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Fraud Detection System
A sophisticated fraud detection analytics platform that leverages statistical patterns and anomaly detection to flag suspicious claims for investigation, protecting the organization from significant financial losses.
4 months
Insurance Industry
Power BIPythonSQLStatistical ModelingDAX
Fraud Detection System
Client Context
Insurance claims department processing 10,000+ claims monthly across motor, health, and property lines.
The Problem
The claims department was experiencing increasing fraud losses with limited tools to proactively identify suspicious patterns before claim payouts.
Pain Points
- Fraud identified only after payment in most cases
- Manual review process for flagging suspicious claims was inconsistent
- No visibility into emerging fraud patterns or networks
- Investigators lacked data-driven prioritization for case allocation
- Limited historical analysis to identify repeat offenders
Constraints
- Must not slow down legitimate claim processing
- Integration with existing claims management system
- Compliance with data protection regulations
- Need for explainable flags (not black-box decisions)
The Solution
Developed a multi-layered scoring system combining statistical anomaly detection with rule-based flagging, providing investigators with prioritized cases and supporting evidence.
Implementation
- Built statistical models identifying outliers in claim amounts, timing, and patterns
- Created network analysis to identify related claims and repeat claimants
- Implemented rule-based flags for known fraud indicators
- Developed investigator workbench with case prioritization
- Automated documentation generation for compliance
Key Features
- Real-time claim scoring as claims are submitted
- Network visualization showing related claims and parties
- Fraud pattern library with historical examples
- Investigation workflow tracking
- ROI tracking for fraud prevention efforts
The Impact
KES 12.3M
Fraudulent claims prevented in first year
45%
Increase in fraud detection rate
60%
Reduction in investigation time per case
3x
ROI on system implementation
"The fraud detection system has fundamentally changed how we approach claims review. We're now catching fraud before it costs us."
Head of Claims|Project Sponsor
Before
Reactive fraud detection, manual review, KES 45M+ annual losses
After
Proactive detection, automated scoring, KES 12.3M saved in first year