Posted on : April 4th 2022
Author : Sudhakaran Jampala
Artificial intelligence (AI) systems are machine-based systems, with varying levels of autonomy, that can make predictions, recommendations, or decisions based on a given set of human-defined objectives.¹ The deployment of AI in finance workflows is increasing rapidly. Many firms are witnessing improvements in efficiency through cost reduction and productivity enhancements. Also, the customer experience (CX) related to financial products and services -- reinforced by AI is showing a massive uptick.
With digitalization, the growth of data has been enormous and transformed how commodities’ trading functions. Earlier, commodities traders largely relied on the exclusive access to information to make investment decision. This was either due to direct control of physical assets (like mines, oil refineries, etc.) or access to information via a network of contacts or agents on the ground.² For trades to be decided, the phone was a handy item.
Now, data and cognitive technologies drive trading decisions and transactions. However, the volume and array of data generated are so vast that commodity traders find it challenging to manage and mine their unconnected data for insights.³ According to the analyst firm IDC, by 2025, the world will generate 163 zettabytes of data annually. For example, data coming from tracking ships in real-time is now routine. For commodities traders, this data and associated insights help make intelligent data-based decisions.⁴
Intelligent sensors, pervasive internet connections, and real-time monitoring make tracking and quantifying vast commodities data. Oil storage tanks are being watched by satellites, sensors track methane emissions, and the flows of oil pipelines are monitored ceaselessly with advanced scanning systems. Data is exploding and emanating throughout the commodities ecosystem.
But the operations part of commodities trading is still lagging. For example, it is still a highly document-intensive process for all companies in the value chain, such as traders, clearinghouses, brokers, and logistics providers. At each stage, many documents, spreadsheets, and call and customers records are generated.
At its essence, commodities trading is a series of reasoning procedures that use the data at hand for forecasting, which, before the digital and data explosion, was simpler and highly manual. For commodities trading, the main issue is that data are generally maintained in different formats across different storage systems.
AI and machine learning (ML), a subclass of AI, employ algorithms to detect patterns in data and decipher salient insights. AI/ML use in commodities trading currently revolves around using natural language processing (NLP) and ML to treat structured and unstructured data to create models forecasting commodity prices with minimal human involvement. NLP uses algorithms to interpret text, which helps in sentiment analysis across news articles, social media posts, emails, and the like, which traders use to study current events and predict market changes. All the data must be made meaningful.
In the new world of data overload, AI/ML is at the core of successful commodity traders’ efforts to extract meaningful market intelligence from raw data feeds to open new revenue streams and manage risks better.⁵
The process of data extraction involves identifying and recovering alternative and semi-structured data from various data sources such as files, XMLs, JSON, etc.
Capital markets are an excellent example of a perfect competition. The nature of the market is such the participants have to be competitive and result focussed. For instance, brokerages and investment banks have to deliver passive gains for their clients and, at the same time, earn a margin for themselves.
Today’s ESG analytics require processing data, patterns, and hidden connections to provide insights that investors, asset managers, and companies need. For example, Straive deploys advanced machine learning algorithms to analyze reams of documents to collect evidence across executive statements for signs of vagueness or obfuscation.
Talking about using data to gain insights is easy. But actually doing it will uncover a newer set of challenges, especially when it comes to unstructured data.
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.
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