Author(s) :   Author(s):  Aishwerya¹, Priyanka Yadav

Introduction : A conference held at Dartmouth University in 1956 saw the formal proposal of the term "artificial intelligence." That instance marked the beginning of a new field of study that examines how machines mimic intelligent human behavior. Machine learning (ML) refers to intelligent systems that can modify their behavior during the system-training phase in response to newly supplied information, whereas artificial intelligence (AI) refers to the concept of giving algorithms the capacity to carry out tasks and draw conclusions that would require an intelligent human in the same position (Lowe et al., 2022;Korteling et al., 2021). Finance and investment decision-making are among the many fields that have seen substantial change as a result of the quick development of artificial intelligence (AI) and machine learning (ML)(Prasad & Seetharaman, 2021;Sun et al., 2020). Because financial markets are so dynamic, intricate, and impacted by so many variables, traditional analytical techniques are becoming less and less suitable for precise risk assessment and forecasting. In financial decision-making, artificial intelligence (AI) and machine learning (ML) have become potent instruments that use large datasets, sophisticated computational methods, and predictive analytics to improve investment strategies.

DOI : 10.61161/ijarcsms.v13i4.5

Pages : 27-35

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