Predicting fraudulent financial statement using cash flow shenanigans
Abstract
Detection of fraudulent financial stewardship in the cash flow section is an exciting thing and is rarely studied. This research empirically tests the discovery of fraudulent financial statements based on basic cash flow shenanigans. The sample of this study amounted to 470 data mining companies in Indonesia, Malaysia, China, and Japan. The analysis method used is a positive approach. The results show that all ratios used can predict fraudulent financial statements. Three ratios of cash flow shenanigans, namely change in receivable to cash flow operations, days payable outstanding, and change in inventory to cash flow operations, significantly affect the F-Score. Meanwhile, the six cash flow shenanigans ratios, namely cash flow operations to current liability, operating cash flow ratio, free cash flow, cash flow operations to total liability, days payable outstanding, and change in inventory to cash flow operations, have a significant effect on the M-Score.
Keyword : fraudulent financial statement, financial shenanigans, cash flow shenanigans, m-score, f-score, detection
How to Cite
Tarjo, T., Prasetyono, P., Sakti, E., Pujiono, Mat-Isa, Y., & Safkaur, O. (2023). Predicting fraudulent financial statement using cash flow shenanigans. Business: Theory and Practice, 24(1), 33–46. https://doi.org/10.3846/btp.2023.15283
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Bhandari, S. B., & Iyer, R. (2013). Predicting business failure using cash flow statement based measures. Managerial Finance, 39(7), 667–676. https://doi.org/10.1108/03074351311323455
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Bukit, R. B., & Iskandar, T. M. (2009). Surplus free cash flow, earnings management and audit committee. International Journal of Economics and Management, 3(1), 204–233.
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Dalnial, H., Kamaluddin, A., Sanusi, Z. M., & Khairuddin, K. S. (2014b). Accountability in financial reporting: Detecting fraudulent firms. Procedia – Social and Behavioral Sciences, 145, 61–69. https://doi.org/10.1016/j.sbspro.2014.06.011
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Dechow, P. M., Hutton, A. P., Kim, J. H., & Sloan, R. G. (2012). Detecting earnings management: A new approach. Journal of Accounting Research, 50(2), 275–334. https://doi.org/10.1111/j.1475-679X.2012.00449.x
Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1995). Detecting earnings management. The Accounting Review, 70(20), 193–225.
Deo, P., & Liu, C. Z. (2016). All cash is not created equal: Detecting fraudulent cash flows. Journal of Forensic & Investigative Accounting, 8(2), 325–337.
Dimitrijevic, D. (2015). The detection and prevention of manipulations in the balance sheet and the cash flow statement. Economic Horizons, 17(2), 137–153. https://doi.org/10.5937/ekonhor1502137d
Dimitrijevic, D., Jovkovic, B., & Milutinovic, S. (2020). The scope and limitations of external audit in detecting frauds in company’s operations. Journal of Financial Crime, 28(3). https://doi.org/10.1108/JFC-11-2019-0155
Dugan, M., & Taylor, G. (2019). The indirect method – a valuable fraud detection tool. Journal of Forensic and Investigative Accounting, 11(3), 503–523.
Ebaid, I. E. S. (2011a). Accruals and the prediction of future cash flows: Empirical evidence from an emerging market. Management Research Review, 34(7), 838–853. https://doi.org/10.1108/01409171111146715
Ebaid, I. E. S. (2011b). Persistence of earnings and earnings components: Evidence from the emerging capital market of Egypt. International Journal of Disclosure and Governance, 8(2), 174–193. https://doi.org/10.1057/jdg.2010.29
Fahlevi, M. R., & Marlinah, A. (2019). The influence of liquidity, capital structure, profitability and cash flows on the company’s financial distress. Jurnal Bisnis Dan Akuntansi, 20(1), 59–68. https://doi.org/10.34208/jba.v20i1.409
Francis, R. N., & Eason, P. (2012). Accruals and the naïve out-of-sample prediction of operating cash flow. Advances in Accounting, 28(2), 226–234. https://doi.org/10.1016/j.adiac.2012.09.004
Ghazali, A. W., Shafie, N. A., & Sanusi, Z. M. (2015). Earnings management: An analysis of opportunistic behaviour, monitoring mechanism and financial distress. Procedia Economics and Finance, 28(April), 190–201. https://doi.org/10.1016/S2212-5671(15)01100-4
Ghozali, I. (2018). Analisis Multivariate SPSS (9th ed.). Badan Penerbit Universitas Diponegoro.
Goel, S. (2013). Decoding gimmicks of financial shenanigans in telecom sector in India. Journal of Accounting and Management Information Systems, 12(1), 118–131.
Goel, S. (2014). The quality of reported numbers by the management: A case testing of earnings management of corporate India. Journal of Financial Crime, 21(3), 355–376. https://doi.org/10.1108/JFC-02-2013-0011
Grove, H., & Basilico, E. (2011). Major financial reporting frauds of the 21st century: Corporate and risk lessons learned. Journal of Forensic and & Investigative Accounting, 3(2), 191–226.
Hugo, J. (2019). Efektivitas Model Beneish M-Score Dan Model F-Score Dalam Mendeteksi Kecurangan Laporan Keuangan. Jurnal Muara Ilmu Ekonomi Dan Bisnis, 3(1), 165. https://doi.org/10.24912/jmieb.v3i1.2296
Jones, J. J. (1991). Earnings management during import relief investigations. Journal of Accounting Research, 29(2), 193–228. https://doi.org/10.2307/2491047
Jordan, S., & Messner, M. (2020). The use of forecast accuracy indicators to improve planning quality: Insights from a case study. European Accounting Review, 29(2), 337–359. https://doi.org/10.1080/09638180.2019.1577150
Kanapickienė, R., & Grundienė, Ž. (2015). The model of fraud detection in financial statements by means of financial ratios. Procedia – Social and Behavioral Sciences, 213, 321–327. https://doi.org/10.1016/j.sbspro.2015.11.545
Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32(4), 995–1003. https://doi.org/10.1016/j.eswa.2006.02.016
Krylov, S. (2018). Target financial forecasting as an instrument to improve company financial health. Cogent Business and Management, 5(1), 1–42. https://doi.org/10.1080/23311975.2018.1540074
Lennox, C., & Yu, Y. J. (2019). Cooking the books using different ingredients: An examination of cash flow frauds. Elsevier. https://doi.org/10.2139/ssrn.3591493
Luo, M. (2008). Unusual operating cash flows and stock returns. Journal of Accounting and Public Policy, 27(5), 420–429. https://doi.org/10.1016/j.jaccpubpol.2008.07.004
Md Nasir, N. A. binti, Ali, M. J., Razzaque, R. M. R., & Ahmed, K. (2018). Real earnings management and financial statement fraud: Evidence from Malaysia. International Journal of Accounting and Information Management, 26(4), 508–526. https://doi.org/10.1108/IJAIM-03-2017-0039
Molina, C. A., & Preve, L. A. (2009). Trade receivables policy of distressed firms and its effect on the costs of financial distress. Financial Management, 38(3), 663–686. https://doi.org/10.1111/j.1755-053X.2009.01051.x
Nagar, N., & Raithatha, M. (2016). Does good corporate governance constrain cash flow manipulation? Evidence from India. Managerial Finance, 42(11), 1034–1053. https://doi.org/10.1108/MF-01-2016-0028
Nobanee, H., Atayah, O. F. and Mertzanis, C. (2020). Does anti-corruption disclosure affect banking performance? Journal of Financial Crime, 27(4), 1161–1172. https://doi.org/10.1108/JFC-04-2020-0047
Peasnell, K. V., Pope, P. F., & Young, S. (2000). Detecting earnings management using cross-sectional abnormal accruals models. Accounting and Business Research, 30(4), 313–326. https://doi.org/10.1080/00014788.2000.9728949
Persons, O. S. (1995). Using financial statement data to identify Fraudulent financial reporting. Journal of Applied Business Research, 11(3), 38–46. https://doi.org/10.19030/jabr.v11i3.5858
PwC. (2019). Mine 2019. https://www.pwc.com/gx/en/energy-utilities-mining/publications/pdf/mine-report-2019.pdf
Rahmatika, D. N., Kartikasari, M. D., Dewi Indriasih, D., Sari, I. A., & Mulia, A. (2019). Detection of Fraudulent financial statement; can perspective of fraud diamond theory be applied to property, real estate, and building construction companies in Indonesia? European Journal of Business and Management Research, 4(6), 1–9. https://doi.org/10.24018/ejbmr.2019.4.6.139
Ratmono, D., Darsono, D., & Cahyonowati, N. (2020). Financial statement fraud detection with Beneish M-Score and Dechow F-Score Model: An empirical analysis of Fraud Pentagon Theory in Indonesia. International Journal of Financial Research, 11(6), 154. https://doi.org/10.5430/ijfr.v11n6p154
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