How Capital Markets Firms Can Revamp Data Management in the Age of Artificial Intelligence

Although back-end platforms are generally less fragmented, it is not uncommon for companies to run multiple post-trade systems. According to a recent study by Firebrand, only 14% of companies surveyed have a single system to process all of their asset classes; the others have silos by asset class and by geography. All of 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. Therefore, a top priority for any financial markets firm must be to establish standardized master data across these multiple systems and a common messaging layer that reduces friction and risk when moving data from one system to another. other.

Share. Using a common data model can help all systems speak the same language, reducing fragmentation, friction, and cost. This makes it easier to move data between systems, which in turn makes it easier to share data between functions and employees. Specifically, companies should strive to make it easier to share 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 of positions, margins and risk levels. It also paves the way for better client analysis, giving traders a better understanding of client portfolios and areas of interest. In turn, better access to front office data can improve critical back office functions such as risk management, balance sheet optimization and compliance. In the fragmented legacy model, any regulatory or reporting change requires intervention in multiple front-end and back-end systems. Real-time data sharing eliminates the need for this redundant effort, minimizing both cost and risk.

Simplification. Data standardization between front-end and back-end systems will go a long way in 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 the systems themselves through componentization and interoperability, an achievement whose efficiency benefits will go far beyond the Data managment. By following this strategy, companies can achieve lower costs, faster processes, increased agility, greater accuracy, better business decisions, and better controls. All of this adds up to potentially higher revenue, wider margins and/or lower risk.

Capital markets firms that focus on opportunities in the three areas of data standardization, data sharing, and simplification will unlock increasing value from data. Consider an example: artificial intelligence can enable the creation of powerful analytics that can predict which transactions are likely to fail. By flagging these risky trades in advance, these systems allow traders to rethink their strategies and operational teams to work proactively to prevent failure.

There are many more examples of how capital markets firms will benefit from artificial intelligence, if they can provide AI solutions with the hard data they need. Many leading companies achieve these benefits by partnering with specialist vendors to accelerate the organizational transformations needed to do so.

As artificial intelligence becomes a necessity to stay relevant, companies that invest in creating the right database will create a competitive gap and see outsized benefits.