PoC Timeline Reduction
8 weeks with NayaOne vs 12 – 18 months traditionally
Time Saved in Vendor Evaluation
1+ year
Decision Quality
The bank gained hard evidence on detection accuracy, speed, and integration fit - enabling a data-driven vendor choice and faster approval across risk and procurement.
KPIs
- Fraud Detection Accuracy (%): Correct identification rate of fraudulent claims.
- False Positive Rate (%): Percentage of legitimate claims wrongly flagged.
- Investigation Time Reduction (%): Improvement in average case resolution speed.
- Model Explainability Score: Transparency and auditability of AI decisions.
- Operational Cost Reduction (%): Savings achieved through automation and faster detection.
Validate AI Fraud Detection Solutions Before Deployment
Use NayaOne’s secure sandbox to test and compare AI-powered fraud detection tools using synthetic claims data — measuring accuracy, explainability, and investigation efficiency before integration into production systems.
