FINANCIAL RISK ANALYSIS AND FRAUD DETECTION TRENDS IN BIG 4 CONSULTING FIRMS (2020-2025) A DATA-DRIVEN APPROACH

Tanvir Rahman, Akash and Leila, Esmaeili (2025) FINANCIAL RISK ANALYSIS AND FRAUD DETECTION TRENDS IN BIG 4 CONSULTING FIRMS (2020-2025) A DATA-DRIVEN APPROACH. Journal of Adaptive Learning Technologies, 2 (5). pp. 1-10. ISSN 2997-3902

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Abstract

This study investigates the transformative role of data analytics and Artificial Intelligence (AI) in enhancing fraud detection, risk oversight, and audit effectiveness within the Big 4 consulting firms—Deloitte, PwC, EY, and KPMG—during the period of 2020 to 2025. By examining audit volumes, compliance violations, sector-specific fraud trends, and the resulting financial impacts, the research offers deep insights into the technological evolution of financial auditing practices. The findings demonstrate that AI-assisted audits significantly increased fraud identification accuracy, reduced the frequency of high-risk cases, and strengthened compliance outcomes, especially when firms employed compliance intelligence (CI) tools over traditional audit methods. Sectoral analysis revealed that the Retail industry experienced the highest financial fraud losses, followed by Finance and Technology, while the Healthcare sector reported the fewest cases, suggesting more robust internal controls. The study also highlights how auditor workload correlates with their ability to detect complex anomalies, raising concerns over operational capacity. Among the firms, PwC and Deloitte led in fraud detection, whereas EY reported the lowest, pointing to potential differences in audit methodologies. The integration of AI tools enabled real-time analysis of transactional data, supporting enhanced transparency, audit integrity, and strategic decision-making. The research concludes that sustained investment in intelligent auditing systems and continuous professional development is essential for organizations to adapt to rapidly changing risk landscapes and tightening regulatory standards. Future studies should expand on these findings by incorporating broader datasets and evaluating the long-term financial and operational cost-effectiveness of AI-driven audit technologies in global markets.

Item Type:	Article
Subjects:	H Social Sciences > HG Finance
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	17 May 2025 05:20
Last Modified:	17 May 2025 05:20
URI:	http://eprints.umsida.ac.id/id/eprint/16083

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