A STUDY ON AI IN AUDITING AND FINANCIAL COMPLIANCE: BENEFITS AND CHALLENGES – WITH REFERENCE TO BANGALORE NORTH, KOTHNUR
DOI:
https://doi.org/10.65009/td0xpz04Keywords:
Artificial Intelligence, Auditing, Financial Compliance, Bangalore North, Kothnur, Fraud Detection, Efficiency, Chi-Square Test, T-Test, AI Adoption,,Abstract
The integration of Artificial Intelligence (AI) in auditing and financial compliance is revolutionizing
the way audit firms and financial institutions operate, particularly in emerging urban hubs like
Bangalore North – Kothnur. This study explores the practical benefits and challenges associated with
the adoption of AI technologies—such as machine learning, data analytics, and natural language
processing—within local auditing practices. AI offers enhanced capabilities in fraud detection, real
time data monitoring, document verification, and risk assessment, significantly improving the
efficiency and accuracy of financial audits. However, challenges such as high implementation costs,
limited technical knowledge among auditors, concerns over data privacy, and the lack of regulatory
clarity pose barriers to full-scale adoption. To examine these factors, the study uses two statistical tools:
the Chi-Square Test to assess the relationship between AI awareness and adoption, and the T-Test to
compare efficiency perceptions between AI users and non-users. Data will be gathered from chartered
accountants, audit professionals, and compliance officers operating in Kothnur. The findings aim to
provide insights into the region’s readiness for AI adoption, helping firms, educators, and policymakers
create training frameworks and strategies for responsible AI implementation in the auditing domain.
References
Brynjolfsson, E., & McAfee, A. (2017). Machine, Platform, Crowd: Harnessing Our
Digital Future. W. W. Norton & Company.
Pannu, A. (2020). Artificial Intelligence and its impact on accounting and auditing.
International Journal of Business and Management Research, 10(1), 50–58.
PwC. (2022). AI in audit: Transforming trust and transparency.
PricewaterhouseCoopers. Retrieved from https://www.pwc.com
Vasarhelyi, M. A., Kogan, A., & Tuttle, B. M. (2015). Big Data in accounting: An
overview. Accounting Horizons, 29(2), 381–396. https://doi.org/10.2308/acch-51071
World Economic Forum. (2023). The Future of Jobs Report 2023: Technology and
Workforce Transformation. Geneva: WEF. Retrieved from https://www.weforum.org
Yoon, K., Hoogduin, L., & Zhang, L. (2015). Big Data as complementary audit
evidence. Accounting Horizons, 29(2), 431–438. https://doi.org/10.2308/acch-51076
Deloitte. (2021). The AI-enabled auditor: Rethinking audit processes for the digital era.
Deloitte Insights. Retrieved from https://www.deloitte.com
ICAI. (2022). Artificial Intelligence and Automation in the Accounting Profession. The
Institute of Chartered Accountants of India.
Brynjolfsson, E., Rock, D., & Syverson, C. (2021). The productivity J-curve: How
intangibles complement general-purpose technologies. American Economic Journal:
Macroeconomics, 13(1), 333–372. https://doi.org/10.1257/mac.20180013
KPMG. (2023). AI in Auditing and Assurance: Driving Quality through Innovation.
KPMG Global Report. Retrieved from https://home.kpmg
