Overview
Built a project around fraud signals, risk heuristics, and cybersecurity-aware anomaly detection ideas in an insurance setting.
Problem
Fraud detection needs to combine structured claim patterns, suspicious behavior signals, and operational risk thinking.
Approach
- Identified risk indicators relevant to insurance workflows.
- Mapped potential fraud scenarios and suspicious event patterns.
- Structured analytics logic around anomaly detection and exception handling.
Results
Produced a framework for thinking about fraud monitoring beyond simple rule-based checks.
What I Learned
The biggest takeaway was that strong detection systems depend on both domain intuition and disciplined data design.