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@article{191192,
        author = {Dr. Kalpesh B. Gelda and Pujara Dhaval Mahendrabhai},
        title = {Forensic Accounting: Detecting Financial Anomalies in HDFC Bank Using Benford's Law Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {12},
        number = {no},
        pages = {1083-1089},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191192},
        abstract = {India's banking sector, especially private sector banks, is an important pillar supporting India’s economic infrastructure and financial stability. But this sector faces consistent challenges ranging from financial manipulation, fraudulent activities, and data irregularities that shake the stakeholder’s confidence and threaten integrity of the institution. This research investigates the use of forensic accounting techniques, specifically Benford's Law, to detect potential manipulation in the financial statements of HDFC Bank, which is India's leading private sector financial institution. Using a quantitative methodology, this study checks financial data ranging from 2021 to 2025, checking Profit and Loss Statement, and Balance Sheets across multiple digit positions using Chi-Square goodness-of-fit testing. The investigation uses secondary data obtained from audited annual reports and official websites. Findings are varying degrees of conformity as per the expected Benford distributions across different digit positions and financial statement categories, with specific deviations identified in certain data subsets that need further investigation. This research shows that Benford's Law serves as an effective screening tool for identifying red flags in banking financial data, though it should be complemented with additional forensic procedures for detailed fraud detection.},
        keywords = {Forensic Accounting, Benford's Law, Financial Fraud Detection, HDFC Bank, Chi-Square Test, Private Sector Banking, Financial Statement Analysis, Fraud Prevention},
        month = {},
        }