How Artificial Intelligence & Machine Learning Are Transforming Commodities Data Operations for Better ESG (Environmental, Social, and Governance) Integration

The New Game

Commodity companies face many environmental, social, and governance (ESG) requirements from sustainability, supply chain compliance, and stakeholder activism perspectives. Environmental challenges are pushing green energy solutions and carbon capture initiatives. Supply chains are following more compliance protocols to eradicate hidden evils, such as child labor. Shareholders and civil society actors (e.g., non-governmental organizations) are calling for and establishing greater scrutiny. ESG integration is now a big agenda item for commodities companies and traders.

The primary data solutions strategy commodities sector companies use is extracting ESG data from disclosures. While quantitative metrics are easy to track and similar across strategies, qualitative data points vary depending on multiple factors. Also, extracted data need to fit into an ESG framework, such as Sustainability Accounting Standards Board (SASB) and the Task Force on Climate-related Financial Disclosures (TCFD), or a custom framework based on the overall ESG strategy.

Other challenges of extracting core ESG data from disclosures—removing analyst bias, timely tracking of multiple published sources throughout the year, and transforming data to meet enterprise ESG frameworks—require flexible customization.

Infusion in Place

At various levels, the commodities-related ecosystem is seeing an influx of the latest cognitive technologies. For example, the value chain in commodities trading is a document-intensive process involving traders, clearinghouses, brokers, logistics providers, investment banks, underwriters, asset managers, and others.

In the commodities-trading life cycle, all stages generate many documents, emails, spreadsheets, messaging platforms, images, videos, and call records. Latest technologies, such as end-to-end artificial intelligence (AI) and machine learning (ML), can process large volumes of unstructured commodities-connected data to provide insights to companies and traders.

Exhibit 1: Commodities-Trading Life Cycle

Source: Straive

Intelligent AI/ML platforms like the Straive Data Platform (SDP) are built specifically for treating and extracting unstructured data, including text-based or scanned documents, audio files, and videos, through automated processes.

Exhibit 2: Automated Processes—SDP

Source: Straive

Using cognitive technologies to automate data extraction and enrich and transform data to deliver actionable insights greatly benefits commodities companies regarding automation (e.g., reducing expensive manual processes), scale, and turnaround time. Commodities companies are better placed for ESG integration when data can be extracted efficiently from numerous unstructured documents and curated in a customized format for data analytics tools to consume. Having unstructured data in a straightforward, easy-to-understand format, a user can detect the patterns and trends without manually trawling for those data.

Exhibit 3: Emerging Data Treatment Value Chain for Commodities Companies

Source: Straive

Integrating ESG data into commodities trading operations requires structured, easy-to-consume data. By their nature, ESG data resist such integration, and highly scalable data solutions across the data life cycle are needed to allow stakeholders to deploy end-to-end data solutions for a successful data-to-intelligence journey.

What Are End-to-End Data Solutions?

In trading, end-to-end AI/ML coverage means using AI and ML to accelerate performance at all stages of the value chain, allowing ingesting massive amounts of data critical to trading. Businesses need data solutions that derive value from unstructured data, such as text from PDFs, invoices, and Word documents; public data, such as annual reports; or visual, such as images and maps. An end-to-end data management life cycle is a key to a customized ESG integration.

Exhibit 4: Visualizing the End-to-End Approach

Source: Straive

A sustainability framework helps collect and report ESG integration, opportunities, and performance. Intelligent data platforms need to constantly capture multiple parameters (e.g., CO2 emissions, natural resources management, labor practices, etc.) and company-specific public information from diverse sources in various formats.

Corporate-specific key performance indicators (KPIs) are needed for ESG parameters, which can then be consolidated into a meaningful ESG profile. An end-to-end data solutions approach means that each generated KPI-themed ESG profile is fully explainable using core drivers. The voluminous rise of ESG-related unstructured data means quickly implementing AI/ML capabilities at scale to obtain intuitively sensible results.

A Compliance Partner

The sheer volume of available unstructured ESG data underscores the importance of continuous real-time monitoring of external and enterprise data to identify compliance lapses or risk management concerns central to developing early warning systems. Commodities companies spend top money on consulting firms to assess and measure compliance.

However, today’s stakeholders don’t expect and won’t accept a report using historical data from internal sources; it doesn’t help with strategic decision-making. For tricky compliance and ESG questions like “Where am I benchmarked against competitors regarding compliance success and costs?” -- no easy off-the-shelf answer exists. Comprehensive and real-time insights and data from the various web sources and unstructured documents scattered across the digital universe can provide deeper dives but require sophisticated AI/ML techniques.

For ESG and compliance preparedness, an AI/ML-based approach that deciphers both structured and unstructured content (text, image, voice) from different data repositories and curates analytics-ready data sets is needed. Identifying and reconciling compliance needs and mismatches are complex and pressure-filled tasks. But now, with highly conscious stakeholders, the margin for error is increasingly low.

Exhibit 5: Optimizing Compliance and ESG Decision-Making

Source: Straive

Adopting AI/ML platforms like the SDP will increasingly differentiate compliance champions and laggards among commodities companies. Those who fall behind may experience negative effects on various enterprise needs, such as accessing new investors or capital bases, attracting talented human resources, and receiving positive government responses.

Conclusion

For commodities companies and related stakeholders, ESG integration is vital for building materiality assessments—intelligence, metrics, and reports. Central to determining the materially significant from the peripheral, this approach makes effective targeted and agile responses possible. Continuously scanning the ESG landscape is mandatory for a stable view of a company’s ESG profile, and AI/ML platforms and technologies are inseparable from this evolving materiality landscape for a clear view of diverse ESG matrices.