Machine Learning in Accounting & Finance: Architecture, Scope & Challenges

  •  Ishan S. Kapoor    
  •  Sunint Bindra    
  •  Monika Bhatia    


Purpose: This paper discusses and presents the importance, scope, and limitations of machine learning in the area of financial decision-making. The purpose of the study is to find out the areas of application of machine/deep learning in the accounting and finance domain and also to identify challenges in adoption.

Design/methodology/approach: The current study is qualitative review-based research, where the systematic approach to reviewing the existing body of literature has been used. This article employs a thoughtful literature review of selected articles in identified journals that were subsequently evaluated through desktop analysis. All papers were selected based on the search in Google scholar. To enhance the quality of research, a scholarly filtration technique was employed. Only papers listed and accepted by the academia were shortlisted. The second criteria were to identify the keywords in the area of interest. The final step included only papers listed in established databases like Google Scholar, SCOPUS & ABDC.

Findings: The findings of the study indicate the importance of machine learning in financial decision-making and prediction. Advanced mathematical models such as unsupervised machine learning techniques have become the need of the hour to model complex non-linear relationships in financial systems, where complex business situations are resulting in the generation of 'Big Data' and 'Alternate Data'. However, there are many challenges in applying ML/DL models in these prediction models especially when the modeling in finance involves behavioral aspects of extremely dynamic customers and markets. The findings further indicate major research trends associated with machine learning in accounting and finance.

Originality/value: This is a novel study in the area of accounting and financial research, which requires considerable attention for interdisciplinary research.

This work is licensed under a Creative Commons Attribution 4.0 License.