Exhibit 1: Commodities-Trading Life Cycle 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. In the commodities-trading life cycle, all stages generate many documents, emails, spreadsheets, messaging platforms, images, videos, and call records. 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. Infusion in PlaceДЄt various levels, the commodities-related ecosystem is seeing an influx of the latest cognitive technologies. 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. 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. While quantitative metrics are easy to track and similar across strategies, qualitative data points vary depending on multiple factors. The primary data solutions strategy commodities sector companies use is extracting ESG data from disclosures.

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

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