Artificial Intelligence Applications in Corporate Finance

Abstract

The research titled "Artificial Intelligence Applications in Corporate Finance," examines the revolutionary integration of artificial intelligence (AI) into financial modeling, with a special emphasis on enhancing investment strategies and its wider effects on business finance. The study comprehensively examines various AI methods, such as machine learning, neural networks, and deep learning, as well as their revolutionary impact on traditional financial modeling methods.

According to the research, AI greatly increases predictive accuracy, improves risk management strategies, and makes it easier to provide customized financial services (Behera et al., 2023). Financial models may identify intricate trends and patterns that are frequently imperceptible by traditional methods by utilizing AI to process and analyze huge datasets, hence enhancing strategic decision-making and investment results.

In spite of these obstacles, the study identifies important prospects, such as progress in dynamic risk management, predictive analytics, and the possibility of providing customized financial advice using AI-driven robo advisors. The introduction of Explainable AI (XAI) is portrayed as a vital step toward promoting openness and confidence in decisions made by AI, fostering a collaborative synergy between human knowledge and artificial intelligence de (Bruijn et al., 2022; Durán & Jongsma, 2021). The thesis concludes by emphasizing the necessity for financial modeling to incorporate AI in order to remain competitive in a financial market that is becoming more and more data-driven, calling for the ethical use of AI and ongoing adaptation to changing technological and regulatory contexts.