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@article{192794,
        author = {Arafat Dhanse and Dr. Reshma Jaisinghani},
        title = {Perception and Effectiveness of Forensic Auditing Techniques in Detecting Corporate Fraud: A Study among Finance Professionals},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {2385-2390},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192794},
        abstract = {Corporate fraud has emerged as a significant challenge for organizations worldwide, resulting in substantial financial losses and erosion of stakeholder trust. Traditional auditing mechanisms, though essential for regulatory compliance, often fail to detect sophisticated and well-concealed frauds. In this context, forensic auditing has gained prominence as a specialized tool that combines accounting, investigative, and legal skills to identify and prevent fraudulent activities. The present study examines the perception and effectiveness of forensic auditing techniques in detecting corporate fraud, focusing on finance professionals in the Indian corporate environment. Primary data were collected through a structured questionnaire administered to 100 finance professionals, including auditors, accountants, analysts, and compliance officers. Secondary data were sourced from academic journals, regulatory reports, and prior studies. The findings indicate that forensic auditing techniques such as data analytics, digital forensics, and fraud risk assessment are perceived as highly effective in fraud detection. However, challenges such as lack of expertise, high costs, and limited organizational support hinder their widespread adoption. The study concludes that increased awareness, training, and integration of forensic audits into regular audit frameworks can significantly strengthen corporate fraud detection and governance mechanisms.},
        keywords = {Forensic Auditing, Corporate Fraud, Fraud Detection, Digital forensics.},
        month = {February},
        }