ABSTRACT

In recent years, the accounting profession has undergone a transformative revolution with the help of rapid advancement in Deep Learning (DL) technologies. Deep Learning, a subset of Artificial Intelligence (AI) has emerged as a powerful tool in the world of finance and accounting, offering unprecedented opportunities for automating tasks, improving accuracy and uncovering valuable insights. This groundbreaking technology has found a multitude of applications in accounting, ranging from automating repetitive data entry tasks to enhancing fraud detection, financial forecasting and risk management. Deep Learning driven by Artificial Intelligence Neural Networks (AI-NN), Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN) and more is reshaping the landscape of financial analysis, auditing, prevention of frauds, risk management and much more. Through these advancements, we gain not only efficiency but also a new perspective on the role of accountants in an increasingly data-driven and digitally interconnected world. 

Keywords: Deep Learning, artificial intelligence, RNN, CNN, AI-NN, accounting.

IMPACT STATEMENT

In the past year, the accounting profession has witnessed a transformative shift propelled by recent advances and applications of deep learning. The integration of sophisticated algorithms and neural networks has significantly enhanced data processing, analysis, and decision-making within the financial landscape. Deep learning models excel in automating routine tasks, reducing errors, and extracting valuable insights from vast datasets, thereby optimizing operational efficiency. This technological leap empowers accountants to focus on strategic, value-added activities while fostering a more accurate and agile financial ecosystem. The increased reliance on deep learning in auditing, fraud detection, and financial forecasting has not only elevated the profession’s precision but has also opened new avenues for strategic advisory roles. As the accounting, landscape continues to evolve, the profound impact of deep learning on efficiency, accuracy, and strategic positioning marks a groundbreaking milestone in the industry’s evolution.

About Author

VENKATASUBRAMANIAN GANAPATHY

Objective: Career Growth in the teaching profession with academic excellence and pursuit of further

Qualifications and research.

Academic Profile:

 M.Phil (Commerce) from The Quaide Milleth College for Men- University of Madras-

Ist Class- (2015-2016).

 B.Ed – IGNOU, New Delhi- Distance Mode- 1 st Class – December 2009.

 M.Com – Annamalai University – Distance Mode- 1 st Class – May 1995.

 D.P.C.S. (Data Preparation and Computer Software) – NCVT Course –Sri Ramakrishna

Mission Computer Centre, Chennai – 1 st Class – April 1993.

 B.Com – St. Joseph’s College (Autonomous), Trichy – Bharathidasan University – 1 st

Class – April 1990.

 ICWA Inter (Group II) – December 1993.

Academic Experience: 18 + Years

Presently working as a Visiting Faculty in the SIRC of ICAI, Nungambakkam, Chennai,

Tamil Nadu, Bharat

Corporate Experience: 9 Years.

Cite this Article

APA (7th ed.): Ganapathy, V. (2024). Recent Advances And Applications Of Deep Learning (DL) In The Accounting Profession. Edumania-An International Multidisciplinary Journal, 2(1), 77–104. https://doi.org/10.59231/edumania/9020

Chicago (17th ed.): Ganapathy, Venkatasubramanian. “Recent Advances And Applications Of Deep Learning (DL) In The Accounting Profession.” Edumania-An International Multidisciplinary Journal 2, no. 1 (2024): 77–104. https://doi.org/10.59231/edumania/9020.

MLA (9th ed.): Ganapathy, Venkatasubramanian. “Recent Advances And Applications Of Deep Learning (DL) In The Accounting Profession.” Edumania-An International Multidisciplinary Journal, vol. 2, no. 1, 2024, pp. 77–104. https://doi.org/10.59231/edumania/9020.

Statements & Declarations

Peer Review: The scholarly quality and contribution of this comprehensive review on deep learning applications in accounting have been confirmed through a rigorous and independent peer-review process conducted by experts in the relevant fields.

Review Type: This article underwent a double-blind peer review, wherein the identities of the author (Venkatasubramanian Ganapathy) and the reviewers were concealed from each other. The review was conducted by subject experts in artificial intelligence, accounting information systems, financial technology, and deep learning applications.

Competing Interests: The author, Venkatasubramanian Ganapathy, declares that there are no financial, professional, or personal competing interests that could be perceived to have biased the work presented in this manuscript.

Data Availability: This manuscript is a review paper synthesizing recent advances and applications of deep learning in the accounting profession. It does not present primary empirical research data. All sources, including technical papers, industry reports, and scholarly works, are cited within the article.

Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The work was completed as part of the author’s professional role at the Southern India Regional Council of the Institute of Chartered Accountants of India (SIRC of ICAI) and personal scholarly effort.

License: This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND) 4.0 International License. This license allows others to download this work and share it with others for non-commercial purposes, as long as they credit the author, but they cannot change it in any way or use it commercially.

Ethical Approval: Ethical approval was not required for this study, as it is a review paper based on publicly available technical and academic literature and did not involve the collection of data from, or experimentation on, human subjects or animals. The work adheres to the highest standards of academic integrity and professional ethics in accounting and technology research.

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