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
Regulators want LIBOR to phased out by December 2021, banks and financial institutes must pivot to risk-free alternative rates.
We have been recognized among the “Top 20 Most Promising Big Data Solution Providers – 2020” in a recent listing by a leading global print magazine. The aforementioned list recognizes an exclusive set of solution providers with a proven track record of consistently delivering customer goals.
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Enterprises tend to employ data from external sources in their data strategy to convert insights into financial gain as they mature in their data journey. This external data comes in diverse forms. However, for enterprises, the most critical is public data.
There are currently no compliance mandate around ESG reporting, especially for private companies, and such reporting is voluntary. While many large companies report on ESG as part of CSR, growing awareness among investors and consumers about ESG has led to this becoming a more widespread practice.
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