Algorithmic Finance: A Literature Review on the Usage and Impact of Artificial Intelligence in Financial Management

Abstract

This study provides a rigorous systematic literature review examining the integration, usage, and implications of Artificial Intelligence (AI) within Financial Management. As modern corporate finance and banking sectors navigate escalating market volatility and massive digital data streams, conventional financial analysis workflows are rapidly evolving into automated algorithmic forecasting, algorithmic trading, real-time fraud detection, and predictive credit scoring. By synthesizing contemporary peer-reviewed literature and applying established financial and economic theories—such as Efficient Market Hypothesis (EMH) modifications, Information Asymmetry Theory, and Agency Theory—this paper maps how machine learning architecture transforms corporate fiscal control and investment strategies. The synthesis demonstrates that while AI applications substantially enhance forecasting accuracy, operational speed, and risk mitigation efficiency, they concurrently introduce critical systemic concerns, including black-box opacity, algorithmic market manipulation risks, data privacy vulnerabilities, and model risk. Finally, this review highlights prominent research gaps and delivers strategic recommendations for financial managers, corporate treasurers, and regulatory policymakers navigating intelligent financial ecosystems.