Published by Gbaf News
Posted on May 31, 2018
11 min readLast updated: January 21, 2026
By Terry Erisman, Vice President of Marketing, GridGain Systems
Multiple trends in the banking industry are making a high-performance technology infrastructure critical to ensure success. Terry Erisman, Vice President of Marketing at GridGain Systems, looks at the key challenges facing financial institutions as they become increasingly reliant on in-memory computing technology to stay competitive and agile.
Real-time financial regulatory compliance and fraud prevention
Fears of another 2008-style financial meltdown and concerns over privacy, including the implementation of the EU’s General Data Protection Regulation (GDPR), have created an evolving and increasingly complex regulatory environment. To ensure compliance, financial institutions must now monitor, collect, and analyse vast amounts of data from multiple, disparate sources in real-time to calculate and report on their regulatory compliance status.
Similarly, fraud prevention requires the real-time analysis of incoming data and, frequently, the ability to apply machine learning to identify unusual activity. Fraud strategies appear in every area of financial services, from the theft of credit card numbers and personal financial information, to document forging and mortgage manipulation, to fraudulent computerized banking and securities trading. Financial services firms can suffer financial loss and damage to their reputations from such activities. According to Juniper Research, the problem is getting worse as online transaction fraud alone is expected to climb from $10.7 billion in 2015 to $25.6 billion in 2020.
The evolution of asset and wealth management
Asset and wealth management services are also changing dramatically. Today’s investors typically eschew the advice of a single investment advisor and reject the idea of a limited trading day. Instead, they want their investment services providers to offer 24/7 access to trading services and an ever-widening array of real-time data that they can explore themselves. This is requiring financial firms to update their technology capabilities and performance in order to provide real-time, high performance 24/7 access through a variety of new distribution channels, including online, mobile, and social. As more customer interaction channels have opened, transaction volumes have increased and providing real-time services has become more challenging.
Support for blockchain
Banks, enterprises and consumers continue to adopt blockchain, bitcoin and other digital-ledger technologies. A 2016 Deutsche Bank survey of 200 participants in the global financial industry found that 87 percent expect blockchain to have a major impact on the securities services market, while 75 percent expect widespread adoption over the next three to six years.
Supporting blockchain’s decentralised architecture typically requires a high-performance infrastructure. When a bitcoin transaction occurs, a record of the transaction is added to the ever-growing blockchain, which must be updated in real-time. Depending on the specific application, as the size of the blockchain and the number of subscribers continue to grow, the demands on the infrastructure to maintain the real-time ledger can become extraordinary.
Spread betting
Spread betting is increasingly popular, especially in the UK. A spread betting platform which can accurately price wagers while providing users with the required services and access to information from multiple channels can place tremendous demands on computing infrastructure. For instance, spread betting bookmakers and host venues must be able to stream tremendous amounts of data into advanced statistical and mathematical models. They must then quickly compute event relationships and outcome probabilities in order to set prices in real-time. To improve the trader experience, spread betting platforms may also need to provide customers with access to real-time data services and news analysis so traders can gain insight into trends that impact their positions.
The role of in-memory computing
While financial services companies are under extraordinary pressure to meet evolving customer demands, prevent fraud, and comply with increasingly complex regulations, a variety of technologies are already in use to address these challenges. However, many such systems are limited by the performance of disk-based databases that are slowed by constant disk reads and writes. Overcoming these limitations today is not only possible, but also potentially less complex and less expensive than one might guess.
Upgrading IT infrastructure to enable real-time processing and massive scalability can be a highly advantageous solution. By maintaining data in memory for rapid processing across a distributed computing cluster, an in-memory computing platform can process huge volumes of data in real-time. Such a platform can scale simply by adding additional nodes to the cluster, enabling firms to keep pace with the rapidly evolving environment and increasing threats. In fact, inserting an in-memory computing layer between existing application and data layers can increase processing speed by 1,000 times versus disk-based databases and support millions of transactions per second, all with minimal integration requirements. Such in-memory computing platforms can include an in-memory data grid, an in-memory database, streaming analytics and a continuous learning framework for maximum flexibility to address a wide variety of computing challenges. On-premises, cloud, and hybrid deployments are all possible.
For example, Wellington Management is an investment firm with more than US$1 trillion in client assets under management. They created their investment book of record (IBOR)on an in-memory computing platform as their single source of truth for investor positions, exposure, valuations and performance. All real-time trading transactions, all related account activity, third-party data such as market quotes, and all related back-office activity flow through the IBOR in real time. The IBOR also supports performance analysis, risk assessments, regulatory compliance and more. To handle the extreme processing challenge of the IBORwhile also controlling costs, Wellington deployed their in-memory computing platform as a hybrid transactional/analytical processing (HTAP) system. The HTAP configuration enables real-time analysis on the live data, offers massive horizontal scalability, and supports ANSI-99 SQL. In various tests, the new platform performed at least 10 times faster than the company’s legacy Oracle database.
While the cost of memory is still slightly higher than disk-based storage, an in-memory computing solution offers a tremendous increase in performance and much greater flexibility to incorporate new capabilities in the future.
The benefit? A far superior return on investment (ROI), especially when competitive advantage and customer experience is taken into account.
Terry Erisman serves as the Vice President of Marketing for GridGain Systems. An industry veteran with more than 25 years of experience, Erisman has initiated and driven high revenue growth for a multitude of award-winning companies in the SaaS, open source, and enterprise software sectors.
By Terry Erisman, Vice President of Marketing, GridGain Systems
Multiple trends in the banking industry are making a high-performance technology infrastructure critical to ensure success. Terry Erisman, Vice President of Marketing at GridGain Systems, looks at the key challenges facing financial institutions as they become increasingly reliant on in-memory computing technology to stay competitive and agile.
Real-time financial regulatory compliance and fraud prevention
Fears of another 2008-style financial meltdown and concerns over privacy, including the implementation of the EU’s General Data Protection Regulation (GDPR), have created an evolving and increasingly complex regulatory environment. To ensure compliance, financial institutions must now monitor, collect, and analyse vast amounts of data from multiple, disparate sources in real-time to calculate and report on their regulatory compliance status.
Similarly, fraud prevention requires the real-time analysis of incoming data and, frequently, the ability to apply machine learning to identify unusual activity. Fraud strategies appear in every area of financial services, from the theft of credit card numbers and personal financial information, to document forging and mortgage manipulation, to fraudulent computerized banking and securities trading. Financial services firms can suffer financial loss and damage to their reputations from such activities. According to Juniper Research, the problem is getting worse as online transaction fraud alone is expected to climb from $10.7 billion in 2015 to $25.6 billion in 2020.
The evolution of asset and wealth management
Asset and wealth management services are also changing dramatically. Today’s investors typically eschew the advice of a single investment advisor and reject the idea of a limited trading day. Instead, they want their investment services providers to offer 24/7 access to trading services and an ever-widening array of real-time data that they can explore themselves. This is requiring financial firms to update their technology capabilities and performance in order to provide real-time, high performance 24/7 access through a variety of new distribution channels, including online, mobile, and social. As more customer interaction channels have opened, transaction volumes have increased and providing real-time services has become more challenging.
Support for blockchain
Banks, enterprises and consumers continue to adopt blockchain, bitcoin and other digital-ledger technologies. A 2016 Deutsche Bank survey of 200 participants in the global financial industry found that 87 percent expect blockchain to have a major impact on the securities services market, while 75 percent expect widespread adoption over the next three to six years.
Supporting blockchain’s decentralised architecture typically requires a high-performance infrastructure. When a bitcoin transaction occurs, a record of the transaction is added to the ever-growing blockchain, which must be updated in real-time. Depending on the specific application, as the size of the blockchain and the number of subscribers continue to grow, the demands on the infrastructure to maintain the real-time ledger can become extraordinary.
Spread betting
Spread betting is increasingly popular, especially in the UK. A spread betting platform which can accurately price wagers while providing users with the required services and access to information from multiple channels can place tremendous demands on computing infrastructure. For instance, spread betting bookmakers and host venues must be able to stream tremendous amounts of data into advanced statistical and mathematical models. They must then quickly compute event relationships and outcome probabilities in order to set prices in real-time. To improve the trader experience, spread betting platforms may also need to provide customers with access to real-time data services and news analysis so traders can gain insight into trends that impact their positions.
The role of in-memory computing
While financial services companies are under extraordinary pressure to meet evolving customer demands, prevent fraud, and comply with increasingly complex regulations, a variety of technologies are already in use to address these challenges. However, many such systems are limited by the performance of disk-based databases that are slowed by constant disk reads and writes. Overcoming these limitations today is not only possible, but also potentially less complex and less expensive than one might guess.
Upgrading IT infrastructure to enable real-time processing and massive scalability can be a highly advantageous solution. By maintaining data in memory for rapid processing across a distributed computing cluster, an in-memory computing platform can process huge volumes of data in real-time. Such a platform can scale simply by adding additional nodes to the cluster, enabling firms to keep pace with the rapidly evolving environment and increasing threats. In fact, inserting an in-memory computing layer between existing application and data layers can increase processing speed by 1,000 times versus disk-based databases and support millions of transactions per second, all with minimal integration requirements. Such in-memory computing platforms can include an in-memory data grid, an in-memory database, streaming analytics and a continuous learning framework for maximum flexibility to address a wide variety of computing challenges. On-premises, cloud, and hybrid deployments are all possible.
For example, Wellington Management is an investment firm with more than US$1 trillion in client assets under management. They created their investment book of record (IBOR)on an in-memory computing platform as their single source of truth for investor positions, exposure, valuations and performance. All real-time trading transactions, all related account activity, third-party data such as market quotes, and all related back-office activity flow through the IBOR in real time. The IBOR also supports performance analysis, risk assessments, regulatory compliance and more. To handle the extreme processing challenge of the IBORwhile also controlling costs, Wellington deployed their in-memory computing platform as a hybrid transactional/analytical processing (HTAP) system. The HTAP configuration enables real-time analysis on the live data, offers massive horizontal scalability, and supports ANSI-99 SQL. In various tests, the new platform performed at least 10 times faster than the company’s legacy Oracle database.
While the cost of memory is still slightly higher than disk-based storage, an in-memory computing solution offers a tremendous increase in performance and much greater flexibility to incorporate new capabilities in the future.
The benefit? A far superior return on investment (ROI), especially when competitive advantage and customer experience is taken into account.
Terry Erisman serves as the Vice President of Marketing for GridGain Systems. An industry veteran with more than 25 years of experience, Erisman has initiated and driven high revenue growth for a multitude of award-winning companies in the SaaS, open source, and enterprise software sectors.
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