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Financial Risk Modelling: Cloud Performance & Scale [Case Study]

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Financial risk modelling is a much sought-after function for many businesses across the financial services sector. In fact, by 2022 the predictive analytics market is projected to be worth $10.95 billion (just over £9.5 billion).

With the right technology and algorithms, these models can help to predict customer outcomes, streamline processes, and enable better, more risk-aware business decisions.

However, certain legacy predictive modelling applications can be costly, unresponsive and inflexible. This was the case for one of our clients, a leading insurer, who felt their previous on-premise estate was reaching its maximum capacity.

By re-platforming their architecture in Azure, our team was able to cut their TCO, increase efficiency and enable elasticity on-demand.

Moving away from Restrictive Financial Risk Modelling

Our client first came to us with the need to improve the performance and resilience of their risk modelling efforts.  As it stood, the firm was being held back by the cost, timescale and inefficiencies of their on-premise environment.

More specifically, they suffered from:

  • Legacy Linux operating systems running outdated and unsupported versions
  • Inflexible vendor contracts
  • A weak business continuity and disaster recovery position

Because of these issues, the firm reached out to us to explore the option of moving to Azure and finding a better home for their predictive modelling workloads.

Finding a new Home for Risk Modelling in Microsoft Azure

When assessing the firm’s difficulties on-premises and their growing needs as a business, we decided that the best solution was to re-house their existing predictive data modelling architecture in the Microsoft Cloud.

Working with their internal IT team, we completed foundational engineering in Azure to provide the firm with a fully functioning hybrid cloud environment.

From there, we re-architected the complex algorithmic risk modelling application to run in the Azure Hybrid Data Centre, providing them with full access to Azure’s on-demand compute and elastic scaling.

To benchmark performance, we ran the revamped application against their previous environment and discovered that Azure outperformed it in every way.

Unlocking the Value of Azure’s Flexible Infrastructure

When moving to Azure, the organisation saw immediate benefits:

  • Their total cost of ownership was significantly lowered, and their key process run times reduced from 18 hours to under three.

What’s more, deployment rates increased dramatically:

  • The firm now has the ability to deploy an additional 1000+ compute cores within 11 minutes
  • They can create an entire disaster recovery environment (DR) build in 30 minutes instead of the weeks and days it took beforehand.

As a direct result of our engagement, the firm is now considering moving other important business workloads into the cloud to help streamline their processes and reduce costs.

Key Takeaways: what can Cloud Financial Modelling do for your Business?

For financial organisations who are looking to improve their predictive modelling performance on Azure, we suggest the following steps:

  1. Assess your current infrastructure and predictive modelling workloads
  2. Investigate the benefits of an Azure, hybrid cloud approach
  3. Migrate your workloads with a trusted partner

1. Assess your Current Infrastructure

Much like our client, it’s important to first look at your current expenditure and the limitations of your existing infrastructure.

If you’re unable to scale your predictive modelling efforts, due to costs or time-constraints, moving workloads to Azure and making the most of flexible PaaS capabilities can increase business agility.   

2. Investigate the Benefits of Azure

Before moving your business processes to Azure, investigate the benefits of a hybrid cloud approach and build a business case around specific outcomes. A clear migration roadmap and objective will make it easier to optimise costs and create a sustainable future in the cloud.

3. Trust the Experts

Re-architecting applications, such as risk and solvency modelling applications, takes more than a simple ‘lift and shift’. To get the most out of the cloud, it’s important you seek help from those in the know. An experienced cloud partner will work with your team to understand the specific requirements of your move and help you manage the platform once the migration is complete.

To find out more about our approach to risk management and application migrations, visit our predictive modelling page.

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