Using AI Technology to Enhance Data‐Driven Decision‐Making in the Financial Sector

Abstract

The finance industry has seen a rapid transformation with the emergence of artificial intelligence (AI), which makes data analysis, modeling, and decision-making more effective and efficient. Artificial intelligence technology allows the financial sector to process massive datasets, identify complex patterns, and generate predictive insights that previously were unattainable through traditional analytics methods. This chapter begins with an overview of AI technology widely used in the financial industry. Three case studies in the agricultural sector, the new energy sector, and the commodity sector are presented, which discusses how AI technology helps farmers, entrepreneurs, marketers, and other financial practitioners. The first case examines the effects of remote-sensing images to forecast crop price trends. The second case study analyzes the relationship between lithium carbonate & industrial silicon future prices and macroeconomic factors utilizing the long short-term memory (LSTM) model to forecast future prices. The third case study introduces commercial software for commodity supply chains which is useful to predict price, calculation of options, and intelligent strategy recommendations to enhance the quality of financial services. The chapter presents how AI plays a critical role in shaping the future of financial technology and innovation which is in line with SDG Goal 9.

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