Fraud Pre - Screener
Detect anomalies and fraud in health claim submissions
Description
Challenge:
Fraudulent or anomalous health claims drain insurer resources and increase operational costs. Traditional fraud detection often occurs late in the process—sometimes even after payout—leading to financial leakage, reputational damage, and regulatory exposure. Manual fraud checks are slow, inconsistent, and difficult to scale, especially with growing claim volumes and increasingly sophisticated fraud patterns.
How It Works:
The Fraud Pre-Screener applies a combination of rule-based checks, anomaly detection, and statistical thresholds to identify suspicious claims. It analyzes structured claim summaries, billing data, medical details, and historical behavior patterns to detect red flags such as mismatched treatments, duplicate claims, inflated charges, and provider collusion. Each suspicious case is scored and flagged in a red-flag report, which is routed to fraud investigators or adjudication teams for targeted manual review. Continuous learning from historical anomalies improves accuracy over time.
Benefits:
Detects fraud before payout, reducing financial losses
Improves fraud team productivity by 30–40% via targeted review
Ensures 92%+ detection rate for common anomalies
Provides standardized red-flag reports for audit and regulatory compliance
Strengthens trust by maintaining consistent, objective fraud detection standards
Enhances operational efficiency by reducing manual triage workload
Resources
Features
The Fraud Pre-Screener integrates into the claims workflow to analyze structured and unstructured claim data in real time. It generates early-warning fraud signals, enabling proactive prevention and faster fraud case management.
Features & Capabilities:
Anomaly Detection Engine: Applies medical, financial, and policy checks to detect outliers.
Duplicate Detection: Identifies overlapping or resubmitted claims.
Code-Procedure Validation: Matches treatments with diagnosis codes (ICD/CPT).
Charge Benchmarking: Flags inflated bills against regional medical cost standards.
Provider Pattern Analysis: Monitors repeated or unusual claim activity from the same provider.
Red-Flag Reporting: Generates structured anomaly reports with clear evidence.
Cross-System Integration: Connects with claims management, fraud analytics, and adjudication systems.
Audit-Ready Logs: Maintains detailed anomaly detection history for compliance.
Operating Blueprint
Knowledge Sources:
Policy Documents: Provides guidelines and conditions required for underwriting document validation.
Regulatory Standards: Ensures documents comply with industry and legal requirements.
Underwriting Checklists: Acts as a baseline for verifying mandatory documents.
Historical Case Records: Offers examples and benchmarks for validating new document submissions.
Business Rules Repository: Supplies predefined rules for identifying missing or invalid documents.
Business Rules:
Code-Procedure Match Rule: Treatment must align with diagnosis; flag if inconsistent
Duplicate Claim Rule: Check metadata for overlapping submissions
Charge Outlier Rule: Flag charges exceeding thresholds (e.g., +50% regional average)
Treatment Pattern Rule: Detect repeated or unusual treatment sequences by provider/patient
Incomplete Case Rule: Block anomaly check if mandatory data is missing
Tool Workflow:
Receive structured claim summary and documents (via Claims Summarization Agent)
Apply anomaly detection: code checks, duplicate scans, cost benchmarking, treatment pattern analysis
Generate red-flag report with anomaly details
Route flagged claims to fraud or adjudication team for manual review
Log detection results for audit trails and model refinement
Badges
Classification
Capabilities
