What does this research mean for the field?

While generative artificial intelligence offers substantial potential for innovation and efficiency in financial markets, its unchecked diffusion risks exacerbating information asymmetries and systemic vulnerabilities, necessitating robust regulatory supervision. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The article explores how generative artificial intelligence influences finance, focusing on its advantages and challenges.

April 6, 2026Open Access

The Use of Artificial Neural Networks in Finance: Applications, Advantages, and Challenges

Key Points

The article explores how generative artificial intelligence influences finance, focusing on its advantages and challenges.
Review of existing literature on generative AI in finance.
Analysis of neural network models and their predictive capabilities.
Discussion of risks associated with algorithmic bias and model opacity.
Demonstrates enhanced decision-making and operational efficiency through advanced AI models.
Identifies risks such as algorithmic bias and model opacity that impact market transparency.
Highlights the need for regulatory supervision to address the challenges posed by generative AI in finance.

Abstract

Summary This article examines the implications of generative artificial intelligence (GAI) in the financial sector through the lens of information asymmetry, a structural issue in which certain economic actors benefit from a comparative advantage in data ownership and processing capabilities. Such asymmetries may undermine market efficiency and lead to suboptimal outcomes, as highlighted in the seminal work of Akerlof (1970) 1. The rapid expansion of neural network–based models in finance constitutes a major technological shift. Inspired by the cognitive functioning of the human brain, these models exhibit advanced learning and predictive capabilities that significantly outperform traditional statistical approaches. In particular, they enable the processing of massive volumes of complex, non-linear data and the identification of latent relationships that are critical for informed financial decision-making. Within this context, generative artificial intelligence emerges as a disruptive innovation with ambivalent effects. On the one hand, it facilitates the automation of trading strategies, real-time data analytics, weak signal detection, and the delivery of personalized financial recommendations, thereby enhancing decision accuracy, operational efficiency, and market responsiveness (Brynjolfsson and McAfee, 2014) 2. On the other hand, the deployment of GAI introduces significant risks related to algorithmic bias, model opacity, technical vulnerabilities, and insufficient human oversight. These risks are further amplified by the capacity of generative systems to produce false yet highly credible financial information, posing serious challenges to regulatory frameworks and threatening market transparency and integrity (Zuboff, 2019) 3. This study emphasizes the dual and paradoxical nature of generative artificial intelligence in financial markets. It argues that while GAI holds substantial potential for innovation and efficiency gains, its unchecked diffusion may exacerbate information asymmetries and systemic vulnerabilities. Consequently, the article underscores the necessity of robust regulatory supervision, ethical governance mechanisms, and enhanced institutional vigilance in order to mitigate abuses, preserve stakeholder trust, and safeguard the stability and integrity of financial markets.

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