Financial Modeling in Corporate Strategy: A Review of AI Applications For Investment Optimization

ABSTRACT : This review paper examines the integration of artificial intelligence (AI) into financial modeling and its implications for corporate strategy, with a particular focus on investment optimization. It explores various AI techniques, including machine learning, neural networks, and deep learning, and their transformative impact on traditional financial modeling practices. Key findings highlight the enhanced predictive accuracy, improved risk management, and personalized financial services that AI can provide. However, the integration of AI also poses significant challenges, such as data quality issues, algorithmic bias, and regulatory uncertainty. The review identifies critical opportunities for organizations to leverage AI-driven insights while addressing ethical considerations and promoting transparency. Ultimately, the paper underscores the importance of adopting AI in financial modeling to gain a competitive advantage in an increasingly data-driven financial landscape, while also advocating for responsible practices in AI deployment.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-08
DOI
https://doi.org/10.5281/zenodo.15016829
Primary Topic
Genomic variations and chromosomal abnormalities
Type
article
Field-Weighted Citation Impact
0.00
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article

Financial Modeling in Corporate Strategy: A Review of AI Applications For Investment Optimization

Zenodo (CERN European Organization for Nuclear Research)
Genomic variations and chromosomal abnormalities
article

Financial Modeling in Corporate Strategy: A Review of AI Applications For Investment Optimization

article en

Abstract

ABSTRACT : This review paper examines the integration of artificial intelligence (AI) into financial modeling and its implications for corporate strategy, with a particular focus on investment optimization. It explores various AI techniques, including machine learning, neural networks, and deep learning, and their transformative impact on traditional financial modeling practices. Key findings highlight the enhanced predictive accuracy, improved risk management, and personalized financial services that AI can provide. However, the integration of AI also poses significant challenges, such as data quality issues, algorithmic bias, and regulatory uncertainty. The review identifies critical opportunities for organizations to leverage AI-driven insights while addressing ethical considerations and promoting transparency. Ultimately, the paper underscores the importance of adopting AI in financial modeling to gain a competitive advantage in an increasingly data-driven financial landscape, while also advocating for responsible practices in AI deployment.

Zenodo (CERN European Organization for Nuclear Research)
Osun State University (NG), Film Independent (US)
Openalex Percentile: Top 51%
Genomic variations and chromosomal abnormalities
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Financial Modeling in Corporate Strategy: A Review of AI Applications For Investment Optimization · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS