Fraudulent Financial Reporting in the Banking Sector of Bangladesh: A Prediction

Document Type : Original Research


Department of Accounting & Information Systems, University of Dhaka, Dhaka, Bangladesh


The purpose of this study is to predict the areas in financial statements susceptive to fraud in the banking sector of Bangladesh. Data of 13 years ranging from 2006 to 2018 of 29 listed banks in Bangladesh were examined for the purpose of this study. Financial data suggested by International Standard on Auditing (ISA) 240 as fraud risk indicators were used as the independent variables and banks identified by Centre for Policy Dialogue (CPD) to be engaged in fraud, scam and heists were taken as dependent variable. Multilayer Perceptron Network (MLP), a class of feedforward Artificial Neural Network (ANN) model was used as the analytical tool. It is found that loan disbursement, assets, profit, operating expenses and tax are the areas that can signal the probable fraud in financial statements of the listed banks of Bangladesh. The findings of this study will have policy implications for auditors and the regulators of money market in Bangladesh.


Main Subjects

Agyei-Mensah, D. B. K. (2015). The determinants of financial ratio disclosures and quality: Evidence from an emerging market. International Journal of Accounting and Financial Reporting, 5(1), pp. 188-211.
American Institute of Certified Public Accountants (AICPA). (2002). Consideration of Fraud in a Financial Statement Audit. Durham, North Carolina.
Bangladesh Bank, (2018). Annual Report 2017-2018. Dhaka, pp. 28, 36.
Beasley, M. S., Carcello, J. V., Hermanson. D. R., & Lapides, P.D. (2000). Fraudulent financial reporting: consideration of industry traits and corporate governance mechanisms. Accounting Horizons, 14(4), pp. 441-454.
Bell, T. B., & Carcello, J. V. (2000). A decision aid for assessing the likelihood of fraudulent financial reporting. Auditing: A Journal of Practice & Theory, 19(1), pp. 169-184.
Centre for Policy Dialogue (CPD), (2018). Banking Sector in Bangladesh: Moving from Diagnosis to Action. Dhaka, pp. 17-24.
Chen G., Firth M., Gao D. N., & Rui O.M. (2006). Ownership structure, corporate governance, and fraud: evidence from China. Journal of Corporate Finance, 12(3), pp. 424–448.
Chen, S. (2016). Detection of fraudulent financial statements using the hybrid data mining approach. SpringerPlus, 5(89).
Chen, S., Goo, Y.J., & Shen, Z.D. (2014). A hybrid approach of stepwise regression, logistic regression, support vector machine, and decision tree for forecasting fraudulent financial statements. Science World Journal.
Chen, S., & Li, J. (2015). Going concern prediction using data mining. ICIC-ELB, 6, pp. 3311–3317.
Chung, J., & Monroe, G.S. (2001). A research note on the effects of gender and task complexity on an audit judgment. Behavioral Research in Accounting, 13, pp. 111-125.
Cressey, D. R. (1953). Other people’s money: A study in the social psychology of embezzlement. The American Journal of Sociology.
Delen, D., Kuzey, C., & Uyar, A. (2013). Measuring firm performance using financial ratios: A decision tree approach. ESWA Expert Systems with Applications, 40(10), pp. 3970-3983.
Elliot, R., & Willingham, J.J. (1980). Management fraud: Detection and deterrence. Princeton, New Jersey: Petrocelli Books.
Eryigit, M. (2019). Short-term Performance of Stocks after Fraudulent Financial Reporting Announcement. Journal of Financial Crime, 26(2), pp. 464-476.
Gullkvist, B., & Jokipii, A. (2013). Perceived importance of red flags across fraud types. Critical Perspectives on Accounting, 24(1), pp. 44-6.
Hansen, J. V., McDonald, J. B., & Stice, J. D. (1992). Artificial intelligence and generalized qualitative-response models: an empirical test on two audit decision-making domains. Decision Sciences, 23(3), pp. 708–723.
Hopwood, W. S., Leiner, J.J., & Young G.R. (2008). Forensic Accounting. New York: McGraw-Hill/Irwin.
Howard, S., & Sheetz, M. (2006). Forensic accounting and fraud investigation for non-experts. New Jersey: John Wiley and Sons Inc.
Ilter, C. (2014). Misrepresentation of financial statements: An accounting fraud case from Turkey. Journal of Financial Crime, 21(2), pp. 215-225.
IFAC, I.F.O.A. (2010). International Standard on Auditing 240: The Auditor’s Responsibilities Relating to Fraud, IFAC, New York, pp. 156-197.
Jan, Chyan-long. (2018). An Effective Financial Statements Fraud Detection Model for the Sustainable Development of Financial Markets: Evidence from Taiwan. Sustainability, MDPI, Open Access Journal, 10(2), pp. 1-14.
Jiang, H., & Habib, A. (2012). Split-share reform and earnings management: evidence from China. Advances in Accounting, 28(1), pp. 120–127.
Kanapickienė, R., & Grundienė, Ž. (2015). The model of fraud detection in financial statements by means of financial ratios. Procedia - Social and Behavioral Sciences, 213, pp. 321-327.
Kaplan, S., & Reckers, P.M.J. (1995). Auditors’ reporting decisions for accounting estimates: The effect of assessments of the risk of fraudulent financial reporting. Managerial Auditing Journal, 10(5), pp. 27-36.
Karwai, S. A. (2002). Bank Fraud: Can Shari’ah prevent it? Journal of Business Administration, 2(1), pp. 62-78.
Kieso, D. E., Weygandt, J. J., & Warfield, T. D. (2012). Intermediate accounting (14th Ed). Full disclosure in financial reporting. Somerset, NJ: John Wiley & Sons, Inc.
KPMG, (2003). Forensic and Fraud Survey. Montvale, NJ
KPMG, (2016). Global profiles of the fraudster: Technology enables and weak controls fuel the fraud. Montvale, NJ
Li, H., & Sun, J. (2009). Predicting business failure using multiple case-based reasoning combined with support vector machine. Expert Systems with Applications, 36(6), pp. 10085-10096.
Lokanan, M. E. (2014). How Senior Managers Perpetuate Accounting Fraud? Lessons for Fraud Examiners from an Instructional Case. Journal of Financial Crime, 21(4), pp. 411-423.
Mahmood, M. (2019). The current state of the banking industry in Bangladesh. Financial Express. Available at: [Accessed: 21 January 2020].
Maricica, M., & Georgeta, V. (2012). Business failure risk analysis using financial ratios. Procedia - Social and Behavioral Sciences, 62, pp. 728-732.
Moyes, G.D., & Hasan, I. (1996). An empirical analysis of fraud detection likelihood. Managerial Auditing Journal, 11(3), pp. 41-56.
Office of the Comptroller and Auditor General of Bangladesh, (2016). Fraud Audit Manual. Dhaka, pp. 2-3.
Okunbor, J.A., & Obaretin, O. (2010). Effectiveness of the application of forensic accounting services in Nigerian organizations. Journal of Management Sciences, 1(1), pp. 171-184.
Omar, N., Johari, Z., & Smith, M. (2017). Predicting fraudulent financial reporting using artificial neural network. Journal of Financial Crime, 24(2), pp. 362-387.
Oumar, A. W., & Augustin D, P. (2019). Credit Card Fraud Detection Using ANN. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(7).
Ozcelik, H. (2020). An Analysis of Fraudulent Financial Reporting Using the Fraud Diamond Theory Perspective: An Empirical Study on the Manufacturing Sector Companies Listed on the Borsa Istanbul. Grima, S., Boztepe, E. and Baldacchino, P. (Ed.) Contemporary Issues in Audit Management and Forensic Accounting (Contemporary Studies in Economic and Financial Analysis, Vol. 102), Emerald Publishing Limited, pp. 131-153.
Ozili, P. (2020). Advances and issues in fraud research: a commentary. Journal of Financial Crime, 27(1), pp. 92-103.
Öztürk, M., & Usul, H. (2020). Detection of Accounting Frauds Using the Rule-Based Expert Systems within the Scope of Forensic Accounting. Grima, S., Boztepe, E. and Baldacchino, P. (Ed.) Contemporary Issues in Audit Management and Forensic Accounting (Contemporary Studies in Economic and Financial Analysis, Vol. 102), Emerald Publishing Limited, pp. 155-171.
Pai, P.F., Hsu, M.F., & Wang, M.C. (2011). A support vector machine-based model for detecting top management fraud. Knowledge-Based Systems, 24(2), pp. 314–321.
Ravisankar, P., Ravi, V., Rao, G. R., & Bose, I. (2011). Detection of Financial Statement Fraud and Feature Selection Using Data Mining Techniques. Decision Support Systems, 50(2), pp. 491-500.
Song, D., Lee, H., & Cho, E. (2013). The Association between Earnings Management and Asset Misappropriation. Managerial Auditing Journal, 28(6), pp. 542-567.
Stanbury, J., & Palsey-Menzies, C. (2010). Forensic Futurama: Why Forensic Accounting Is Evolving.
Uretsky, M. (1980). An interdisciplinary approach to the study of management fraud, in Elliott, R.K. and Willingham, J.J. (eds.), Management fraud: Detection and Deterrence. Princeton: Petrocelli Books.
Vanasco, R. (1998). Fraud Auditing. Managerial Auditing Journal, 13(2), pp. 4-71.
Wei, Y., Chen, J., & Wirth, C. (2017). Detecting Fraud in Chinese Listed Company Balance Sheets. Pacific Accounting Review, 29(3), pp. 356-379.
Yeh, C.C., Chi, D.J., & Hsu, M.F. (2010). A hybrid approach of DEA, rough set and support vector machines for business failure prediction. Expert Systems with Applications, 37(2), pp. 1535–1541.
Yeh, C.C., Chi, D.J., Lin, T.Y., & Chiu, S.H. (2016). A hybrid detecting fraudulent financial statements model using rough set theory and support vector machines. Cybernetics and Systems, 2016, 47(4), pp. 261–276.
Yucel, E. (2013). Effectiveness of red flags in detecting fraudulent financial reporting: An application in Turkey. Journal of Accounting and Finance, 60, pp. 139-158.
Zakaryazad, A., & Duman, E. (2015). A profit-driven Artificial Neural Network (ANN) with applications to fraud detection and direct marketing. Neurocomputing, 175(A), pp. 121-131.
Zhou W., & Kapoor G. (2011). Detecting evolutionary financial statement fraud. Decision Support Systems, 50(3), pp. 570–575.
Zupan, J. (1994). Introduction to Artificial Neural Network (ANN) Methods: What They Are and How to Use Them. Acta Chimica Slovenica, 41(3).