Problem:
Financial institutions face growing challenges in detecting sophisticated fraud patterns without disrupting legitimate transactions. These fraudulent activities include Identity theft, Phishing, Credit card fraud, Account takeovers, Money laundering, wire fraud, check fraud, ACH fraud, investment fraud, mortgage fraud, payment fraud, advance fee fraud, insurance fraud, chargeback fraud,
Financial fraud can impact business and financial institutions causing significant financial losses, reputation damage, legal repercussions, decline in trust and other damage depending on the severity of the issue
Proposed Solution:
Drawing from our experience we propose a two-stage fraud detection system:
Stage 1: An Isolation Forest model rapidly flags suspicious transactions based on anomalous behavior.
Stage 2: A Graph Neural Network (GNN) analyzes complex transaction networks to uncover coordinated fraud rings.
Implementation Process:
- Data Engineering: Use Cloud Dataflow to preprocess transaction data.
- Model Training: Develop the Isolation Forest model with anomaly scores feeding into the GNN.
- Deployment: Serve both models on Vertex AI Prediction for low-latency inference.
- Monitoring: Implement Vertex AI Model Monitoring to track model drift and precision.
Anticipated Impact:
✅ 20% reduction in false positives.
✅ Improved detection of previously undetected fraud patterns.
✅ Enhanced system scalability using GKE and API Gateway to support peak transaction loads.
Financial fraud can impact business and financial institutions causing significant financial losses, reputation damage, legal repercussions, decline in trust and other damage depending on the severity of the issue