Azure high-performance computing (HPC) for financial services
Confidently meet regulatory requirements with an elastic and intelligent infrastructure for risk modeling. Extend your capacity as needed and only pay for what you use.
Deploy and manage grids for risk analysis and reporting flexibly and highly securely while optimizing total cost of ownership (TCO).
Help global teams to collaborate more effectively and make decisions faster by providing high-quality data in real time.
Financial services HPC scenarios
Rapidly execute defined models and ad hoc experiments using common tools such as R and Python. Easily integrate data from different sources to consolidate risk reporting and boost capacity management for your risk calculations with vast Azure compute resources.
Efficiently manage delivery of regulatory reports by running high-performance risk simulations to aggregate and normalize siloed data from individual trading desks. Conduct fundamental review of the trading book (FRTB) impact assessments to inform early stage strategic decisions and manage ongoing FRTB compliance with Numerix FRTB, a high-performance cloud solution built on Azure.
Optimize your portfolio while meeting buyers’ preferences and constraints in an ever-changing regulatory environment. Spin up Azure HPC clusters with large memory footprints for your large optimization problems in a matter of seconds.
Confidently model prices for securities and other financial assets using numerical methods of your choice. Get the infrastructure you need to reliably run compute-intensive workloads, such as complex pricing models, perfectly parallel Monte Carlo simulations, and stochastic differential equations.
Powerful infrastructure as a service (IaaS) for financial services
- Speed up risk simulations with affordable, GPU-optimized N-series Azure Virtual Machines (VMs).
- Offload daily risk calculations to the cloud with Azure CycleCloud, which supports schedulers like HPC Pack, Tibco GridServer, and IBM Spectrum Symphony.
- Use H-series VMs for tightly-coupled MPI-based simulations.
- Visualize your results using NV-series VMs and hold results for later re-analysis with Azure Archive Storage.
- Process jobs on demand rather than on a pre-defined schedule and configure, manage, and monitor your jobs with Azure Batch APIs, tools, and command-line scripts.
Creating a hybrid risk analysis solution with Azure HPC
Hybrid risk analysis architecture
This templated risk analysis solution uses Azure HPC compute and GPU virtual machines (VMs) to expand on-premises Tibco GridServer compute to Azure using Azure CycleCloud for auto-scaling integration. The job executes both on-premises and in the cloud by using Avere vFXT fast caching and native NFS access to market data available on-premises.
- 1 Operations team uses Azure CycleCloud to configure and launch risk analysis grid in Azure.
- 2 Azure CycleCloud orchestrates VM creation and software configuration for Tibco Gridserver brokers and HPCCA, in-memory data cache, and Avere vFXT cache.
- 3 Quant (or scheduled batch) submits a risk analysis template workflow to the on-premises Tibco GridServer director. Based on job policies and current on-premises use, the workflow is allowed to burst to Azure to expand on-premises grid capacity.
- 4 The Tibco HPCCA detects the change in queue depth for each Tibco broker and requests additional Tibco engine capacity using the Azure CycleCloud Auto-Scaling API. Azure CycleCloud then autostarts engine nodes in Virtual Machine Scale Sets using the Azure H-series, HB-series, and HC-series VMs to optimize cost and performance and NC-series VMs to provide GPU capacity as required.
- 5 As soon as engine VMs join the Azure Grid, the brokers begin executing tasks to the new nodes.
- 6 Risk jobs pull artifacts from on-premises and Azure Blob storage as needed from NFS mounted Avere vFXT and/or via the fast in-memory cache.
- 7 As each task completes, results are returned to the submitter or driver and data is written back to the in-memory cache, or to NFS storage through the Avere vFXT, as required. Cached data is persisted either on-premises or in Azure Blob storage.
- 8 As task queues drain, the Tibco HPCCA uses the Azure CycleCloud Auto-Scaling API to shrink the compute grid and reduce cost.
See how financial services industry customers are innovating with Azure HPC
Brian Cartwright, Assistant Vice President, MetLife
"In more than 10 years of working in large-scale data analytics, I have seen over and over how bottlenecks can be addressed…. With the Microsoft technologies and cloud infrastructure supporting our environment, we can make our processes smoother, faster, and more sustainable."
Milliman takes a holistic, forward-looking approach to the automation and governance of actuarial modeling and reporting with a revolutionary cloud-based system for the life insurance market built on Azure.
Andy Lingard, Global Leader of General Insurance Software Development, Willis Towers Watson
"There has been a major evolution over the past few years in how we support big computing at Towers Watson, and Microsoft plays a growing role in that support."
Robin Johnson, CIO, Munich Re
"Azure gives us the ability to improve the analysis of the risks of change resulting from climate change to a new level."
Robert Griffiths, Director, MUFG
"We would need to buy enough servers to cope with the need for 4,000 cores on a weekend—that's roughly 350 servers and a lot of datacenter space. Instead of leasing a big new datacenter, we put several thousand cores on an ongoing basis in Azure, which gives us the ability to scale almost instantaneously in case of a market event."
Mikhail Dron, Managing Director and Vice President, TD Securities
"Being in the public cloud allows us to expand and contract as needed as well as change the configuration of the infrastructure almost on demand. That allows us to iterate very quickly through different solutions and find the right fit for the business problem, or for the client, almost at no notice and respond to the client's demands on the spot."