AI in Commodity Finance: Transforming the Industry in 2025

As we step into 2025, the commodity finance industry is undergoing a profound transformation, fuelled by advancements in artificial intelligence (AI). From improving operational efficiency to enhancing risk management, due diligence, and regulatory compliance, AI is reshaping how industry players operate. Emerging AI trends and innovations are creating new opportunities while addressing long-standing inefficiencies, particularly in commodity finance due diligence. However, AI also poses unique business and regulatory risks in these use cases that call the companies developing and using AI to implement robust risk management frameworks to tackle these challenges. This week I have the privilege to co-author this article with Laura Kiviharju an experienced legal expert specialising in AI governance, data protection and cybersecurity in AI.

Emerging AI Trends in Commodity Finance

1. Specialised and Efficient LLMs Enter the Market

The emergence of smaller, task-specific Large Language Models (LLMs) is creating evolution in AI’s capabilities, offering unique solutions for commodity finance due diligence. These models are transforming data extraction, analysis, visualisation and risk detection processes by improving speed, accuracy, and scalability.

Key examples include:

• GPT-4o and GPT-4o Mini: Compact and highly efficient with improved accuracy, tailored for specific business functions.
• Gemini Ultra and Nano: Offering advanced and focused solutions for granular analysis.
• Claude 3 Series: Addressing complex operational needs with flexibility.

For commodity finance, these models enable faster counterparty, transaction and compliance assessments, real-time flagging of potential risks, and streamlined workflows for evaluating transaction data.

2. Driving Down AI API Costs

Cost optimisation has become a critical enabler for AI adoption in commodity finance. With techniques like batching requests, retrieval-augmented generation (RAG), and cascading models, firms can significantly reduce operational expenses:

• RAG for Due Diligence: By automating document analysis and extracting critical data upfront, RAG minimises costs while improving precision in counterparty and transaction evaluation.
• LeanContext: Reducing token usage in LLMs by up to 68%, allowing firms to handle large-scale transaction data at a fraction of the cost.

Above techniques as well as expected API price decreases due to increasing competition is making AI usage more affordable. These savings are reinvested into advanced AI capabilities, creating a competitive edge for firms adopting these strategies.

AI-Driven Due Diligence: Transforming Commodity Finance

Due diligence has long been a bottleneck in commodity finance, characterised by manual processes, inconsistent risk assessments, and missed opportunities. AI is now addressing these challenges head-on, making due diligence faster, more accurate, and cost-effective.

Challenges in Commodity Finance Due Diligence

• Data Silos: Disconnected systems lead to incomplete counterparty, transaction and compliance evaluation.
• Compliance Complexity: Regulatory frameworks vary widely, increasing the workload for compliance teams.
• Resource Intensive: Manual processes for data validation and fraud detection are time-consuming and error-prone.

AI Solutions for Due Diligence

AI-powered tools are revolutionising how firms approach due diligence by automating key processes and enabling deeper insights. Examples include:

1. AI-Powered Risk Flagging: Automatically identifying high-risk counterparties and transactions using predefined criteria and historical data patterns.
2. Automated Data Verification: Extracting, validating, and reconciling data from trade documents, invoices, and contracts and external data sources to reduce errors and ensure accuracy.
3. Predictive Analytics: Using scenario-based simulations to anticipate risks tied to market volatility, geopolitical events, and supply chain disruptions.

By reducing the time and cost of due diligence, firms can approve transactions faster, leading to increased trade completion rates and improved access to financing for underserved markets, which may even create social benefits by providing financing opportunities for smaller transactions for example in emerging markets that get currently often ignored due to the high due diligence costs.

AI in Commodity Trading and Risk Management

AI is not just enhancing due diligence; it is transforming trading, risk management, and supply chain optimisation.

Commodity Trading Applications

• Data Pre-Processing: Automating the cleaning and integration of large datasets for trading models.
• Market Forecasting: Advanced algorithms provide more accurate predictions of country risks, and commodity prices and trends.
• Portfolio Management: Real-time tracking of commodity mixes and their associated risks, enabling better allocation decisions.

Risk Management Enhancements

AI-driven Systems of Intelligence (SOIs) are playing a critical role in risk management by:

• Supply Chain Optimisation: Identifying efficient trade routes and mitigating potential delays or disruptions.
• Predictive Modeling: Enhancing hedging strategies through scenario-based simulations.
• Dynamic Compliance Monitoring: Continuously scanning for regulatory updates and automating compliance reporting.

These systems enhanced transparency, and improved forecasting accuracy.

AI and the Cost of Transformation

Game-Changing Cost Reductions

The cost of deploying generative AI has dropped dramatically—by over 60 times since 2020. Mid-sized firms that were once priced out of adopting advanced AI tools are now able to implement state-of-the-art solutions, making transformation accessible across the industry.

Accelerated Deployment Timelines

AI implementation timelines have been reduced from 12 months to as little as 12 weeks. This rapid deployment allows firms to respond to emerging opportunities and risks more effectively. For instance, a leading commodity trader and bank can implement an AI-based due diligence platform in weeks, halving the time taken to approve new transactions.

Diverging Strategies

The industry is seeing a clear split:

• AI Visionaries: Firms embracing AI for automated due diligence, predictive risk management, and cost optimisation, reporting up to a 15% boost in performance.
• AI Laggards: Organisations hesitant to adopt AI, facing reduced competitiveness and higher operational costs.

Navigating Risk Management, Regulatory and Compliance Challenges

As AI adoption accelerates, so does the complexity of risk management and regulatory compliance. Firms must navigate a shifting landscape, including:

• CFTC Guidelines: New frameworks for managing AI-related risks in commodity finance.
• NIST Guidelines: AI Risk Management Framework (AI RMF), AI RMF Generative Artificial Intelligence Profile and AI Safety Institute's upcoming extension of AI RMF for financial sector.
• UK PRA Requirements: Adherence to Model Risk Management Principles for AI-driven decision-making.
• EU AI Act: Requirements for high-risk AI (e.g. evaluation of creditworthiness of individuals), AI literacy requirements for all AI developers and deployers.
• EU ESMA Reports and Guidance: Artificial Intelligence in EU securities markets
• Swiss Financial Market Supervisory Authority FINMA: Guidance on governance and risk management when using artificial intelligence

AI solutions tailored for commodity finance, such as automated compliance workflows and real-time regulatory updates, are helping firms stay ahead of these evolving requirements.

AI Governance

At the same time the use of AI exposes companies developing and using AI to new risks and it is critical to adapt the existing risk management programs to cover AI-specific risks especially for preventing misuse of AI to engage in fraud or misconduct, ensuring high level of data accuracy and explainability of the decisions and recommendations, managing biases in algorithms and protecting the data as datasets used in due diligence process include business sensitive and KYC data. To achieve these goals companies should consider good practices in the AI development, including:

1. AI governance and oversight: roles and responsibility, company guidelines and standards for trustworthy AI development, AI integrated to the risk management, data governance, human oversight, testing and AI incident management.

2. Model risk management: core modelling process, model validation and model risk controls.

3. Documentation and Instructions: inventory of AI use cases, data collection and selection, and technical documentation for correct use of AI tools, AI model explainability

4. Measurement: approaches and metrics for measuring AI, qualitative or quantitative AI system performance criteria, monitoring AI risk incl. security and data protection

5. Management of AI system: post-deployment AI system monitoring, continuous improvement based on feedback and incidents

The Path Forward: AI as a Strategic Imperative

AI is now a strategic imperative for firms striving to lead the commodity finance industry into 2025 and beyond, and Efides AG is at the forefront of this AI-driven transformation. Efides empowers clients to address key industry priorities, including:

1. Investing in Infrastructure and Talent: Efides helps organisations build the expertise and systems necessary to effectively harness AI, while fostering AI literacy across all levels of the business.
2. Developing AI-Driven Strategies: By aligning AI solutions with business objectives, Efides enables firms to maximise value creation and stay ahead of the competition.
3. Fostering a Culture of Innovation: Efides supports a mindset of adaptability and continuous learning, ensuring organisations are equipped to thrive in a rapidly changing environment.
4. Maintaining Regulatory Vigilance: Efides assists clients in proactively navigating evolving compliance frameworks, ensuring seamless adherence to regulatory requirements.
5. Managing AI Risks: By adapting existing risk management frameworks to address AI-specific challenges, Efides ensures organizations mitigate potential risks effectively.

Through Efides' expertise and solutions, firms can holistically embrace AI, unlocking unprecedented efficiencies, streamlining due diligence processes, and enhancing their resilience in an increasingly volatile and competitive market.

Conclusion

AI is revolutionising commodity finance, offering tools that address inefficiencies in due diligence, mitigate risks, and enhance overall operational effectiveness. Those who adapt quickly to these advancements will not only survive but thrive, leading the industry into a new era of innovation and growth. Successful integration of AI requires also robust risk management and responsible AI practices to prevent and mitigate potential harmful effects.

In 2025, the future of commodity finance is being defined by those who harness AI’s transformative power responsibly —and those left behind. For firms willing to invest in AI, the rewards will be substantial, reshaping the landscape of commodity trade financing.

Efides.io Laura Kiviharju Dr. Ari Aaltonen World Trade Organization ICC United Kingdom World Economic Forum #AI #CommodityFinance #Innovation #RiskManagement #EfidesAG #ArtificialIntelligence #FutureOfFinance #AIGovernance #technology #Fintech #commodity #tradefinance