AI in Business and Finance: A Micro-to-Macro Perspective on Its Impact

Authors

  • Ayesha Shehzadi National University of Sciences and Technology (NUST), Islamabad, Pakistan
  • Muhammad Sameer Amjad National University of Sciences and Technology (NUST), Islamabad, Pakistan
  • Muhammad Nabeel Anwar National University of Sciences and Technology (NUST), Islamabad, Pakistan https://orcid.org/0009-0001-4841-788X
  • Fahad Ahmad Satti National University of Sciences and Technology (NUST), Islamabad, Pakistan https://orcid.org/0000-0002-9883-3355
  • Muhammad Moazam Fraz National University of Sciences and Technology (NUST), Islamabad, Pakistan https://orcid.org/0000-0003-0495-463X

DOI:

https://doi.org/10.14738/abr.1210.17781

Keywords:

Artificial Intelligence, Business, Finance, Accounting, Natural Language Processing, Algorithmic trading, Risk assessment

Abstract

This comprehensive survey examines the transformative impact of machine learning (ML), deep learning (DL), and natural language processing (NLP) across diverse business and finance sectors. From micro-level operational efficiencies to macro-level strategic decision-making, we delve into the multifaceted applications of AI and its influence on traditional practices. Our analysis highlights the increasing prominence of ML in enhancing decision-making and achieving greater accuracy and efficiency. We also identify key challenges such as data quality, modelinterpretability, and linguistic information processing. By addressing these challenges, we can further unlock the potential of ML in revolutionizing business and finance. We conducted a systematic literature review using the PRISMA technique to identify relevant research published within the last five years. Our findings reveal the growing adoption of ML techniques in various business and finance applications, such as fraud detection, risk assessment, customer relationship management, and algorithmic trading. We identified relevant publications through comprehensive searches in leading academic databases, including Scopus, Web of Science, and Google Scholar. We also discuss the challenges associated with implementing ML models in these domains, including data quality issues, model interpretability, and ethical considerations. Byunderstanding the applications, challenges, and opportunities associated with AI, businesses, policymakers, researchers, and investors can make informed decisions and leverage AI to drive innovation and growth in the business and finance sectors.

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Published

2024-10-18

How to Cite

Shehzadi, A., Amjad, M. S., Anwar, M. N., Satti, F. A., & Fraz, M. M. (2024). AI in Business and Finance: A Micro-to-Macro Perspective on Its Impact. Archives of Business Research, 12(10), 24–69. https://doi.org/10.14738/abr.1210.17781