Study details how Artificial Intelligence is creating

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One of the points addressed is the use of algorithms to analyze large volumes of data in real time, allowing investors to make decisions based on more detailed and accurate information.

The use of artificial intelligence (AI) in the financial sector has profoundly transformed investment strategies, risk management and corporate finance. This is the central theme of the article “Artificial Intelligence: The Vanguard of Finance”, published in the July 2023 issue of GV Executivo Magazine, written by Claudia Emiko Yoshinaga , professor at FGV EAESP and coordinator of the Center for Finance Studies (FGVcef), and F. Henrique Castro , professor at the São Paulo School of Economics (FGV EESP).

The study explores how AI is applied in the world of finance and the impacts of this technology on the sector. One of the points addressed is the use of algorithms to analyze large volumes of data in real time, allowing investors to make decisions based on more detailed and accurate vp financial email database information. “AI offers advantages in scalability and diversification, processes large amounts of data and helps in efficient portfolio management,” explain the authors.

In addition to improving financial decision-making, AI is also widely used in investment strategies, especially algorithmic trading. This type of trading uses algorithms to automatically execute transactions based on market conditions and real-time data, allowing operations to be completed in milliseconds. The article highlights that this practice is redefining the way the market operates, favoring large investors who have access to more advanced technologies.

However, the authors warn of the risks that come with using AI in the financial sector. One of the main challenges mentioned is the reliability of the data used by the algorithms. Inaccurate or biased data can lead to incorrect investment decisions, resulting in losses. Furthermore, reliance on historical data may not reflect future market conditions, limiting the effectiveness of AI models.

Another risk addressed is the use of complex machine learning and deep learning models, which operate as “black boxes”, meaning it is difficult to understand how they reach their conclusions. This raises ethical and regulatory issues, as the lack of transparency can make it difficult to identify potential biases or errors in automated decision-making processes.

The article also discusses the role of AI in risk management. The technology is being used to improve risk assessment in corporate finance, enabling the analysis of large amounts of data and the identification of fraud or security breaches more efficiently. In addition, AI is being applied in Natural Language Processing (NLP) tools, which analyze news and market sentiment to predict economic impacts.