Introduction

This week representatives of ComFin Software attended the EDAY 2024 in Vienna, Austria. The EDAY illustrated how Artificial Intelligence (AI) can unleash business opportunities and offer valuable assistance to companies. Nevertheless, the adoption of state-of-the-art technologies prompts considerations about the consequences of data usage and the significance of data, topics that were extensively explored during the discussions. This has motivated us to scrutinise the subject, with a particular emphasis on our industry.

In today’s rapidly evolving business landscape, the term ‘Artificial Intelligence’ has become a key buzzword, promising revolutionary changes across industries. AI, encompassing natural language processing, deep learning, and autonomous systems, boasts the capability to analyse vast datasets in order to enhance decision-making processes. However, while AI has attracted significant attention, its application within Commodity Trading and Risk Management (CTRM) systems may not live up to the hype. CTRM systems, critical for managing the complexities of commodity trading, require thorough examination before integrating AI solutions.

AI Complexity vs. Practicality in CTRM Systems

CTRM systems operate within highly dynamic environments, where they are tasked with managing a diverse array of real-time data streams originating from various sources, including market prices, logistics data, and regulatory frameworks. These systems form the foundation of commodity trading operations, enabling seamless execution of trades while ensuring compliance with regulatory requirements. Despite the vast potential of AI to process extensive datasets, its practical application within the realm of real-time commodity trading encounters significant hurdles.

The volatility inherent in commodity markets, shaped by multifaceted factors including geopolitical events, economic indicators, and market sentiment, presents formidable challenges to AI algorithms. The algorithms are designed to provide accurate predictions and insights to traders and risk managers. However, the dynamic nature of commodity markets demands constant vigilance and adaptation, making it difficult for AI to consistently generate precise forecasts without continual refinement. Consequently, the practical utility of AI in real-time commodity trading scenarios is subject to ongoing evaluation and refinement.

Data Quality and Availability in CTRM

In the world of commodity trading, the availability of accurate and comprehensive data is crucial for any AI systems to provide trustworthy insights. Unfortunately, we frequently encounter challenges such as incomplete, inaccurate, or delayed data. To successfully integrate AI into CTRM systems, it is essential to have access to both historical and real-time data streams that are not only accurate but also reliable. However, ensuring the quality and availability of such data is no simple task, especially given the diverse nature of commodity markets. It requires careful coordination (e.g. timely data updates and validation) and robust infrastructure (e.g. stable internet connection and data security measures) to maintain data integrity and accessibility.

Risk of Over-reliance on AI in CTRM

While AI provides automation capabilities, an excessive reliance on its algorithms can foster complacency among users. In the realm of commodity trading, human intuition and experience play a vital role in understanding market nuances and making well-informed decisions. Over-reliance on AI algorithms may result in the overlooking of crucial factors that the algorithms are unable to grasp, potentially exposing trading firms to unexpected financial risks. Also, despite recent progress, those algorithms sometimes offer suggestions that are irrational if not utter nonsense. Thus, striking a balance between leveraging AI’s strengths and recognising human expertise is imperative for effective decision-making. It should always be the human being who makes the final decisions. This approach ensures that traders can capitalise on AI’s capabilities while remaining attentive to the nuanced complexities of the market.

Regulatory and Compliance Issues in CTRM

The commodity trading industry operates within a highly regulated environment, where adherence to compliance standards is of the utmost importance. Despite the advanced capabilities of AI, errors in AI systems could lead to regulatory breaches. Therefore, ensuring alignment with regulatory requirements demands meticulous oversight. This oversight is necessary to mitigate the risk of facing significant fines and legal consequences for trading entities. Maintaining compliance is crucial for safeguarding the reputation and financial stability of trading firms. Thus, robust measures must be in place to prevent regulatory lapses and ensure adherence to industry regulations at all times. Governments around the world have only just begun to draft and pass legislation tailored to AI technology. In the coming months and years, we expect both sector-specific and broader AI regulations to impact almost every industry as the use of AI expands.

High Costs and Implementation Challenges of AI in CTRM

The integration of AI into CTRM systems involves substantial costs and complexities. These encompass investments in technology infrastructure and skilled personnel. However, for smaller trading firms, the expenses linked with AI integration may outweigh the potential benefits. Industry surveys indicate that the average annual cost of implementing AI technologies in the CTRM space ranges from $500,000 to $1 million, with the scale and scope of integration being the primary determining factor. Furthermore, ongoing operational costs, including data storage, maintenance, and personnel training, contribute to the substantial financial commitment required. Moreover, navigating the intricacies of implementation presents significant challenges. This complicates the adoption process further, requiring careful planning and resource allocation. As such, trading firms must assess the costs and benefits of AI integration before proceeding with implementation.

Limited Use Cases with Proven ROI in CTRM

Despite theoretical propositions, the number of AI implementations in commodity trading that have demonstrated tangible returns on investment (ROI) is limited. Most AI initiatives in this sector are still experimental. Alexandra Ebert from MOSTLY.AI, speaking at EDAY, said there is a lack of conclusive evidence of their effectiveness in delivering measurable benefits in real-world  scenarios. Therefore, trading firms need to meticulously assess the relevance and viability of AI within their operational frameworks. This involves considering factors such as the specific needs of their trading operations, the potential risks, and the expected ROI. By conducting thorough evaluations, trading firms can make informed decisions regarding the integration of AI into their CTRM systems. Ms Ebert further presented statistics showing that 70% of all current AI projects fail.

Conclusion

While AI holds promise for enhancing various business processes, its integration into Commodity Trading and Risk Management (CTRM) systems requires careful consideration. The complexities associated with CTRM operations, including risk assessment, regulatory compliance, and logistical coordination, pose significant challenges. Additionally, ensuring data quality and availability remains a crucial concern, as inaccurate or incomplete data can undermine AI’s effectiveness.

Furthermore, regulatory compliance is a top priority in commodity trading, and any errors in AI systems could lead to non-compliance issues. This underscores the importance of thorough oversight and risk mitigation strategies when integrating AI into CTRM systems. Moreover, the high costs associated with AI implementation, including technology infrastructure and skilled personnel, can present barriers to adoption, particularly for smaller trading firms with limited resources.

At ComFin Software, we are well-versed in the intricacies of the commodity markets and offer comprehensive CTRM solutions that combine advanced technologies with proven techniques. For financial risk managers and commodity traders seeking pragmatic solutions, careful evaluation of AI’s applicability alongside platforms like the Comcore CTRM system offers a robust approach. Platforms like Comcore prioritise reliability, compliance, and user-centric design, addressing the specific needs of commodity trading operations. By leveraging proven methodologies and intuitive interfaces, these platforms empower trading firms to navigate volatile markets with confidence and precision, without the complexities and uncertainties associated with AI integration.

Contact us for further information or book a demo of the Comcore CTRM system.

Vienna, 24 May 2024