The next step in AI strategy: investment operations

Early adopters are proficient at using AI for research. Further, the ROI for this function is obvious: alpha generation. Firms are now applying learnings from these research-oriented AI projects to investment operations, a use category still in the very early stages of adoption. The ROI for investment operations is primarily efficiency — handling more portfolios and lowering risks. But the ROI is also about making investment data a true strategic asset, beginning with shoring up data quality and governance.

Data connectivity and curation

Managers are leveraging AI to simplify data connectivity, improve data quality, understand new datasets faster, and build usable data products. AI is adept at automating critical data management functions like preparing, processing, and normalizing new incoming financial data. Then, firms are using AI to locate, explore, and understand their datasets, for advanced analysis, visualizations, and reporting. Firms that have self-service, low-code AI and data platforms enable their people to execute the above functions without technical know-how, only using natural language prompts.

An AI reconciliation revolution

Managers are experimenting with leveraging AI for more automated, pattern-driven reconciliation and managing corporate actions. Reconciliation is a taxing manual process required to synchronize information stored across disparate systems that may hold different versions of the same data. AI can help cut out time-intensive manual human intervention, allowing a firm’s skilled humans to set their sights on higher-value tasks.

Corporate actions processing

Inadequate operations technology and poor operational oversight can lead to a manager thinking they have a different position than they truly hold, primarily due to a missed or improperly booked corporate action. If a firm handles corporate actions manually and is a high-volume business, it may necessitate hiring many people just to process trades. As volumes skyrocket, this is becoming less sustainable.

AI can help handle automated lifecycle events, including corporate actions on public equities or mergers and acquisitions. This ensures that when PMs arrive in the morning, the corporate actions have already been auto processed, empowering the front office with the right information promptly. AI-powered automation for investment operations like reconciliation and corporate action processing is essential in today’s complex markets.

Solving the unstructured data problem in public and private markets

Unstructured data presents thorny problems for operations as well as research. The management of capital calls is one critical area. Without automation in place to speed the tracking and generation of capital calls, the manual effort is onerous and error prone. It requires utter precision to ensure critical transactions are executed on time. For example, institutional investors like pension funds are increasingly seeking AI-powered automation because they generally trade in 75% public securities and 25% private asset classes. GPs send LPs quarterly, weekly, and in some cases daily communications for things like capital calls on these unstructured products in the form of security information and transaction information. And it still comes in PDF and spreadsheet formats. LPs literally have people go through 100 or 200 documents and manually input them into an Excel sheet. Without AI, it's simply impossible to deal with this volume of frequent, scattershot data.