In-Memory Computing for Financial Services

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

Algorithmic Trading

EOD & Intraday Risk Assessment

Interest Rate Derivatives (IRD)

Market Data Applications

Financial Exchange

Resources

This white paper will discuss the challenges facing today’s insurance industry, the opportunities new technologies can offer, and the crucial edge that providers can gain with solutions such as the GridGain in-memory computing platform.
This white paper will give you a better understanding of how in-memory computing forms the backbone of successful high performance, highly scalable and mission-critical technology solutions in the FinTech industry. You will also learn how in-memory computing helps address many current limitations of legacy financial systems.

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.

This paper looks at the current state of high-frequency trading – why it’s popular and what types of strategies and technologies are being used – and then explores how in-memory computing can meet the technological challenges and increase profits within this market segment.
Read this white paper and learn how financial services companies are using in-memory computing to address the technical challenges caused by new and recent financial regulatory initiatives
As a large financial technology company, Misys solutions must manage 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 Java-based IT stack that will support the use of data lakes instead of traditional databases.
This eBook, Part 3 in the In-Memory Computing for Financial Services eBook Series, discusses how financial service firms are using in-memory computing platforms such as GridGain and Apache® Ignite™ in their strategy to improve the performance of asset and wealth management, spread betting and banking applications.

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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