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

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.

Leading banks, asset management firms and fintech companies rely on the GridGain in-memory computing platform as their new foundation for real-time risk analytics, portfolio management and regulatory compliance. With GridGain, these companies have brought together many different types of information to achieve a common, real-time view of risk. They have supported the needs of trading, settlement, accounting, customer portfolio management, risk management, internal and regulatory compliance. They have achieved all of this on a common platform with in-memory speed, unlimited horizontal scalability and broad integration to support any future needs. Download this Industry Brief to learn how.
Omnichannel banking requires more than a consistent API strategy across channels. It requires a single, real-time view of the customer that can be seamlessly shared across channels; infrastructure that can handle 10x or greater loads created by the increased interactions on digital channels; the ability to proactively personalize, promote and improve each customer’s experience; and use of real-time analytics and automation with transactions and interactions to help improve the experience. Download this Industry Brief now and learn how the GridGain In-Memory Computing Platform is addressing these challenges and more.
Being a fast follower or late adopter may have been good enough in the past. But now, to maximize their chances of surviving and thriving, insurers must not only fulfill the latest regulatory requirements. They must also be the first to innovate in three areas: Customer and Risk Analytics, Customer Experience Management, and Digital Business. Download the Industry Brief, "Becoming a Customer-Centric, Digital-First Insurer with In-Memory Computing,” and learn how the GridGain In-Memory Computing Platform can help you achieve this.
Leading banks and fintech companies have already adopted the GridGain in-memory computing platform as the foundation for FRTB and their next generation trading systems. With GridGain, these banks have been able to rapidly implement the required XVA calculations, continuously run their new risk models and price new securities in near real-time. Learn more now.
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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