Near real-time fraud detection
Proactive fraud prevention
Scalable and robust GCP solution
Continuous monitoring and insights
The challenge
Preventing ATO fraud: The need for real-time detection
A top 20 U.S. bank faced severe financial losses due to ATO fraud. Without a predictive system, the fraud team could only respond after losses occurred, impacting customer satisfaction and trust.
Key challenges
Reactive detection failed, allowing losses to grow and customer trust to decline
Without prediction, fraud went unchecked—real-time prevention was critical
The solution
Insight-driven analysis and predictive modelling
Data driven fraud prediction
Uncovered patterns in non-monetary data
Defined model targets through data discovery
Generated risk scores using a predictive engine
Machine learning
Analyzed login patterns, device and IP usage
Strengthened detection and mitigation
Enabled proactive fraud prevention
Implementation approach
1
Cloud integration
Scalable fraud detection
Ensured high availability and real-time processing
Integrated batch and streaming data
2
AI model development
Trained ML algorithms to detect fraud
Optimized for accuracy and efficiency
Built a predictive engine for ATO risk scoring
3
Fraud team enablement
API-based fraud alerts
Customer risk insights
Looker dashboard
The impact
Future-ready, Real-time fraud detection
Near real-time predictions
Detected ATO fraud every 5 minutes using non-monetary data
Future ready blueprint
Set a foundation for GCP-based MLOps and future projects
Empowered with documentation
Delivered risk management, data lineage and codebase for seamless maintenance
Looking ahead
Enhanced fraud mitigation
Enables targeted interventions to minimize financial and reputational risks
Proactive risk management
Transitions from post-loss investigation to real-time fraud detection
Operational efficiency
Drives significant cost savings through early fraud identification
