Developing a model to predict fraudulent financial reporting

[1] S.J. Ahmadi, K. FaghaniMakerani and N. Fazeli, Presenting a model for predicting fraudulent financial statements and comparing financial statements and ratios with Benford’s Law, Manage. Account. Audit. Knowledge 9 (2020), no. 35, 221–237.

[2] N. Aqilah, N.F. Mohammed and A. Kamaluddin, Application of Beneish M-Score model in detecting probable earnings manipulation in Malaysian public listed companies, Integrated J. Bus. Econ. 5 (2021), no. 1, 86–100.

[3] I.R. Andaningsih and I. Rosyidi, The effect of detecting financial statement fraud of accounting data by external auditors in understanding red flags, which aims to reduce audit costs, JIA Sandikta 6 (2020), no. 8.

[4] H. Askaralooj, M.R. Nikbakht, G. Karami and M. Momeni, Development of the Beneish model with combination of artificial neural networks and the optimization algorithm of the cumulative movement of particles for the prediction of earnings manipulation, Account. Audit. Stud. 26 (2019), no. 4, 615–638.

[5] M.D. Beneish, The detection of earnings manipulation, Financ. Anal. J. 55 (1999), no. 5, 24–36.

[6] M.D. Beneish and P. Vorst, The cost of fraud prediction errors, Account. Rev. 97 (2022), no. 6, 91–121.

[7] Q. Bolo, F. Barzideh and H. Allahyari Abhari, A model to assess the risk of fraud in the audit of financial statements, Account. Knowledge 11 (2020), no. 4, 23–45.

[8] R.M. Dani, Financial ratios and fraudulent financial statements: Evidence from Malaysian fraud cases, PhD diss., Universiti Teknologi MARA, 2013.

[9] H. Etemadi and H. Zalqi, Application of logistic regression in identifying fraudulent financial reporting, Audit Knowledge Quart. 13 (2013), no. 2, 1–26.

[10] K. Forgandoost Haqiqi and F. Barwari, Investigating application of analytical methods in assessing the distortion risk, Account. Knowledge Res. 16 (2009), 18–24.

[11] H. Hariri, A.W.S. Pradana and S.L Widjajanti, Predicting financial statements corporate fraud: Beneish M-score model, J. Ilmiah Bidang Akunt. Manaj. 14 (2017), no. 2, 92–100.

[12] P. Harsanti and U.R. Mulyani, Testing of fraudulent financial statements with the Beneish M-score model for manufacturing companies listed in the Indonesian stock exchange, KnE Soc. Sci. 5 (2021), no. 7, 125–133.

[13] K.G. Hashedi and P. Magalingam, Financial fraud detection applying data mining techniques: a comprehensive review from 2009 to 2019, Comput. Sci. Rev. 40 (2021), 100402.

[14] A. Hosseini, The most important fraud in listed companies, Second annual conference on prevention of fraud and financial abuses, Tehran, 2011.

[15] A. Hosseinzadeh, M. Mousavi Shiri, Z. Hajiha and H. Nikoomaram, The relationship between accounting information quality criteria in response to managers’ motivational components, Dev. Transform. Manag. Quart. 45 (2021), 27–37.

[16] S. Istikhoroh, Financial target, external pressure, and ineffective monitoring: How do the impacts on financial statement fraud, Saudi J. Econ. Financ. 5 (2021), no. 9, 354–360.

[17] A. Janabadi and M. Motavasel, The effects of the earnings components on the stock returns and earnings stability in companies listed on the Tehran stock exchange, Dev. Transform. Manag. Quart. 9 (2017), 193–204.

[18] P. Kagias, A. Cheliatsidou, A. Garefalakis, J. Azibi and N. Sariannidis, The fraud triangle–an alternative approach, J. Financ. Crime 29 (2022), no. 3, 908–924.

[19] A. Khorasani, Examining the effects of applying auditing standards on information disclosure in fraudulent financial reporting, PhD Thesis in Accounting, Allameh Tabatabai University, 2018.

[20] G. Kordestani and R. Tatli, Predicting earnings manipulation: development of a model, Account. Audit. Rev. 23 (2016), no. 1, 73–96.

[21] S. Lehenchuk, T. Mostenska, H. Tarasiuk, I. Polishchuk and M. Gorodysky, Financial statement fraud detection of Ukrainian corporations on the basis of Beneish model, Int. Conf. Bus. Technol. Springer, Cham., 2020, pp. 1341–1356.

[22] J. Liodorova, M. Barkauskas and R. Sneidere, Detecting asset misappropriation: forensic accounting, Socrates 2 (2021), no. 20, 287–300.

[23] B. Mashayekhi and A. Hosseinpour, Investigating the relationship between real earnings management and accrual earnings management in companies suspected of fraud in the Tehran stock exchange, Financ. Account. Empir. Stud. 13 (2016), no. 49, 29–52.

[24] N. Rahimian and R. HajiHaidari, Detecting fraud using Beneish’s adjusted model and financial ratios, Account. Empi. Res. 31 (2019), 47–69.

[25] S.H. Sajjadi and T. Kazemi, Presenting a comprehensive model for fraudulent financial reporting in Iran using the grounded theory, Experiment. Account. Res. 6 (2016), no. 21, 185–204.

[26] L. Sedighi Kamal, Fraud in financial statements based on the report of the association of certified fraud examiners, Audit. J. 64 (2013), 116–120.

[27] F.H. Sezgin, Estimation financial information manipulation by negative binomial hurdle model, Int. J. Econ. Finance Stud. 4 (2012), no. 1, 71–82.

[28] S. Shahsavaripour, Detecting and preventing fraud in accounting, 7th International Conference on Accounting, Management and Innovation in Business, Tehran, 2020.

[29] M.M. Shakoori, A.A. Taherabadi, M. Ghanbari and B. Jamshidinavid, The Beneish model and presenting a comprehensive model of fraudulent financial reporting, J. Nonlinear Program Anal. 12 (2021), 39–48.

[30] S. She’ri Anaqiz, N. Rahimian, J. Salehi Sadaqiani and A. Khorasani, Examining and comparing the accuracy of the results obtained from Beniesh and adjusted Beneish models based on Iran’s economic environment in detecting and disclosing fraudulent financial reporting, Financ. Manage. Persp. 18 (2017), 105–123.

[31] A. Sobhanian, Investigating impact of financial distortions on tax avoidance with an emphasis on the role of transactions with related parties, J. Account. Manage. Vis. 1 (2018), no. 3, 1–13.

[32] C. Spathis, Detecting false financial statements using published data: some evidence from Greece, Manage. Audit. J. 17 (2002), no. 4, 179–191.

[33] R. Syahria, Detecting financial statement fraud using fraud diamond (a study on banking companies listed on the Indonesia stock exchange period 2012-2016), Asia Pacific Fraud J. 4 (2019), no. 2, 183–190.

[34] M. Tabari, H. Mohammadi and H. Arab, Assessing the situation and ranking the factors affecting the institutionalization of administrative ethics, Dev. Transform. Manag. Quart. 42 (2020), 35–45.

[35] H. Vakilifard, S. Jabarzadeh Kangarlooti and A. Poorreza Sultan Ahmadi, Examining the fraud characteristics in financial statements, Account. Mag. 24 (2009), no. 210, 36–41.

[36] E.F. Zainudin and H.A. Hashim, Detecting fraudulent financial reporting using financial ratio, J. Financ. Report. Account. 14 (2016), no. 2.

[37] M. Zare Behnamiri and E. Melkian Kalebasti, Predicting fraud in financial statements using financial ratios (case study: companies listed on the Tehran stock exchange), Financ. Manage. Persp. 12 (2015), 65–80.