Business Situation
- The client was grappling with increased payment holds and sales declines, driven by a legacy set of complex strategies, without a simultaneous reduction in fraud.
- The client wanted to implement advanced machine learning (ML) to detect effective fraud, reduce false positives, mitigate sales declines for genuine customers, and establish comprehensive tracking mechanisms.
SGA Approach
Technology
- Integrated advanced data integration platforms to consolidate transaction records, user behavior logs, and external databases
AI
- Deployed semi-supervised learning models, combining supervised and unsupervised techniques
- Continuously trained and validated models using a mix of labeled and unlabeled data
- Implemented adaptive learning systems to update models based on new data and emerging fraud patterns
Data
- Collected extensive labeled and unlabeled data, including transaction histories, user profiles, and known fraud patterns
- Developed sophisticated features capturing nuances of fraudulent behavior based on transaction frequency, amount anomalies, and geolocation inconsistencies
- Implemented robust data privacy and security measures to comply with regulations like GDPR
Key Takeaways
- Integrating diverse data sources provided a comprehensive view of transactions and user behavior
- Implementing real-time processing systems facilitated immediate detection and response
- Ensuring extensive data collection, feature development, and compliance with privacy regulations enhanced effectiveness
- Semi-supervised learning and anomaly detection helped the client identify fraudulent patterns effectively
- Establishing a workflow for ongoing model training, validation, and refinement led to consistent results
- Updating models with new data allowed for continuous knowledge gain about emerging fraud patterns
- A notable reduction in fraudulent transactions and false positives was achieved
