Challenge
Legacy fraud systems were too slow to catch modern threats.
Despite significant investments in fraud prevention, the bank’s existing rules-based system was unable to detect synthetic identities, real-time laundering, or AI-generated scams. Batch processing delayed fraud scoring by hours, resulting in high false positives and missed threats. Compliance teams were overwhelmed with manual investigations and opaque models that lacked explainability.
Solution
Legacy fraud systems were too slow to catch modern threats.
Altpai deployed a real-time, transformer-based fraud detection system — integrated directly with the bank’s payment gateway and KYC stack.
Key Capabilities Delivered:
- Transformer-based anomaly detection models trained on historical and synthetic fraud patterns
- Streaming inference pipelines integrated with payment gateways for real-time detection
- Analyst dashboards with model explainability, alert scoring, and action traceability
- Modular APIs compatible with legacy AML and KYC systems
- Audit-ready logging to support regulatory reviews and investigations
This system was built to align with the bank’s zero-trust architecture, with full traceability, rollback support, and real-time alert feedback loops.
The Results
- 67% reduction in undetected fraud incidents
- >40% faster analyst response time to flagged transactions
- 55% drop in false positives, improving operational efficiency
- Full alignment with internal compliance and external audit expectations
Why It Worked
Altpai didn’t just implement a fraud detection model — we engineered a scalable, explainable, and regulation-aligned AI system designed to operate inside legacy financial infrastructure. This allowed the Tier‑1 bank to move from reactive fraud response to proactive fraud prevention at scale.