2026/01/17
Reducing Risk Analytics Latency in Financial Services with Pooled GPU Resources
A financial services case study on accelerating fraud detection and risk scoring while cutting GPU costs by 38%.
Customer Profile
A mid-size financial institution running real-time fraud detection, credit scoring, and stress-testing models. The institution operated in a regulated environment with strict data residency and auditability requirements.
The Business Problem
Three production bottlenecks were hurting the business:
- Inference latency spikes during payment peaks (lunch hours, salary days).
- GPU resources locked by batch jobs, starving real-time pipelines.
- Cost opacity: business lines could not see GPU consumption by product.
Baseline metrics:
| Metric | Baseline |
|---|---|
| Risk scoring P95 latency | 380–450ms |
| GPU utilization | 28–35% |
| Fraud model retraining cycle | 14 days |
| GPU cost / month | 100% (baseline) |
TensorFusion Solution
TensorFusion delivered policy-driven GPU pooling and priority isolation:
