Anomaly Detection & MLApr 2024 – Jul 2024

Hybrid Fraud Detection System

Multi-layered anomaly detection combining deterministic heuristic rules and LLM-based signals

Hybrid PipelineHeuristic RulesLLM SignalsAnomaly DetectionHuman-in-the-LoopFinancial Risk

System Architecture & Challenge

Fraud detection systems often struggle to balance rule-based triggers with machine learning classifiers. Strict rules generate excessive false positives for normal transactions, while black-box models make it difficult to explain decisions during audits.

I built a hybrid fraud detection pipeline combining deterministic business rules with LLM reasoning signals to flag suspicious transactions and provide interpretable risk explanations.

Multi-Layered Architecture

Heuristic Validation:Deterministic checks for IP legitimacy (public, private, invalid), IP change velocity, account age thresholds, and fund sufficiency.

LLM-Based Signals:Contextual reasoning signals analyzing irregular transaction patterns and generating natural-language risk justifications.

Human-in-the-Loop:Borderline risk cases routed automatically to an administrative audit interface for human review before final execution.

Verified Key Scope

Built a multi-layered hybrid architecture integrating deterministic business rules with machine learning and LLM-assisted reasoning signals.
Engineered transactional feature representations including IP classification (public/private/invalid), account age binning, and IP change velocity ratios.
Implemented human-in-the-loop review protocols for borderline risk scores, ensuring high-risk decisions maintain interpretability and auditability.
Provided explainable risk explanations detailing exact anomaly factors for flagged transfers.