Commodity trading deals with buying and selling various commodities and their derivatives like wheat, gold, silver, or crude oil. Commodity trading requires a document-intensive process deals with a pile of physical papers collected from various sources. The sources can be any reports, phone calls, chats, emails, or documents. With digitalization, the quantity of these unstructured data has increased enormously across various trading channels. The commodity trading regulations and compliance has made it mandatory to manage these data faster and accurately. Therefore, commodity trading companies are now obligated to adopt the automated mechanism to sift and develop insights from the data and streamline their operations to bring accuracy and efficiency.
For Commodity trading companies, it is imperative to record the performance on environmental, social, and governance (ESG) parameters. It is critical because big investors always scrutinize their records and initiatives taken by the management.
However, commodity data centers consume lots of electricity and require lots of water to keep cool. The environmental challenge of the data center was a big concern around the world. To reduce the usage of electricity and carbon footprint, the data centers are optimized with solar or other renewable sources of electricity. Even these environmental footprints are bringing green energy solutions and carbon capture initiatives. Similarly, supply chains follow more compliance protocols along with labor and environmental clearances to eliminate child labor and bring transparency across the suppliers, subcontractors, distributors, and service vendors’ ecosystem.
ESG (Environmental, Social, and Governance) Integration is the primary agenda for commodity trading companies and investors. Intelligent AI/ML platforms are specially designed to process large volumes of unstructured commodities data and provide insights to industries and traders. Cognitive technologies are used to automate data extraction and transform data into deliverable insights. The ESG integration into commodity industries helps extract numerous unstructured documents and curate them in a customized format for data analytics tools. These data provide end-to-end scalable data solutions to the stakeholders for a successful trading journey.
End-to-End Data Solutions indicates accelerating performance using AI and ML at all stages of the value chain and ingesting enormous data related to trading. A sustainable framework of end-to-end data solutions helps businesses to derive value from unstructured public data such as annual reports, images, or maps.
An end-to-end commodity data management life cycle is a key to customized ESG integration. To capture ESG multiple parameters, corporate-specific key performance indicators (KPIs) are consolidated into a meaningful ESG profile. Comprehensive and real-time insights into structured data from various websites provide deeper dives into commodity trading with the help of sophisticated AI/ML techniques.
The Al/ML-based approach converts structured and unstructured content from various data repositories and curates customized data for the investors. ESG integration is vital for creating metrics and reports for commodities companies and investors. To get a company’s ESG stable profile, continuous scanning of the ESG landscape is mandatory. AI/ML platforms and advanced technologies are compulsory to evolve the materiality landscape and get a clear view of diverse ESG matrices.
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