Leveraging in-memory computing is imperative for financial services organizations like banks and investment management firms. GridGain® Systems is a leader in in-memory computing solutions for the financial services industry. The GridGain in-memory computing platform enables unprecedented speed and scalability for transactional, analytical and hybrid transactional/analytical processing (HTAP) financial services use cases across any data store.
GridGain is proven technology which delivers in-memory speed with massive scalability to petabytes of in-memory data. GridGain solutions provide predictable latency, flexible scaling, configurable data consistency and reliable uptime. The GridGain in-memory computing platform can run on commodity hardware installed on premise, in the Cloud, or in a hybrid environment. It is built on the open source Apache® Ignite™ project code base, offering all the benefits of open source software with the backing of a reliable, commercial company behind the technology.
Financial Services Clients
A leading global bank based in Belgium. View their keynote from the In-Memory Computing Summit on capital market use cases for in-memory computing.
The largest bank in Eastern Europe with over 130 million clients. Read about their needs in this CIO.com article.
A leading global bank based in France which ranks among the top 20 banks worldwide based on total assets.
Financial Services Use Cases for GridGain Technology
High Speed Transactions
EOD & Intraday Risk Assessment
Interest Rate Derivatives (IRD)
Market Data Applications
Bitcoin and blockchain, the digital-ledger technology behind this electronic currency, are generating enormous amounts of interest in the financial services industry. Most of the larger banks are investigating this area, and many technology companies are building platforms to enable blockchain technology for financial services firms.
Spread betting offers some compelling advantages, including low entry and transaction costs, preferential tax treatment, and a diverse array of products and options. Traders can bet on any type of event for which there is a measurable outcome that might go in either of two directions – for example, housing prices, the value of a stock-market index, or the difference in the scores of two teams in a sporting event.
Financial fraud detection and prevention is not a simple task, and firms must tackle it simultaneously with other crucial tasks such as ensuring regulatory compliance. To accomplish these data-intensive tasks in a timely manner, financial firms need solutions that are flexible, scalable, reliable, and fast enough to analyze extremely large datasets in real-time.
FinTech companies are faced by many of the same challenges as their largest customers with speed, scale and innovation. Their new channels and services, as well as core banking, insurance and real estate systems must deliver 100-1000x speed and scale compared to existing systems. They must adopt new technologies from streaming analytics to machine and deep learning to implement real-time analytics and decision automation. The latest regulations also require 100-1000x more computations than before.
With the tight regulatory environment, competition from traditional and non-traditional industries, customer demands, and cost pressures that companies are facing today, e-commerce initiatives require big data technologies that make processes and transactions much faster and more efficient. Large companies accumulating massive amounts of data need to be able to perform analytics on that data in real time in a cost-conscious manner to ensure a good user experience.
With the tight regulatory environment and cost pressures that financial services companies are facing today, they need big data technologies that make their risk management, monitoring, and compliance processes much faster and more efficient. Large financial institutions accumulating massive amounts of data need to be able to perform analytics on that data in real time in a cost-conscious manner to ensure a good user experience.
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