By Kasper Walet, Founder and Partner AI Energizer and Maycroft

I recently read a very interesting article in the Financial Times about how commodity trading firms are still lagging behind other trading sectors in using Artificial Intelligence solutions. but that the industry is trying hard to catch up. These findings are in line with my practical experiences in our advisory services business: AI Energizer.

Particularly in today’s market situation where margins are squeezed, commodity traders are looking for tools that could help them to increase profitability. Intelligent technologies such as robotic process automation and machine learning, offer new opportunities to improve process performance and realize significant cost savings. These technologies can be implemented in short sprints, focused on a specific problem, with manageable costs.

If you click here, you can read our white paper about successful AI projects we executed for two of the leading energy commodity trading firms in Europe.

Compared to other financial and industrial sectors, commodity traders are coming from way behind. Currency, equities and interest-rates investors have already used algorithms, machine learning and artificial intelligence to turn data into successful trades for years. Now, commodity traders are seeking ways of exploiting their information to help them profit from price swings as well. It really is a combination of knowing what to look for and using the right mathematical tools for it.

Traders are looking to gather data on a large scale and run machine-learning algorithms to find patterns linking fundamentals with price movements, thus improve decision-making in trading and, as a result, the profitability. With a properly trained algorithm and a sufficiently sized historical data set, a company using machine learning to identify patterns in the trading data - even when the data has inconsistencies – will reduce redundant trades and streamline the entire process. To accommodate all this commodity trading firms are investing in people, processes and systems to centralize their data.

Despite this new enthusiasm, the road to electronification may not come easily. One issue is that some of the larger commodities traders face internal resistance in centralizing information on one platform. With each desk in a trading house in charge of its profit-and-loss account, data are closely guarded even from colleagues. The move to ‘share all our data with each other’ is a very, very big cultural shift.

Another problem is that in some trading houses, staff operate on multiple technology platforms, with different units using separate systems. Rather than focusing on analytics, some data scientists and engineers are having to focus on harmonizing the platforms before bringing on the data from different parts of the company. Even where the digital infrastructure is in place, it may take some time before AI becomes a large part of commodities trading.

Company leaders should start with the following three steps:

Step 1: Focus on value

As AI can solve targeted problems, it's up to company leaders to identify applications that offer the most potential value. Demonstrating strong returns in a short time will convert the cynics. AI projects typically happen in a series of sprints and can be completed in around 3-4 months.

Step 2: Change Management

Automation alone does not save money or improve performance: People and processes will also have to change. This is an area where there is understandable anxiety; automation stokes fears that machines will take people's jobs. But practice suggests that many of the tasks being automated are activities people tend not to want to undertake, such as spending half the day pulling and loading data. Or they are tasks for which small automation can actually improve people's performance - for instance, by introducing predictive algorithms that help them make better decisions and free up their time for more rewarding, interesting, and higher­ value-add activities.

This is an entirely new way of working, and company leaders will need to ensure that both they and their people have the right knowledge and skills, such as programming and data science knowledge and process improvement skills - to be successful. And they'll need to ensure that everyone's mind-set and behaviors also shift accordingly.

Step 3: Strategy is King

The backbone of this new way of working is strategy. Companies need to know their own strengths. There will be "keep the lights on" activities that can become touchless, as well as differentiating activities that should become increasingly intelligent. Moreover, companies need to take a fresh look at their organizational structure to ensure that it gives teams the freedom to develop creative solutions and experiment.

Conclusion

Concluding we can say that digitization is increasingly driving trading, and needs to be embraced, as many commodities executives believe. “It’s another tool that traders have to understand.”