How do you manage investment risk for a billion data points?
Name of Organization: Misys
Location: London, UK
Business Opportunity or Challenge:
Misys, a financial services software provider, is charged with managing huge amounts of trade and accounting data. To meet evolving customer demands for real-time services and satisfy evolving compliance and reporting regulations in Europe, Misys opted to implement a new IT stack that will support the use of data lakes instead of traditional databases, and provide real-time views of transactions, accounts and risks.
Misys has more than 2,000 customers in 130 countries, and its software is designed to help its customer make smart business decisions and drive performance and growth.
The company provides its clients a bird’s eye view of financial risks across the trading and banking book, and the entire organization, including a ‘what-if?’ analysis on a billion data points designed to take less than a second; where global exposure is known by country, by counterparty, by trade; and where credit, market and liquidity risk can be seen in one place.
Investment risk management
Misys delivers a software platform that leverages clients’ existing investments in trading systems with a single view of their trading business and risk exposures, cross-asset and cross-business; and where it’s possible to price, trade, execute, analyze and add new components instantly. The company provides an integrated banking solution portfolio, enabling banks to embrace the needs of their customers –from branch to web to mobile and beyond.
How This Opportunity or Challenge Was Met:
To handle the caching of data from the data lake and distributing the cached data across a network cluster for massive parallel processing, Misys opted to deploy GridGain, a provider of in-memory technology. The GridGain In-Memory Data Fabric, based on Apache Ignite, enables high-performance transactions running faster than disk-based approaches. It provides high-speed transactions, real-time streaming, and fast analytics in a data access and processing layer which works with RDBMS, NoSQL or Hadoop databases.
In-memory technology is the processing of data and code within the random access memory of computers. Previously, data has stayed on disks, and needed to be located and brought over to RAM as it was called by applications. When RAM was filled to capacity, data not in use was sent back to disk as new data was brought in. This disk-to-RAM transfer process is accomplished in milliseconds, of course, but when multiplied by the hundreds of gigabytes it begins to add up to a great deal of latency.
Prior to implementing GridGain, Misys relied on batch processing of large amounts of transaction data, resulting in delays before the data was available for querying. The new solution enables synchronous real-time processing and computation with low latency by storing the data in memory and parallelizing processing across multiple machines in the cluster. A typical Misys cluster consists of commodity servers relying on Xeon processors with 256 GB of RAM.
The new service integrates Misys trading systems with cloud-based components to offer a business-wide, cross-silo approach to handling over-the-counter derivatives, exchange-traded derivatives, inflation, fixed income, FX/MM, hybrids, and structured products from trading through to accounting.
The implementation helps provide computational elasticity and real-time risk analysis with full valuation, the case study states. This helps provide an advantage when pricing complex strategies, boosting performance and speed with unlimited scalability. The platform uses in-memory computing to cope with big swings in volumes.
Benefits from This Initiative:
Not only is the in-memory solution speeding data delivery to customers, but it is also helping the company support clients’ regulatory mandates. In Misys’ primary market, Europe, regulatory requirements have been tightening, with increasing pressure on banks to improve data management and performance. For example, the Basel Committee on Banking Supervision’s fundamental review of the trading book capital standards requires banks to run far more compute-intensive calculations to determine the amount of capital they need. With the new solution, Misys can keep the document store of trade and market data in-memory and run the calculations in parallel to dramatically reduce processing time.
“We have achieved real-time processing of massive amounts of trade and transaction data, eliminating bottlenecks and enabling us to offer next-generation financial services to our customers,” according to Felix Grevy, director of product management for FusionFabric and cloud at Misys.