The securities industry is making significant technological advancements. Where is the company progressing and what is holding them back?
go through Monica SomervilleHead of Capital Markets, Serent
As the financial markets of the future emerge, the technologies used by the securities industry are also evolving. The digital transformation of securities firms will revolve around developments in three key areas: cloud adoption, mainframe modernization, and the way firms utilize data exchange mechanisms.
Celent report Prepare for a cloud-enabled data-driven world Highlights are analyst-led interviews (conducted in Q4’21 and Q1’22) and findings from a survey of 28 technology and operations executives at 19 North American financial institutions (FIs). DTCC, a financial services firm that provides clearing and settlement services to financial markets, commissioned research from Celent to outline the industry’s progress as it transitions. Analysis of this study illustrates key takeaways for Chief Information Officers (CIOs), Chief Information Security Officers (CISOs), Chief Risk Officers (CROs), and Heads of Business Lines (LOBs) on the buy-side and sell-side of capital markets when they evaluate comprehensive digital transformation When the required parallel technology structure.
- cloud adoption
The study found that cloud adoption is almost universal in the securities industry. The main drivers of cloud adoption (for buyers and sellers) are: increased business agility, improved operational efficiency, and increased security and resiliency. The previous focus on Infrastructure as a Service (IaaS) and “lift/shift” to the cloud is changing; companies are recognizing that to reap the true benefits of the cloud, full adoption of cloud technology and new ways of working is required. Cloud-native and cloud-first (meaning new applications are built as cloud-native) approaches are now widely used by financial institutions in the securities and investment management space.
However, the level of cloud adoption by any individual FI varies. Nearly 50 percent of study participants could be described as “cloud leaders.” This cohort has or is moving towards a fully cloud-native stack; their new application development leverages cloud-native technologies, and modernizing legacy applications is a priority. Most cloud leaders work with multiple public cloud vendors, but the study found that AWS and MS Azure together dominate the market share of the capital markets industry.
Over the next two years, many companies will shift to large-scale hybrid private/public cloud-first adoption. By 2024, about a quarter (28%) of companies will be cloud enablers, deploying some applications in public and private clouds while maintaining on-premises infrastructure. Preserving on-premises computing is often used for use cases where latency, performance, or data privacy are prioritized, but FIs lack confidence in the public cloud or the necessary in-house expertise. By 2024, the number of cloud leaders adopting “cloud first” is expected to grow from 37% today to 50%
Finally, a handful of securities firms remain skeptical and have no interest in migrating to the public cloud. External data sharing, cost, the need for high-performance computing, and concerns about cloud service provider (CSP) lock-in are the main reasons why skeptics shy away from moving to the cloud.
While approaches to cloud strategies vary, most take a federated approach, with a core center of excellence (COE). The COE aligns with the architecture team and acts as a center of expertise and consulting, providing flexibility to the business technology team.
The gap between cloud computing and older computing platforms, including mainframe computers, is closing. Mainframes are still ubiquitous across capital markets, with over 50% (and 44 of the top 50 banks) still using mainframes. Just over half (56%) plan to retire mainframes, a process that will take five years or more; the other 44% will maintain and modernize them. The top considerations for mainframe strategic decisions are scaling, skills gaps (possibly technical/resource issues and business risks) and application development agility.
The number of companies that may maintain mainframes is staggering, but the reliance on mainframes comes from their superior capabilities as workhorses, operating with reliability, security, and speed. Because the mainframe plays an ongoing role in supporting core processing, it’s unlikely to go away. Instead, technological advancements (i.e. mainframe containerization) have addressed historical core problems and facilitated mainframe support for modern applications.
- Data exchange and data management
The area that shows the greatest potential for transformation is data exchange mechanisms. As a digital, data-driven organization, you can employ artificial intelligence (AI) approaches including machine learning (ML), natural language processing (NLP), and deep learning.
Today, key areas of artificial intelligence and data are undergoing major changes. Broadly enabling data for insights requires effective enterprise data management; to realize this possibility, companies still need to do a lot of work around the areas of data exchange and data management. Given this need, it’s no surprise that the majority of companies (50%) consider themselves in the “early stages” of company-wide AI adoption. 17% said they were building AI expertise and 33% said AI has been widely adopted. Today, the typical use of AI is in discrete (or even mundane) domains, not enterprise use cases. At the same time, companies are evaluating various types of artificial intelligence, such as NLP for chatbot development, robotic process automation (RPA) and optical character recognition (OCR) for post-trade workflows.
Manual and batch-based data exchange methods have hindered some data initiatives, but progress is expected in the next two years, when real-time data transfer/exchange methods will dominate. Key drivers of data exchange efforts include timeliness and customer expectations/business drivers (both cited by 23% of respondents) and reliability and agility (tied at 14%). An increasing reliance on data markets, distributed ledger technology (DLT), and application programming interfaces (APIs) is one of the most popular approaches to data-related technologies.
Compared to a range of data exchange mechanisms ranging from more advanced methods such as DLT and data feeds to traditional mechanisms including manual methods such as faxing, the investment and adoption of APIs is expected to increase as they gain in customer satisfaction top rated). APIs can enhance a customer’s experience by improving access to functionality or accessing data at a frequency that is convenient for the customer.
Securities firms face ongoing challenges in embracing new technologies. For cloud initiatives, concerns include the cost of moving data in and out of the cloud; storing data in the cloud without clear control of global location also encounters regulatory hurdles related to data privacy and sovereignty. For mainframe initiatives, the total cost of ownership (TCO) requires careful consideration of factors related to the deployment model; the benefits (availability, reliability, and security) may justify the expense of modernizing and retaining the mainframe rather than decommissioning it.
When it comes to data exchange, a full migration to modern data exchange methods has stalled due to the need to support clients that stick to the old method. The poor state of data assets hinders the development of artificial intelligence. While data exchange is promising, the business case for true transformation in the field will require coordination across industries, as well as management of data privacy concerns.
Successful technological transformation in the securities industry depends on understanding these barriers, adopting modern approaches, and making effective firm-wide investments in technological innovation. Digital transformation requires moving beyond traditional technology domains and focusing on customer-centricity and customer support.