In the rapidly evolving trading landscape, the CIO/CTO shoulders significant responsibilities, including overseeing the maintenance, enhancement, and updating of the internal ETRMS, managing PMS, addressing the concerns of business analysts, and fulfilling constant business demands. With numerous competing priorities, allocating resources to implement another solution may not be the most efficient use of their time. However, with over 100 AI solutions, there is a promising opportunity to revamp FRONT, MIDDLE, and BACK-OFFICE functions, potentially streamlining the IT development roadmap.
In the current era of technological advancement, where streamlining business operations is of paramount importance, the advent of AI-driven positions has opened up new avenues to automate not just the entirety of a business, but also the pivotal IT division.
Building an AI team would not have even made the plateau of projects sitting on a CIO/CTO desk, but today, if your CTO/CIO is not exploring such technologies, it may be time to move to a more web-appreciative chief or take the road that some commodity trading giants are doing and hire a Chief AI Officer. While tech-related, these positions are given a clear directive in companies to explore the possibility of utilizing AI to optimize operational efficiency by reducing headcount, eliminating unnecessary projects, streamlining processes, and mitigating human error. The focus is on significantly saving time and enhancing productivity by automating tasks performed manually. Additionally, the aim is to identify and retire processes that are no longer needed or have been rendered obsolete, thereby freeing up resources to focus on more strategic initiatives.
While the full capabilities of AI have yet to be exposed to trading companies, many suggest that middle managers within the business are still being prepared to accept AI because of the disruption it would cause to the army of people they have built around them. People protection is a tough game right now, but for those of us who understand, appreciate, and are fully versed in the capabilities of AI, we know that the rise of the group AI officers would clean up the operating model quicker than if Dyson built the model.
When leveraging AI technology in the commodity trading, most systems include NLP and ML technologies. When paired together, they create a unified “smart” system that can pick up on specific words and phrases traders use and learn from all the data traders deal with daily. This is just one example of how AI technology can help streamline and optimize commodity trading processes.
Because of the fast trading, most traders need more time to enter a deal right after it is agreed upon. This can present a challenge because they may not recall all the specifics, which can result in mistakes when entering details into the deal capture system. AI technology allows the user to reduce these potential pitfalls by streamlining the deal capture process so that trades can be entered while they are still fresh in their mind, saving time, and generating more accurate data. The solution helps avoid these inconsistencies’ significant impacts on downstream functions such as schedulers, risk, and accounting, each of which may be impacted by deal entry errors that are often critical inputs to their daily processes.
Software offerings in the market are specifically tailored to more efficient and streamlined commodity trading. For example, Venus Technology Ventures Mistro is an NLP and ML technology created by former traders. It is configured to understand commonly used trading language, and its ML capabilities enable it to quickly learn what types of deals a trader would typically perform. Admins and/or users can also add specific language used on their trade floor into their dictionaries. Once configured, the internal dictionary picks up on keywords and phrases and translates them into something that can be leveraged for other processes and applications.
Incorporating AI solutions within commodity trading increases productivity and saves time through automation. Every day, a trader uses multiple forms of communication, such as messaging platforms, emails, or phone calls, to execute deals. It is difficult for a trader to remember a specific deal’s details. AI, and NLP and ML can help reduce the potential for mistakes and streamline the deal capture process, ultimately generating more accurate data and increasing overall efficiency.
So the rise of the GROUP AI OFFICER presents a unique opportunity to revolutionize front, middle, and back-office functions within commodity trading.
By leveraging AI technology, companies can optimize operational efficiency, reduce headcount, eliminate unnecessary projects, and mitigate human error. With the potential to save significant time and enhance productivity by automating tasks currently being performed manually, integrating AI solutions within the commodity trading space is a worthwhile pursuit. I am well-versed in the subject if you need help moving in this direction, and I would be happy to assist.