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.
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.”
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Headline crude prices for the week beginning 11 February 2019 – Brent: US$61/b; WTI: US$52/b
Headlines of the week
Midstream & Downstream
Global liquid fuels
Electricity, coal, renewables, and emissions
2018 was a year that started with crude prices at US$62/b and ended at US$46/b. In between those two points, prices had gently risen up to peak of US$80/b as the oil world worried about the impact of new American sanctions on Iran in September before crashing down in the last two months on a rising tide of American production. What did that mean for the financial health of the industry over the last quarter and last year?
Nothing negative, it appears. With the last of the financial results from supermajors released, the world’s largest oil firms reported strong profits for Q418 and blockbuster profits for the full year 2018. Despite the blip in prices, the efforts of the supermajors – along with the rest of the industry – to keep costs in check after being burnt by the 2015 crash has paid off.
ExxonMobil, for example, may have missed analyst expectations for 4Q18 revenue at US$71.9 billion, but reported a better-than-expected net profit of US$6 billion. The latter was down 28% y-o-y, but the Q417 figure included a one-off benefit related to then-implemented US tax reform. Full year net profit was even better – up 5.7% to US$20.8 billion as upstream production rose to 4.01 mmboe/d – allowing ExxonMobil to come close to reclaiming its title of the world’s most profitable oil company.
But for now, that title is still held by Shell, which managed to eclipse ExxonMobil with full year net profits of US$21.4 billion. That’s the best annual results for the Anglo-Dutch firm since 2014; product of the deep and painful cost-cutting measures implemented after. Shell’s gamble in purchasing the BG Group for US$53 billion – which sparked a spat of asset sales to pare down debt – has paid off, with contributions from LNG trading named as a strong contributor to financial performance. Shell’s upstream output for 2018 came in at 3.78 mmb/d and the company is also looking to follow in the footsteps of ExxonMobil, Chevron and BP in the Permian, where it admits its footprint is currently ‘a bit small’.
Shell’s fellow British firm BP also reported its highest profits since 2014, doubling its net profits for the full year 2018 on a 65% jump in 4Q18 profits. It completes a long recovery for the firm, which has struggled since the Deepwater Horizon disaster in 2010, allowing it to focus on the future – specifically US shale through the recent US$10.5 billion purchase of BHP’s Permian assets. Chevron, too, is focusing on onshore shale, as surging Permian output drove full year net profit up by 60.8% and 4Q18 net profit up by 19.9%. Chevron is also increasingly focusing on vertical integration again – to capture the full value of surging Texas crude by expanding its refining facilities in Texas, just as ExxonMobil is doing in Beaumont. French major Total’s figures may have been less impressive in percentage terms – but that it is coming from a higher 2017 base, when it outperformed its bigger supermajor cousins.
So, despite the year ending with crude prices in the doldrums, 2018 seems to be proof of Big Oil’s ability to better weather price downturns after years of discipline. Some of the control is loosening – major upstream investments have either been sanctioned or planned since 2018 – but there is still enough restraint left over to keep the oil industry in the black when trends turn sour.
Supermajor Net Profits for 4Q18 and 2018
- 4Q18 – Net profit US$6 billion (-28%);
- 2018 – Net profit US$20.8 (+5.7%)
- 4Q18 – Net profit US$5.69 billion (+32.3%);
- 2018 – Net profit US$21.4 billion (+36%)
- 4Q18 – Net profit US$3.73 billion (+19.9%);
- 2018 – Net profit US$14.8 billion (+60.8%)
- 4Q18 – Net profit US$3.48 billion (+65%);
- 2018 - Net profit US$12.7 billion (+105%)
- 4Q18 – Net profit US$3.88 billion (+16%);
- 2018 - Net profit US$13.6 billion (+28%)