Fraud Moves Fast—AI Moves Faster

Legacy rules engines generate too many false alerts and miss new fraud patterns. Modern ML models stream transactions in real time, learn evolving behavior, and help investigators focus on high-risk events.

Core AI Fraud Capabilities

• Anomaly detection using graph neural networks and embeddings
• Behavioral biometrics to profile legitimate users
• Device fingerprinting and IP reputation scoring
• Explainable AI dashboards for regulators
• Case management automation for investigators

Deployment Framework

TensorBlue's fraud detection accelerator delivers production models in 10 weeks:

1. Week 1-2: Data intake, schema mapping, and baseline metrics.
2. Week 3-5: Feature engineering, streaming pipelines, and model training.
3. Week 6-8: Real-time inference service with Kafka / Kinesis integration.
4. Week 9-10: Investigator console, alert triage workflow, and compliance reporting.

Measured Outcomes

• 53% reduction in chargeback losses
• 37% drop in false positives
• Under 200ms scoring latency at 2k TPS
• Regulator-ready audit trail and model governance