Scaling Risk Analytics on Google Cloud

  • 14/5/2017
  • One-minute read

Quantitative risk workloads demand bursty compute, broad datasets, and strict security controls. The presentation below explains how Google Cloud Platform (GCP) supports those requirements with managed data services and autoscaling infrastructure.

Key Takeaways

  • Use pre-emptible and autoscaling Compute Engine instances to reduce cost during Monte Carlo simulations.
  • Combine BigQuery for interactive analytics with Cloud Storage for raw market data retention.
  • Integrate Cloud IAM and VPC Service Controls to enforce least privilege across risk, treasury, and compliance teams.

Suggested Next Steps

  • Pilot workloads with synthetic data to estimate runtime and budget.
  • Benchmark data transfer and egress fees; large risk datasets can be costly to export off-platform.
  • Review regulatory obligations (e.g., PRA, SEC, GDPR) before migrating sensitive risk data to the cloud.
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