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GraphGuard
A fraud detection system combining graph analytics (Neo4j), explainable ML (XGBoost + SHAP), and a GraphQL API to uncover hidden transaction rings and money laundering patterns.
Technologies & Skills
Challenge
Traditional fraud rules miss complex network patterns like circular transactions and mule accounts used in money laundering.
Solution
Implemented a graph-first approach using Neo4j to detect cycles and community structures, paired with an XGBoost classifier and SHAP explainability to provide transparent risk scores to investigators via a GraphQL dashboard.
Graph
Neo4j + Cypher
API
GraphQL + FastAPI
ML
XGBoost + SHAP
Behind the build
Graph Data Engineering
Designed a Neo4j graph schema to model Users, Accounts, and Transactions, ingestion streams from Kafka to surface 2nd and 3rd-degree connections in real-time.
Explainable ML
Trained XGBoost models on graph-engineered features (PageRank, Community ID) and integrated SHAP values to explain valid reasons behind every fraud flag.
Investigator Interface
Built a Next.js + D3.js dashboard consuming a Strawberry GraphQL API, creating an interactive visual exploration tool for compliance teams.