As artificial intelligence takes on a growing role in global capital markets, financial service firms are developing a seemingly insatiable appetite for high-quality, real-time data. Satisfying that appetite will be no easy task.
The industry’s fragmented, legacy technology platforms were not built to process vast amounts of data. As a result, efforts to develop modern data management capabilities often end up morphing into sweeping overhauls of enterprise technology platforms. To date, these projects are moving slowly. According to the 2022 Broadridge Digital Transformation and Next-Gen Technology study, only 20% of capital markets firms have reached the advanced stages when it comes to building a central data platform with access to data across siloes.
Given the expense and risk associated with such large-scale transformations, CIOs and senior management teams of capital markets firms should hesitate before committing to complex, multi-year efforts. Instead, they should break down the process by objectively assessing their starting point, defining top priorities for growth and a target end state, and then identifying a series of incremental steps that will move the organization closer to those objectives over time. These steps should represent near-term wins that start the journey and demonstrate early benefits. Firms should focus on identifying opportunities in three key areas:
Normalization. Systems fragmentation is an obstacle to efficient data management. That goes for companies in any industry. It’s an especially vexing problem for large capital markets firms, many of which are the products of multiple mergers and acquisitions in which incoming IT systems were bolted onto the existing platform. Some firms have as many as 20-40 order management systems for different asset classes and regions, with 10-15 systems for cash equities alone. While back-end platforms are usually less fragmented, it’s not uncommon for firms to run multiple post-trade systems. According to recent research by Firebrand, only 14% of respondent firms have a single system for processing all of their asset classes; the rest have silos by asset class and geography. All these front-end and back-end systems have their own reference data and unique data formats. From a data management perspective, this is a recipe for error and inefficiency. As a result, a top priority for any capital markets firm must be establishing normalized reference data across these multiple systems and a common messaging layer that reduces friction and risk in the hand-off of data from one system to another.
Sharing. The use of a common data model can help all systems speak the same language, reducing fragmentation, friction, and costs. This makes it easier for data to move across systems, which in turn makes it easier to share data across functions and employees. Specifically, firms should be working to facilitate the sharing of back-office data with the front office, and front-office data with the back office—all in real-time. Better access to back-office data gives traders a real-time view into positions, margins, and risk levels. It also paves the way for enhanced client analytics, giving traders a better understanding of client portfolios and areas of interest. In turn, better access to front-office data can improve essential back-office functions like risk management, balance sheet optimization and compliance. In the fragmented legacy model, any regulatory or reporting changes require intervention in multiple front- and back-end systems. Real-time data sharing eliminates the need for this duplicative effort, minimizing both costs and risk.
Simplification. Normalizing data across front-end and back-end systems will go a long way toward reducing the reconciliation burden and limiting the risk of data errors. However, a more strategic solution is to solve the problem at its source by consolidating systems themselves through componentization and interoperability—an achievement whose efficiency benefits will go far beyond data management. Following this strategy, firms can achieve lower costs, faster processes, enhanced agility, more accuracy, better trading decisions, and better controls. All this adds up to potentially higher revenues, expanded margins, and/or lower risk.
Capital markets firms that focus on opportunities in the three areas of data normalization, data sharing, and simplification will unlock increasing value from data. Consider just one example: Artificial intelligence can enable the creation of powerful analytics that can predict trades likely to fail. By flagging these at-risk trades in advance, these systems allow traders to rethink their strategies and operations teams to work proactively to prevent the fail.
There are scores of other examples of how capital markets firms will benefit from artificial intelligence—if they can supply AI solutions with the robust data they require. Many leading firms are achieving these benefits by partnering with specialist vendors to accelerate the organizational transformations required to do so.
As Artificial Intelligence becomes a necessity to stay relevant, firms that invest in creating the right data foundation will create a competitive gap and see outsized benefits.
