Artificial intelligence is revolutionising the world of commodity trading, reshaping how traders analyse markets, manage risk, and execute strategies. With AI-driven algorithms processing data in real time, commodity traders can make faster, more informed decisions, gaining a competitive edge in volatile markets.
What Is Commodity Trading?
Commodity trading is broadly understood as the process of buying, transporting, storing, transforming and selling physical commodities.
Companies engaging in commodity trading vary greatly in scope, size and organisational form, ranging from micro-enterprises to large diversified multinationals. Some trade a wide range of commodities within or across commodity categories, while others are highly specialised, dealing in one or a small number of commodities bought from or sold to several actors.
How Is AI Used In Commodity Trading?
AI stands poised to revolutionise the commodities market by optimising operations, enhancing market analysis, and promoting sustainable practices. However, the successful integration of AI depends on addressing concerns related to data security, literacy and the perceived threat of job displacement.
How Will AI Impact Commodity Trading?
The rise in AI tends to bring both challenges and opportunities in the world of commodity trading. Here are the different ways AI will impact commodity trading:
Reducing Complexity
The intricate supply chain that goes into producing and distributing soft commodities is one of the main obstacles to using commodity trading solutions in this sector. Soft commodities, as opposed to more conventional commodities like metals or oil, are usually produced and processed in a number of different places across the globe. Keeping track of inventory, controlling transportation and storage expenses, and guaranteeing regulatory compliance can all become difficult as a result.
The enemy of innovation and advancement is that same complexity. In the commodity trading industry, agility, speed, and the flexibility to act on information in real-time are huge factors. Fortunately, AI excels in complicated situations. You will be able to go right to the point in no uncertain terms, from collecting, aggregating, and handling all the data to producing understandable reports and user-friendly dashboards for any means of speedy decision-making.
Navigating the Futures Market
The fact that a lot of soft commodities are traded on futures markets presents another difficulty for commodity trading. Although they can be a helpful tool for risk management, futures contracts are also very volatile and susceptible to abrupt changes in market mood. This implies that traders and risk managers must be able to respond swiftly to market shifts and make defensible choices based on up-to-date information.
Volatility in soft commodities markets can be caused by a variety of factors, including weather patterns, geopolitical events, and changes in consumer behavior.
You can use AI to improve your decision-making by simulating possible transactions, experimenting with a wide range of virtual situations to see how things might work out, and using intelligent forecasting.
Mitigating Risk
Software for trading commodities has always been complicated. AI can just make your interactions with them better. Effective risk management is already possible with a number of tools and techniques. Actually, they serve as the foundation for the software’s risk management section. AI can provide a clearer view and a better position for decision-making in this situation as well.
Risk management tools can include the use of advanced analytics and machine learning algorithms to predict market trends, as well as the implementation of rigorous supply chain management processes to ensure that inventory is tracked and managed effectively.
Finding Paper Trail Mistakes
Commodity trading is a volatile and fast-paced industry. Agiblocks, for example, lets you put up a series of “events” for every delivery so you can keep track of what was done, when, and by whom. Status, transportation, counterparties, delivery lines, and so on are a few examples. Users of Agiblocks can be required to provide documentation to their governments for each of those actions, which ought to create a clear trail.
The Future Prospects Of AI In Commodity Trading?
Emerging technologies and possible market changes that could change the landscape are what define the future of AI in physical commodities trading. These developments are expected to improve decision-making skills and efficiency:
Advancements On the Horizon
Commodity trading is expected to change as a result of advancements in AI technology, such as machine learning and natural language processing. By examining large datasets, like historical trends and current market indications, predictive analytics will help traders predict commodity prices more precisely.
AI algorithms will be used by automated trading systems to execute deals at the best times. Profitability is increased, and human error is reduced. Furthermore, integrating blockchain technology may increase transaction trust and transparency, enabling improved tracking of goods across the supply chain.
Potential Market Shifts
It is believed that market dynamics would change as AI technology advances. A more level playing field for traders could result from greater transparency and less information asymmetry. AI-enabled solutions could help small and medium-sized businesses compete more successfully with larger companies.
As more trading choices are made by automated systems, human jobs may shift from execution to planning and monitoring. Professionals must adjust to this change by learning new AI and data analytics techniques.