A tier-one bank needed a unified machine learning platform to detect financial crime across regions. Multiple teams were running bespoke stacks, model training was slow, and infrastructure drift quietly inflated costs. cloudlogic.dev led the modernization of a GCP-based platform that kept regulators confident while giving data scientists a faster path from idea to production.
Where we started
The finance crime organization operated several disconnected pipelines—each with its own tooling, data sources, and governance gaps. That fragmentation made it difficult to collaborate, slowed regulatory reporting, and undermined trust in ML models.
- Model training runs regularly breached SLAs, delaying new risk signals.
- Infrastructure drift and manual provisioning added ~20% wasted spend.
- Teams in EMEA, APAC, and the Americas used different deployment patterns, complicating compliance.
- Leadership needed a roadmap for scaling TensorFlow/TFX workloads on GCP without guesswork.
What we did
- Unified the ML platform blueprint: Standardized on TensorFlow, TFX, Kubeflow Pipelines, and GKE, backed by Dataflow and BigQuery for feature engineering and scoring.
- Optimized training performance: Delivered targeted upgrades to pipeline components, storage tiers, and GPU/TPU utilization, raising training throughput by 25%.
- Eliminated infrastructure drift: Applied Terraform-based guardrails and automated audits that reclaimed ~20% of unnecessary infrastructure spend.
- Enabled global teams: Ran hands-on workshops and pair-programming sessions to upskill engineers and data scientists on GCP best practices.
- Mapped the future roadmap: Built proofs of concept, worked with Google to beta test new features, and documented the gaps that shaped the platform backlog.
Impact
- 25% faster model training cycles, allowing crime detection models to ship improvements quarterly instead of semi-annually.
- 20% infrastructure cost reduction through standardized IaC and continuous drift detection.
- Consistent pipelines and governance across global teams, improving audit readiness and collaboration.
- Data scientists empowered with self-service tools and documented patterns, reducing reliance on central engineering for day-to-day experimentation.
How the bank works today
The finance crime platform now runs on production-ready templates for ingestion, training, deployment, and monitoring. Global teams collaborate on shared pipelines, while Terraform policies keep infrastructure aligned with budget and compliance expectations. cloudlogic.dev continues to partner with the bank to evaluate new ML capabilities and expand real-time detection use cases across the enterprise.