Highlights
Machine Learning-Driven Detection
An initial model was trained and validated on known fraud data, achieving an F-Score of 0.84, a strong result by industry standards.
Anomaly Detection Framework
To catch unknown fraud cases, an anomaly detection approach was applied, identifying 8% of transactions requiring further investigation, leading to more comprehensive fraud detection.
Time Series Model (TSM)
A predictive model was developed using historical transaction data to anticipate the characteristics of future transactions. If a transaction deviates significantly from the predicted pattern, it is flagged as suspicious and flagged for review. This also facilitated dynamic transaction limit adjustments.
Results
- Improved Fraud Detection Accuracy: The new system significantly reduced false positives by analyzing both known and unknown fraud cases.
- Complete Data Privacy: The on-premises system ensures sensitive transaction data never leaves the client’s infrastructure, addressing full compliance with security and privacy regulations.
- Dynamic Fraud Monitoring: The Time Series Model introduced the ability to dynamically adjust transaction limits and predict unusual patterns, enabling proactive fraud detection.
