Risk Management

Risk management in financial services is an extremely data-intensive discipline which typically requires expensive, high performance systems to process all the calculations on the huge volumes of financial data. Fortunately, new technology approaches to risk management are making these systems more manageable and cost-effective. Distributed, cloud-native data platforms running in either private or public clouds help risk and compliance officers get their jobs done on time and avoid the risks and regulatory penalties that financial services firms continually face.

Risk management professionals often seek a combination of the following business requirements:

• Fast, reliable systems that can be built and maintained within tight budget constraints
• Ability to pay for computing resources on a pay-as-you-consume basis to reduce costs in risk management processes
• Systems that can deliver data and reports, including outputs of large-scale Monte Carlo simulations, within stringent service-level agreements (SLAs)
• High performance and low latency without requiring a huge investment in hardware
• Providing real-time views of the risk exposure to allow risk management teams to get an immediate jump on emerging market risks
• Ability to add new capabilities to IT systems without adding additional operational risk (i.e., no/minimal ripping and replacing of existing technologies)

When seeking to address risk management business requirements, IT teams often face a familiar set of technical challenges:

• Complex architectures that have relied on legacy systems over the years, making them hard to extend and accelerate
• Monolithic architectures that are difficult to scale in a cost-effective way
• Difficulty in migrating to cloud-native environments, which can improve the economics of running highly varying workloads
• Efficiently handling growing workloads that can run within the business- or regulation-mandated SLAs
• High costs of maintaining legacy systems

A global investment bank needed to perform large-scale risk exposure calculations for capital markets using their latest exposure ("LEX") deployment. These calculations had to be conducted after the market closed, within a limited timeframe.

This daily reporting was critical due to federal government requirements. However, as transaction volumes increased, their existing system exceeded mandated SLAs, risking delayed reporting to auditors and regulators, resulting in penalties and higher liquidity requirements.

By replacing their existing legacy system with Hazelcast Platform as the core processing engine, they added a modern technology that enabled the high performance computing environment with which they were already familiar. With fast, in-memory data accesses and distributed stream processing capabilities, the bank was able to run highly parallelized calculations that let them complete the analysis job well within the SLA.

And with the cloud-native scalability of Hazelcast Platform, they know that as their transaction volumes grow, they can simply add more nodes in an incremental manner to scale up to the required compute power to continue meeting the SLAs.