If you work in commodity trading, you know how competitive and dynamic this industry can be. Day to day, traders need to make quick decisions, assess risk, and use knowledge of the market to gain an edge.
The need to remain competitive in this market is driving decision-makers across the sector to seek out ways to optimise their trading strategies, improve profitability, and avoid costly fines incurred by manual errors. AI and machine learning (ML) provide a unique opportunity to automate, streamline, and improve trade lifecycle management from start to finish.
Yet, the true value of AI and ML can only be harnessed by making sure the underlying business problem is fully understood and key stakeholders are part of the process – shaping the solution through their contextual knowledge.
The solution must be explainable and well-understood if you want it to be widely adopted and trusted by users. A ‘black box’ AI solution only leads to suspicion, uneasiness, and the risk of creating more shelfware. Instead, the expertise of traders must be incorporated at every stage to ensure the decisions are explainable and the outputs are effective.
For all its merits, AI doesn’t work on its own. That’s why we’ve tapped into the skills and expertise of leading NTT DATA UK&I data scientists, like Rebecca Cook, leader of our trading algorithm development team.
Together, we’ve outlined the six most exciting use cases for AI at every stage of the trading lifecycle, from fraud detection, to smart forecasting, power demand, and everything in between.
1. Simulating future scenarios with predictive analytics in the pre-trade stage
With pre-deal analytics, your traders can test potential trades before committing to them, modelling a wide variety of virtual scenarios and using smart forecasting to see how that will affect the profitability of the trade.
Traders can use predictive AI algorithms to simulate multiple scenarios and generate a comprehensive prediction of the outcome of each trade. By putting this technology to work, your entire organisation gets an accurate and reliable view of a trade’s potential returns and can identify any risks before they emerge.
2. Augmenting decision-making with intelligent automated trade entry at the trade capture stage
The ability to make quick, accurate decisions is one of a commodity trader’s most important attributes. We’re not aiming to replace that; instead, we’ve seen the benefits of using AI to augment their skills: providing the trader with faster options and alternatives to help speed up the decision-making process.
To accurately capture trades, AI-powered intelligent automated trade entry helps you to automate and streamline the process of capturing, interpreting, and entering trades into the system. This not only frees up valuable time and resources but also reduces the potential for small human errors that can compound further down the line.
Intelligent automated trade entry grants you access to a wide array of tools and analytics that can save time and reduce the margin for error: including smart order routing, price optimisation, best execution, and optimal allocations of block trades.
3. Preventing errors with anomaly detection in the trade validation stage
If the trade validation stage is where you’re experiencing the most issues, you can use AI here to look for anomalies and patterns in new trades by weighing them against historic trade behaviour. This makes perfect use of AI’s pattern-matching capabilities and its ability to analyse large volumes of information to find anomalies.
With AI, you can find trade anomalies either at the point the trade is made, or within the confirmation window – which, crucially, means that you have time to reverse these trades if necessary.
By detecting any fraudulent activities or errors in the data entry, anomaly detection shields you against:
- Costly settlement errors
- Hefty regulator fines
- Overly optimistic risk reporting
4. Automating documentation with generative AI in the trade confirmation stage
The best way to search for mistakes, fraudulent activity, and discrepancies is to ensure that you have a consistent, thorough paper trail for every trade that you make. However, traders – as a rule – would prefer to be spending time trading and watching the markets rather than filling out endless paperwork.
That’s why generative AI is the ideal technology to auto-generate and auto-send confirmation documentation at the confirmation stage – saving time spent on manual work by valuable, highly-skilled employees and reducing the likelihood of documentation falling through the cracks.
5. Optimising scheduling profiles with neural networks at the trade scheduling stage
When integrating AI solutions with the trading process, the key is to keep the human in the loop: combining their decision-making with the AI’s ability to rapidly generate insights and analysis. When it comes to scheduling your trades, it’s no different.
By using AI to evaluate real-time market and system prices and constraints, you can gain valuable insights into the optimal scheduling of trades. By allowing traders to respond faster and more accurately to shifting market conditions and keeping manual effort to a minimum. AI can ensure that your organisation can effectively respond to bottlenecks in the value or supply chain, as well as geopolitical turbulence.
6. Managing portfolio risk levels with machine learning at the position and risk management stage
When it comes to risk management in this market, the margin for error is incredibly small. Any performance boost, anomaly and pattern recognition, or split-second calculation that AI can add to the abilities of human analysts will greatly enhance a firm’s resilience against unexpected risk levels.
At the position and risk management stage, you can take advantage of machine learning models that highlight patterns in risk data by using intelligent analysis. If you want to explore all the tools at your disposal to keep portfolio risk levels within operational limits, this may be the solution for you.
Why NTT DATA?
Staying competitive in a dynamic market isn't just a nice-to-have; it's a necessity. You can rely on AI to automate, streamline, and optimise trade lifecycle management in commodity trading, but only when you’ve identified and understood the key business challenge you’re trying to solve. This means putting the foundations in place to make AI trusted and accessible.
At NTT DATA UK&I, our teams have deep expertise and many years of experience in advisory and implementation services. We’ve solved business challenges by applying advanced analytics, machine learning, and AI technologies in the most effective way with traders across the world.
Our practical experience working with trading firms to deliver AI models for the use cases outlined here ensures that we have models and guidelines that put safe and ethical AI development at the forefront of our strategies.
I’d welcome the opportunity to discuss how NTT DATA UK&I can apply AI and ML as part of your trading lifecycle. Please book a meeting with me to learn more.