Case Study — Nexora
Cutting Fraudulent Claims Payout by $6M Annually Through AI-Assisted Claims Triage
AI agents scoring and routing high-risk claims before payment, not after
Industry
Regional Health Insurance Payer
Timeline
14 weeks
Team
6 engineers
Tech
Multi-Agent Orchestration + ML Scoring + PostgreSQL
The Challenge
A regional health insurance payer processing 4TB of claims data annually had no systematic way to flag suspicious claims before payment — fraud investigation was entirely reactive, triggered by post-payment audits sampling less than 2% of claims. Investigators were reviewing flagged claims months after payout, when recovery was difficult and evidence was stale, while an estimated $8M in fraudulent or erroneous claims was paid out annually.
Our Approach
How We Solved It
Claims Ingestion & Feature Extraction
Built a pipeline that extracts risk-relevant features from every incoming claim — provider billing patterns, procedure-diagnosis consistency, patient utilization history — in real time as claims arrive, not in a nightly batch.
Fraud Risk Scoring Agent
Trained a scoring agent on 3 years of confirmed fraud and audit outcomes that assigns every claim a risk score before adjudication, replacing the fixed 2% random-sample audit approach.
Investigator Triage Queue
High-risk claims are automatically routed to a prioritized investigator queue with the specific risk factors that triggered the flag surfaced alongside the claim — not just a score, but the reasoning.
Provider Pattern Analytics
Built a provider-level analytics view that aggregates risk signals across a provider's full claims history, surfacing systemic billing patterns individual claim review would miss.
Engineering Process
How We Built It
Real-Time Feature Pipeline
Claims features are computed as claims are submitted, not in a nightly batch, so high-risk claims can be flagged for review before payment rather than after.
Explainable Risk Scoring
The scoring agent outputs the specific contributing risk factors alongside its score, not a black-box number, since investigators need a documented rationale to justify claim holds and any resulting provider action.
Feedback Loop From Investigation Outcomes
Every investigator determination — confirmed fraud, false positive, insufficient evidence — feeds back into the scoring model's training set, so the model's precision improves as the investigation team works.
Architecture Decisions
Key Technical Choices
Score-and-Route, Not Auto-Deny
The system flags and routes for human investigation; it never automatically denies a claim. This kept clinical and financial liability decisions with licensed claims staff, a requirement from the payer's legal and compliance teams.
Provider-Level Aggregation as a Separate Layer
Provider pattern analytics run as a separate aggregation layer from per-claim scoring, since systemic provider fraud requires a different detection approach than isolated claim errors.
Audit-Grade Logging for Every Flag
Every risk flag, its contributing factors, and the resulting investigator decision are retained in an immutable log — required both for internal audit and to defend provider actions if disputed.
Results
What We Delivered
Solution Blueprint
How It All Fits Together
- Real-time claims feature pipeline
- 3-year fraud/audit training history
- Provider billing pattern store
- Explainable fraud risk scoring agent
- Provider-level pattern analytics
- Investigator feedback loop
- Investigator triage queue
- Provider action dashboard
- Immutable audit log
Lessons Learned
What We Improved
Explainability Was Not Optional
The compliance team rejected the first black-box scoring model outright. Rebuilding it to surface contributing factors was what got it approved for production use.
Feedback Loops Compound Fast
Model precision improved measurably within the first quarter once investigator outcomes started feeding back into training — the biggest gains came from the cases the model got wrong, not right.
2% Random Sampling Was Hiding the Real Problem
Moving from random-sample audits to risk-based review surfaced systemic provider billing patterns the old approach had never had the coverage to detect.
More From Nexora
Related Case Studies
Consumer & Commercial Lending
Loan Origination Workflow Hub
Multi-agent workflow automation replacing manual underwriting handoffs
Multi-Specialty Health System
Clinical Prior-Authorization Automation
AI agents assembling clinical evidence and predicting approval likelihood before submission
Multi-National Financial Holding Group
Multi-Entity Regulatory Reporting System
AI agents reconciling and assembling regulator-ready reports from fragmented entity data
Related Research
Research Reports for This Industry
Enterprise AI Adoption Trends 2026
Enterprise AI has crossed the operational threshold. Seventy-two percent of Fortune 500 organizations now run at least one AI system in production — and the average enterprise manages 3.4 concurrent AI initiatives. This report maps the state of enterprise AI across healthcare, manufacturing, financial services, retail, and beyond.
Read reportHealthcare AI Trends 2026
Healthcare AI is the fastest-growing sector in enterprise AI investment, projected to grow from $45.2B (2025) to $187.4B by 2030. This report examines clinical AI maturity, administrative automation ROI, and the emerging regulatory frameworks that will define healthcare AI deployment strategy through 2028.
Read reportSaaS Development Benchmarks 2026
What does it actually cost to build and scale a SaaS product in 2026? This report benchmarks engineering team size, deployment frequency, infrastructure spend, and time-to-market across 521 SaaS companies — from $1M ARR seed-stage startups to $100M+ enterprise SaaS leaders.
Read reportAI Agent Adoption Report 2026
AI agents are the most transformative enterprise technology category of the 2025–2026 cycle. This dedicated report examines architecture patterns, deployment economics, governance approaches, and the emerging multi-agent production landscape across 634 organizations — the most comprehensive agent-specific enterprise research available.
Read reportPricing Intelligence
Related Cost Guides
Transparent pricing breakdowns to help you plan and budget your technology investments.
Decision Intelligence
Related Compare Guides
Side-by-side decision frameworks to help your team choose the right technology approach.
Monolith vs Microservices: The Architecture Decision That Defines Your Engineering Velocity
Start with a well-structured monolith. Decompose into microservices only when you have specific, measured scaling proble
AI Agent vs Traditional Automation: What's the Difference and Which Do You Need?
Use traditional automation for deterministic, rule-based workflows. Use AI agents for tasks requiring judgment, language
Explore Further