In-Memory Computing for FinTech

In-memory computing can add dramatic speed and scale to financial technology applications. The GridGain® in-memory computing platform is used in leading fintech applications which are installed broadly throughout the financial services industry. For example, the GridGain solution is included in solutions used by 48 of the top 50 banks worldwide.

The GridGain in-memory computing platform enables unprecedented speed and scale for all transactional, analytical and hybrid transactional/analytical processing (HTAP) applications across any data store. Our solutions deliver predictable latency, flexible scaling, configurable data consistency and reliable uptime. Ultra low latency use cases can be addressed using high performance Java Virtual Machines (JVMs). GridGain, built on the open source Apache Ignite® project code base can run on commodity hardware, virtual machines or cloud providers, on-premises, in public or private clouds, or on hybrid environments.

FinTech Use Cases for the GridGain In-Memory Computing Platform

The FinTech market grew from $1.8 billion in 2010 to more than $19 billion in 2015. According to a 2016 Accenture report, venture capitalists, private equity firms, and other players have invested $50 billion in almost 2,500 FinTech companies since 2010. There are many reasons why.

Driven by all that VC investment and an accompanying startup mentality, FinTech development is happening internationally, and the applications are easily reaching a worldwide market of global banks. Many barriers to entry have been removed as developers take advantage of lower-cost technologies and open source software to innovate and then implement and iterate their ideas quickly.

Banks are rapidly adopting cloud-based applications and outsourcing some of their data processing and analysis to FinTech companies that can do it faster, better, and within very specific regulatory strictures that they specialize in addressing. By offloading a significant percentage of their traditional back-end work, banks can focus on their core specialties and improve their customer service.

FinTech Clients

Finastra is used by 48 of the world's 50 largest banks. Read about their solutions in our Finastra (Misys) case study

Markit - A leading worldwide provider of fintech solutions for applications including risk analysis and enterprise document management for financial services firms.

The Glue is a fintech startup that is creating a platform for financial institutions to quickly develop innovative financial services.

FinTech Use Cases for GridGain Technology

Analytics Risk Engines

Front-End Systems Integration

Treasury, Risk and Management Solutions

Enterprise Data Management

Automated Investment and Trading

Peer-to-Peer Lending




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.
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
Finastra deployed the GridGain in-memory computing platform to embrace real-time services and satisfy evolving compliance, reporting regulations, and customer demands in Europe. As a large financial technology company, Finastra solutions must manage huge amounts of trade and accounting data. Finastra used GridGain to help to implement a new Java-based IT stack that supports data lakes instead of traditional databases.
If you are new to in-memory computing, curious to learn how in-memory computing can be used for financial applications, or seeking to educate a non-technical team member about the benefits of in-memory computing for financial applications, this eBook can help.