Digital Transformation is taking place in every industry, and the extent and pace of change in financial services is truly breathtaking. “In-Memory Computing for Financial Services eBook, Part 1” published by GridGain Systems, explored how in-memory computing platforms such as Apache® Ignite™ and GridGain support rapidly evolving requirements for high-frequency trading, fraud prevention and real-time regulatory compliance.
“In-Memory Computing for Financial Services eBook, Part 2” explained how in-memory computing platforms help financial services firms take advantage of modern payments systems, the Internet of Things (IoT) and blockchain. “In-Memory Computing for Financial Services eBook, Part 3,” the recently published final eBook in the series, examines how in-memory computing is revolutionising asset and wealth management, helping spread betting platforms thrive, and enabling fintech providers to offer a wider range of real-time services to meet the increasing demand.
Transforming asset and wealth management
In the past, investors typically worked with large, institutional investment firms, relying on advisors during regular banking hours to help them choose from standard investment vehicles, such as equities and bonds.
Today’s younger, tech-savvy investors, however, have very different expectations. They want more personalised investment advice, including comparative recommendations and performance reports. They expect 24/7 access to investing via their mobile devices. And they’re looking a wider range of investment vehicles, including socially responsible investments, innovative startups and international opportunities, not to mention help dealing with the increased market volatility in a post-financial-crisis world.
Financial services firms must either address these new demands or face being marginalised. New investment service providers have emerged and started to satisfy these demands, increasing the pressure on existing firms to expand their services, reduce fees and increase efficiency. Specifically, they must figure out how to deliver real-time, 24/7 personalised services across each customer’s channels of choice, including mobile, all while dealing with increasing regulatory requirements.
Meeting these challenges requires implementing new technologies and platforms, such as big-data management and analytics, machine learning, content automation, scenario analysis, robo-advisors, virtual reality, complex event processing, blockchain and more – all of which require huge increases in computing performance. Sberbank, who forecast a 100x increase in transactions as they moved towards a 24/7 online and mobile world, adopted in-memory computing to both increase performance at a much lower cost, and to become much more agile so they could introduce new products in hours.
The spread betting market is growing globally, with more than one million people opening spread betting accounts in Great Britain alone. The benefits of spread betting include low entry and transaction costs, preferential tax treatment, and a virtually unlimited array of products and options.
But spread betting is also highly volatile and minimally regulated, which results in significant risks. To limit these risks, spread betting platform providers must use advanced mathematical models to analyse vast amounts of data from subscription services and current news, predict outcomes of events, and help devise optimal strategies, including hedging bets.
All these strategies require real-time and near real-time data analysis if they are to account for rapidly changing – sometimes by-the-second – conditions. The specific technologies used to achieve this include big data, complex event processing or streaming analytics, robots, online and cloud platforms, data partitioning, parallel processing clusters, mobile trading and more. As with modernising asset and wealth management, using these technologies to deliver a successful spread betting platform requires tremendous processing performance.
Banks are rapidly adopting cloud-based applications and outsourcing some of their data processing and analysis to fintech companies that can do it faster and better while satisfying very specific regulatory requirements. Banks also use fintech to meet consumer demands for 24/7 availability, digital banking, payment through social media connections, biometrics, contactless spending, and more.
Other financial institutions have turned to fintech to automate algorithmic and data-driven investments and trade, while lenders are looking to fintech to help implement new strategies, such as peer-to-peer lending and crowdfunding. Even insurance companies have turned to fintech to leverage technologies, including the Internet-of-Things (IoT) sensors and social media analysis, which allow them to offer more customized and relevant products.
This explains why a 2016 Accenture report found that venture capitalists, private equity firms, and other players have invested $50 billion in almost 2,500 fintech companies since 2010.
Still, the challenges for fintech abound, including ensuring consistent performance and security and overcoming concerns related to evolving regulations and global turmoil. It should come as no surprise, then, that the key to meeting these challenges is a highly adaptable, highly scalable, high performance technology platform. Finastra (formerly Misys), a financial services provider to 48 of the 50 largest banks, relied on in-memory computing to move from batch processing to real-time processing of transactions and market data, and also to introduce new cloud offerings.
The performance solution: In-memory computing
In-memory computing platforms provide parallel, distributed processing across a pool of RAM deployed on a computing cluster. This model enables much faster transaction processing and analytics for data-intensive applications compared to disk-based solutions, along with simple scale-out just by adding new nodes to the cluster. Until recently, the high cost of RAM meant in-memory computing was economically feasible only for very high-value applications. Today, however, a steady drop in the price of RAM has made in-memory computing platforms economical for a much wider range of use cases. Gartner now projects that the in-memory technology market will grow at a compound 22 percent per year to reach $13bn by the end of 2020.
For asset and wealth management, in-memory computing delivers the performance, scale, and interoperability to rapidly roll out new investment products. A modern in-memory data grid can be inserted between existing application and data layers with no rip-and-replace to improve the speed and scalability of existing applications while supporting ACID transactions in an always-on individualised investment environment. Sophisticated streaming capabilities also enable the integration of market and news feeds with portfolio information.
For spread betting, the ability of in-memory computing platforms to keep all data in RAM for extremely fast access with no disk-related slowdowns provides the performance boost necessary to combine and analyse all the required data sources and respond instantly, accurately and reliably to incoming bets and changing odds.
For fintech, in-memory computing delivers the performance boost, scalability, and flexibility to enable firms to innovate in a complex, rapidly growing market that requires the utmost in real-time deliverables, reliability and security. With in-memory computing, fintech solution providers will be able to deliver a far wider range of services to meet evolving demand.
As a mature technology providing high performance, high scalability and cost savings, in-memory computing should now be considered a go-to technology for financial services firms looking to thrive in today’s highly competitive, always growing, information-centric environment. The “In-Memory Computing for Financial Services eBook, Part 3”, is a good place to start.