For years, automation in finance was basically about speeding up repetitive work. Today, the change is greater: automation is becoming a structural advantage. It determines how quickly a person converts information into decisions and how well the operation holds up when the market moves. 

This happens for one simple reason: the financial industry lives on mountains of data that must be processed quickly, accurately, and continuously. When that cycle is slow or manual, opportunities are lost, points of failure multiply, and operations become more expensive. Automation not only speeds things up; it also reduces human error, detects inconsistencies earlier, and allows for 24/7 operation. And thanks to recent advances, today it's not just about processing. It's also possible to find patterns and anticipate scenarios in near real time, even incorporating signals from news and public conversation. 

In investments, this is seen less as a great new tool and more as improvements in many parts of daily work, comparing figures between systems, reviewing prices and valuations, sorting data, preparing reports, and triggering alerts when something is out of the ordinary. It's hardly noticeable, but there's a lot to be gained: fewer errors, more timely information, and more confidence in measuring risk and making decisions. 

This is already evident in large institutions. JPMorgan, for example, automated the reading of credit agreements with an internal tool (COIN), drastically reducing time and manual work. In the world of investments, there has also been a growing effort to "connect the entire flow," from the investment decision to the transaction and reporting, to prevent work from being broken up between systems and ending up being done manually. Platforms such as Aladdin aim to achieve this connection, and players such as State Street (which operates much of the day-to-day machinery of funds and custody) have developed solutions to unify data and processes throughout the investment cycle. At the same time, global banks are deploying internal assistants to speed up routine tasks (summarizing documents, preparing drafts, searching for information, or extracting key points, etc.), such as the GS AI Assistant (Goldman) or Morgan Stanley Assistant.  

However, automation also amplifies the impact of an error. When a decision depends on automatic flows, bad data, a critical supplier, or a poorly calibrated rule can spread faster and further. That is why, alongside the enthusiasm, control standards are growing today: data traceability, monitoring, and governance so that speed does not trump reliability. 

Finally, the human factor: automation does not eliminate work, it displaces it. The World Economic Forum estimates that by 2030 a significant percentage of key skills will change, reinforcing the need for training and retraining. In terms of investment, this translates into teams moving from performing repetitive tasks to designing, supervising, and auditing automated processes. 

The strategic question, then, is not whether to automate, but where to start: focus first on areas where errors and risk are most costly; then on high-volume processes where automation scales best; and finally on cases where improving the quality and timeliness of information directly enhances decision-making.

Matías Márquez 
Financial Funds Analyst, Fynsa AGF