Asset and wealth management services providers face mounting competitive pressure on two fronts. On one front, today’s investors, especially younger ones, have very different expectations. Rather than relying on the advice of a single investment advisor, they tend to get more personally involved by consulting peers and independent reports and comparisons. They also seek a wider range of asset classes and investment vehicles, such as startup companies, crowdfunding and international opportunities, and instead of adhering to standard risk-management models and performance benchmarks, they often favor strategies that align with their personal goals.
On the second front, the investment services provider landscape itself has significantly evolved. New providers have arisen to offer the investment vehicles investors want, and by providing 24/7 access and delivering new distribution channels – such as online, mobile, and social media – they are gaining a key competitive advantage. Meanwhile, existing providers are under pressure to reduce their fees, even as the demand for new services, along with increased market volatility and a tighter regulatory environment, threaten to increase costs.
A new white paper by GridGain Systems, “Transforming Asset and Wealth Management with In-Memory Computing,” provides a deep dive into how the industry is using in-memory computing to maintain a competitive edge now and in the future.
In order to cost-effectively meet new customer demands and fend off the competition, asset and wealth management services providers are looking to add a variety of new capabilities and technologies:
- Content automation can streamline communications between customers and financial advisors, while robo-advisors can select individualized investment scenarios for customers based on the specific information they provide.
- Big data, analytics, machine learning, scenario analysis, and even virtual reality are all being employed to help individual investors obtain more information about possible investments, better understand their specific options, and make better decisions based on modeling, historical analysis, and “what-if” analysis.
- Complex Event Processing (CEP) is being used to create algorithms and models that can automatically place orders for customers based on pre-configured ordering parameters and a real-time analysis of current market conditions.
- Blockchain, the digital-ledger technology behind Bitcoin, is now being used for messaging, instructions and payment options to reduce transaction costs and processing time.
- Multi-channel integration and session-state tracking is used to make services – including advice, account setup, quote requests, real-time notification of portfolio changes, etc. – available and consistent across all digital communication channels and platforms, including social media and mobile devices.
- Cloud platforms are a key way that data and applications are made available from any physical or virtual environment, and open source and open architecture solutions facilitate interoperability among platforms.
Beyond meeting customer demands for new services, providers must make sure the investment advice they provide and the actions they facilitate, such as trades, meet increasingly rigorous regulatory requirements, including time-stamped orders, audit trails, and pre- and post-trade compliance checks. In this area, big data analytics is used for real-time monitoring of large quantities of data, such as the current prices of assets across all investment platforms, to ensure regulatory compliance. Fast-data analytics is also used to perform fraud detection and prevention and make automatic decisions about whether to approve trades and other actions such as asset transfers. Finally, fully integrated security strategies ensure privacy and compliance with new cybersecurity regulations.
High performance is the key to success
All these technologies can play a critical role as asset and wealth management services providers adapt to meet their competitive challenges, but implementing them in a way that delivers the promised customer experience – real-time, 24/7 access to investment advice and trading – requires an unprecedented level of performance.
Many companies have found that the most cost-effective path to achieving this performance is utilizing in-memory computing. By keeping data in RAM, in-memory computing eliminates the bottleneck that slows down every other high performance computing strategy: painfully slow disk access. Instead, in-memory computing, coupled with parallel processing and data distribution across a computing cluster, enables service providers with heavy analytics and real-time applications to process transactions about 1,000 times faster, performing hundreds or even millions of transactions per second. This computing strategy is important not just for providing the required new investment services, but also to ensure high availability, disaster recovery, and concurrency across systems.
In the past, in-memory computing was considered far too expensive because of the high cost of RAM. However, memory costs have dropped approximately 30 percent per year since the 1960s, making it far more affordable. While it may still be slightly more expensive than disk-based storage, the performance is so much higher that it improves ROI significantly. Many organizations implementing an in-memory computing platform have seen a tenfold or more improvement in their ROI.
For example, Sberbank, the largest bank in Russia and the third largest in Europe, faced a problem similar to transitioning from traditional, human-based investment advising to a real-time, online environment. The bank was switching from brick-and-mortar services, with a limited number of financial transactions being processed during a finite time period each day, to 24/7 access by online and mobile customers.
Recognizing the need to move to a next-generation data-processing platform to handle the expected massive rise in transaction volume, Sberbank opted for an in-memory computing platform that delivered the required performance and scale using industry-standard hardware. The solution even supported the ability to conduct integrity checking and rollback on financial transactions. According to the bank, the in-memory computing platform delivered very high performance and reliability while being much less expensive than the technology previously in use.