[vc_row][vc_column][vc_column_text]Commodity businesses generate massive data. However, several traders fail to fully understand its true potential and utilise advanced analytics to their benefit. In fact, failure to do so could contribute towards poor decision making, inaccurate analysis and forecasting, or even trading errors. This can be avoided through effective use of Artificial Intelligence and Machine Learning because they hold the potential to push crucial real-time information across the business functions with high efficiency and value.
While early adopters of such advanced technology are already yielding its benefits, it’s never too late to kickstart and keep pace with them. Investing in cutting-edge ERP solutions is like finding the key to success. It can contribute to short-term and long-term growth of your commodity business.
Practical Applications of ArtificiaI Intelligence and Machine Learning in Commodity Trade:
1) Forecasting Price: Adopting forecasting algorithms can help predict prices over the span of a single year or upcoming years. While manually aggregating research, analysing market sentiment and historical data can take several days and manhours; Artificial Intelligence can offer valuable information in just an hour or two. Additionally, Artificial Intelligence can provide more accuracy and tactful thinking – unlike manual calculations that could increase the risk of trading errors. This coupled with machine learning can enable your ERP to test and learn from previous results for improved insights.
2) Risk Management: An efficient analytical tool can even complement time-consuming calculations essential to estimate the risk of a trading portfolio based on the overall positions. For instance, VaR estimations for wide portfolio consisting of a huge set of positions can take countless hours. With Machine Learning, commodity traders can receive a quick overview of the portfolio’s risk factors in minutes. Thus, allowing managers to focus on the strategic aspect of the business while automation takes care of the latter.
3) Asset and Derivatives Valuation: Artificial Intelligence and Machine Learning’s success in forecasting prices and risks has made commodity traders realise its potential for managing asset and derivatives valuation as well. While calculating price and risk often analyse industry-wise data, asset and derivatives valuation is solely based on organisational data. Thus, with the support of an efficient IT team and experienced ERP providers, businesses can consider building and training machine learning algorithms in alignment with their business goals.
4) Compliance and data integrity: Artificial Intelligence and Machine Learning can help maximise one’s efforts to ensure regulatory compliance is met by their business. For instance, frauds, repetitive errors, and other mistakes can be detected at prior stages. In the case of a potential fraud or error, these systems can even investigate for any intentional interference with data integrity. With such advanced fraud detection tools, commodity traders can secure their assets and prevent data breaches.
Robo-Commodity, our advanced ERP for Commodity Management has been helping commodity traders across industries experience the power of advanced analytics and improve their ROI. Need advice for your business? We can set up a Free Consultation with your team!
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