Accounting Fraud Detection Using Artificial Intelligence: Evidence from Indian Listed Companies

Abstract

Accounting fraud is a big problem that affects companies, investors, and the overall economy, particularly in Indian listed companies where money transactions are now more complex and done by online. If companies give wrong financial information, report incorrect income, and hide their liabilities it misleads stakeholders and reduce trust in financial systems. This research highlights on how artificial intelligence (AI) can make fraud detection in accounting faster and with better accuracy. Conventional fraud detection methods mainly depend on manual review and simple auditing techniques, which take a lot of time and may not to be determine hidden or sophisticated frauds. Compared to older methods, AI tools like machine learning, data mining, and predictive analytics can quickly study large amount of financial data and spot unusual patterns, trends, and irregularities that may suggest fraud. This system can also learn from previous information and become more accuracy over time. The study examines on company data from selected Indian listed companies to examine the effectiveness of AI-based models can dete

Introduction

The text discusses how accounting fraud is a growing issue that reduces financial transparency and weakens trust in capital markets, especially in India. Traditional audit and fraud detection methods are becoming ineffective due to the increasing complexity and volume of financial data.

It highlights the role of Artificial Intelligence (AI) as a powerful solution for detecting fraud. AI techniques such as machine learning, neural networks, data mining, and natural language processing can analyze large datasets, identify unusual patterns, and adapt to new fraud trends more effectively than conventional methods. Various AI-based tools are already being used in the financial and banking sectors to enhance fraud prevention.

The study aims to evaluate how AI can detect accounting fraud in Indian listed companies, compare its effectiveness with traditional methods, and analyze fraud patterns using financial and textual data (including sentiment analysis). It also examines the implications for auditors, management, and regulators.

The research is based on secondary data from major Indian companies over the period 2021–2025, using financial metrics like turnover, profit, and ratios. Results show that all selected companies experienced steady growth in turnover, indicating strong financial performance, although some calculated growth rates appear inconsistent with the trend. Overall, the study emphasizes that AI-driven fraud detection can improve accuracy, efficiency, and decision-making in financial systems.

Conclusion

The financial analysis of selected leading Indian companies during 2021–2025 reveals strong overall growth in turnover and profitability across sectors. Companies like Reliance Industries Ltd and Life Insurance Corporation of India maintained the highest revenue levels, demonstrating market dominance. The banking sector, particularly HDFC Bank and State Bank of India, showed significant improvement in profits and EPS, indicating strong financial recovery and operational efficiency. IT companies such as Tata Consultancy Services and Infosys demonstrated stable growth with consistent earnings, reflecting resilience in global digital demand. Bharti Airtel exhibited a remarkable turnaround from losses to strong profitability, highlighting improved financial restructuring. Debt–Equity analysis shows that banking institutions maintain higher leverage as part of their operational model, while IT and FMCG companies maintain lower and stable debt levels, indicating strong internal financing capacity. Overall, the study concludes that the selected companies experienced financial strengthening during the period, with improved shareholder returns and operational growth. However, certain inconsistencies in calculated growth rates suggest the need for recalculation and validation to ensure analytical accuracy.

References

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Copyright

Copyright © 2026 C. K. Gowri Priya, Abhishek D, Sivani P, Likhith D, A. Chiranjeevi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.