The WEB3 Operating Model for Commodities is now required’
As a professional within the commodities industry, the above are a few key topics that I frequently discuss with my colleagues and peers. Recently, I had the opportunity to engage in a lively conversation about these subjects with two prominent entrepreneurs in the space, during a lunch meeting in Geneva.
As we shared updates about our respective businesses, the conversation naturally drifted towards the impact of technology on the commodities industry. All three of us have extensive experience within the top three trading companies in the world, and we share a deep passion for how technology is transforming the industry.
I began by highlighting my belief that artificial intelligence (A.I) will have a significant impact on the Middle Office Function, and that tokenization has the potential to fundamentally change accounting processes. I emphasized the importance of understanding the basics of crypto for members of the C-suite, particularly the CFO, in order to drive modernization within their businesses.
However, as the conversation progressed, it became clear that while there are many visionary leaders within our industry, it will take a collective effort on the scale of OPEC to truly bring about the technological advancements we desire. There are certainly external projects and initiatives aimed at helping commodity traders navigate these changes, but the question remains: do they have the internal resources and expertise to make it happen?
The reality is that while many in our industry are excited about the potential of blockchain, tokenization, and A.I, most have yet to even begin exploring these technologies. The primary focus remains on improving existing Energy and Trading Risk Management (ETRM) systems.
As someone who has spent years working on operating models that are designed to complement ETRM systems, rather than adding unnecessary headcount, I am acutely aware that we are now at a point where a new “WEB3 Operating Model for Commodities” needs to be developed. This may require freezing enhancements to current ETRM systems, but the potential benefits of embracing new technologies far outweigh the short-term costs.
One of the entrepreneurs at lunch pointed out that the IT divisions of many trading companies are primarily focused on fixing existing systems, and may not have the budget or expertise to take on such a major shift. I agree that this is a significant challenge, but I also believe that there is an opportunity for individuals with both deep industry knowledge and a WEB3 focus to step in and lead the way.
Here are several ways that Artificial Intelligence (AI) can automate roles within a trading company:
- Algorithmic trading: AI can be used to develop algorithms that can analyze market data and execute trades on behalf of the trading company.
- Predictive modeling: AI can be used to build predictive models that can identify patterns and trends in financial data, which can help traders make better decisions.
- Risk management: AI can be used to monitor and manage risk by analyzing data and identifying potential threats to the trading company’s portfolio.
- Portfolio optimization: AI can be used to optimize the trading company’s portfolio by analyzing market data and identifying the best investments.
- Trade execution: AI can be used to automate the execution of trades, such as identifying the best price for a particular security and executing the trade at that price.
- Backtesting: AI can be used to backtest trading strategies to identify which strategies are likely to be most successful in the future.
- Compliance: AI can be used to monitor trades for compliance with regulatory rules, such as detecting insider trading or market manipulation. Data analysis: AI can be used to analyze data from various sources to inform trade decisions.