WHAT COMES NEXT: AI-POWERED INSIGHT AND STRATEGIC FORESIGHT


As trading organisations mature in their data capabilities, the question shifts from “how do we manage data?” to “how do we make data work harder for us?” The next frontier in navigating the Data Deluge is the integration of artificial intelligence (AI) – not merely as a buzzword, but as a foundational technology to unlock insight, drive strategy, and deliver sustained competitive advantage.


AI and machine learning algorithms excel at identifying patterns in complex, high-volume datasets – precisely the type of data environment that defines commodities trading. While human analysts bring context, intuition, and market knowledge, AI brings scale, speed, and a level of pattern recognition that would be impossible to achieve manually.


By training on years of historical trading data, including price movements, geopolitical events, weather anomalies, and macroeconomic signals, AI systems can begin to infer relationships that might otherwise remain hidden. These models can then be used not only to understand what has happened in the past, but to anticipate what might happen next – offering traders a strategic edge in a highly volatile market.


Examples of AI-enabled decision support include:


• Trend Detection and Forecasting – AI models can detect subtle shifts in sentiment or price patterns ahead of broader market recognition, enabling pre-emptive positioning or reallocation of capital.


• Anomaly Detection – Systems can monitor live trading data and flag deviations from historical norms, highlighting potentially fraudulent activity, operational errors, or emerging risks.


• Strategy Backtesting and Optimisation – Traders can use AI to simulate strategies across decades of historical data, fine-tuning parameters based on performance under a wide range of conditions.


• Behavioural Clustering – Algorithms can classify


counterparties, instruments, or market conditions into behavioural groups, guiding dynamic risk strategies or bespoke product structuring.


Importantly, these models need not operate in isolation. Human-machine collaboration is the real sweet spot. AI becomes the co-pilot – monitoring thousands of data streams in parallel, surfacing opportunities, and flagging risks – while traders remain in control, applying judgment, experience, and contextual awareness to validate and act on the insights.


As AI systems evolve, we can expect to see:


• Reinforcement learning being used to optimise execution strategies in real time.


• Natural language processing (NLP) mining news, reports, and disclosures for sentiment, intent, and risk signals.


• Generative AI creating scenario models, decision trees, or even code snippets to accelerate new ideas from concept to execution.


In short, AI will shift data analysis from a largely retrospective exercise to a forward-looking, continuously adaptive discipline. As trading becomes increasingly automated and digital- native, the winners will be those who


fuse human intelligence with artificial intelligence – blending art and science to outpace their competition.


THE FUTURE OF DATA IN COMMODITIES TRADING


The volume and complexity of data will only continue to grow, making a proactive approach to data management essential. Companies that embrace modern data architectures, automation, and advanced analytics will not only survive the Data Deluge but thrive in it - gaining a significant competitive edge in an increasingly complex and fast-paced market.


In the world of commodities trading, data is not just an asset; it’s the foundation of every strategic decision. Companies that invest in the right tools and strategies today will be the ones leading the market tomorrow.


To find out more about how Digiterre helps Energy and Commodities clients tackle their complex data challenges, drop me a line.


LAURENCE PISANI Energies and Commodities Practice Director laurencep@digiterre.com


13 | ADMISI - The Ghost In The Machine | Q3 Edition 2025


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