How to cite this paper
Shalaby, A & Al-Harkan, A. (2022). The awareness of judicial accounting techniques towards the expectations of the external auditor in detecting fraud and its impact on the performance.Accounting, 8(3), 345-354.
Refrences
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Antunes, A. L. F. (2017). Big data analytics applied to sensor data of engineering structures: automatic detection of outliers (Doctoral dissertation).
Beasley, M. S., Carcello, J. V., Hermanson, D. R., & Neal, T. L. (2009). The audit committee oversight process. Contemporary Accounting Research, 26(1), 65-122.
Bhasin, M. L. (2015). Contribution of forensic accounting to corporate governance: An exploratory study of an Asian country. International Business Management, 10(4), 2016.
Beneish, M. D. (1999). A note on Wiedman's (1999) instructional case: detecting earnings manipulation. Issues in Accounting Education, 14(2), 369.
Collins, J. C. (2017). Using Excel and Benford's Law to detect fraud. Journal of Accountancy, 223(4), 44-50.
Cusack, B., & Ahokov, T. A. (2016). Improving forensic software tool performance in detecting fraud for financial statements.
Diekmann, A., & Jann, B. (2010). Benford's law and fraud detection: Facts and legends. German Economic Review, 11(3), 397-401.
Durtschi, C., Hillison, W., & Pacini, C. (2004). The effective use of Benford’s law to assist in detecting fraud in accounting data. Journal of forensic accounting, 5(1), 17-34.
Grammatikos, T., & Papanikolaou, N. I. (2015). Applying Benford‟ s law to detect fraudulent practices in the banking industry (pp. 1-28). Working Paper.
Haynes, A. H. (2012). Detecting fraud in bankrupt municipalities using Benford's Law.
Ijeoma, N. B. (2015). Empirical analysis on the use of forensic accounting techniques in curbing creative accounting.
Kotsiantis, S., Koumanakos, E., Tzelepis, D., & Tampakas, V. (2006). Forecasting fraudulent financial statements using data mining. International journal of computational intelligence, 3(2), 104-110.
Kovalerchuk, B., Vityaev, E., & Holtfreter, R. (2007). Correlation of complex evidence in forensic accounting using data mining. Journal of Forensic Accounting, 8(1), 53-88.
Mehta, A., & Bhavani, G. (2017). Application of forensic tools to detect fraud: The case of Toshiba. Journal of Forensic and Investigative Accounting, 9(1), 692-710.
Mehta, G. S., & Mathur, T. (2007). Preventing Financial Fraud ThroughForensic Accounting'. CHARTERED ACCOUNTANT-NEW DELHI-, 55(10), 1575.
Miller, S. J. (2008). Benford’s Law and Fraud Detection, or: Why the IRS Should Care About Number Theory!.
Miller, S. J., & Nigrini, M. (2008). Theory and applications of Benford’s law to fraud detection, or: Why the IRS should care about number theory!.
Nichols, D. C., & Wahlen, J. M. (2004). How do earnings numbers relate to stock returns? A review of classic accounting research with updated evidence. Accounting Horizons, 18(4), 263-286.
Pan, G., & Seow, P. S. (2016). Preparing accounting graduates for digital revolution: A critical review of information technology competencies and skills development. Journal of Education for business, 91(3), 166-175.
Perols, J. (2011). Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Auditing: A Journal of Practice & Theory, 30(2), 19-50.
Phua, C., Lee, V., Smith, K., & Gayler, R. (2010). A comprehensive survey of data mining-based fraud detection research. arXiv preprint arXiv:1009.6119.
Sharma, A., & Panigrahi, P. K. (2013). A review of financial accounting fraud detection based on data mining techniques. arXiv preprint arXiv:1309.3944.
Sikka, P., Puxty, A., Willmott, H., & Cooper, C. (1998). The impossibility of eliminating the expectations gap: some theory and evidence. Critical perspectives on accounting, 9(3), 299-330.
Singleton, T. W. (2010). Fraud auditing and forensic accounting (Vol. 11). John Wiley & Sons.
Syan, C. S., & Menon, U. (Eds.). (2012). Concurrent engineering: concepts, implementation and practice. Springer Science & Business Media.
Vladu, A. B., Amat, O., & Cuzdriorean, D. D. (2017). Truthfulness in accounting: How to discriminate accounting manipulators from non-manipulators. Journal of Business Ethics, 140(4), 633-648.
Zhou, W., & Kapoor, G. (2011). Detecting evolutionary financial statement fraud. Decision Support Systems, 50(3), 570-575.
Antunes, A. L. F. (2017). Big data analytics applied to sensor data of engineering structures: automatic detection of outliers (Doctoral dissertation).
Beasley, M. S., Carcello, J. V., Hermanson, D. R., & Neal, T. L. (2009). The audit committee oversight process. Contemporary Accounting Research, 26(1), 65-122.
Bhasin, M. L. (2015). Contribution of forensic accounting to corporate governance: An exploratory study of an Asian country. International Business Management, 10(4), 2016.
Beneish, M. D. (1999). A note on Wiedman's (1999) instructional case: detecting earnings manipulation. Issues in Accounting Education, 14(2), 369.
Collins, J. C. (2017). Using Excel and Benford's Law to detect fraud. Journal of Accountancy, 223(4), 44-50.
Cusack, B., & Ahokov, T. A. (2016). Improving forensic software tool performance in detecting fraud for financial statements.
Diekmann, A., & Jann, B. (2010). Benford's law and fraud detection: Facts and legends. German Economic Review, 11(3), 397-401.
Durtschi, C., Hillison, W., & Pacini, C. (2004). The effective use of Benford’s law to assist in detecting fraud in accounting data. Journal of forensic accounting, 5(1), 17-34.
Grammatikos, T., & Papanikolaou, N. I. (2015). Applying Benford‟ s law to detect fraudulent practices in the banking industry (pp. 1-28). Working Paper.
Haynes, A. H. (2012). Detecting fraud in bankrupt municipalities using Benford's Law.
Ijeoma, N. B. (2015). Empirical analysis on the use of forensic accounting techniques in curbing creative accounting.
Kotsiantis, S., Koumanakos, E., Tzelepis, D., & Tampakas, V. (2006). Forecasting fraudulent financial statements using data mining. International journal of computational intelligence, 3(2), 104-110.
Kovalerchuk, B., Vityaev, E., & Holtfreter, R. (2007). Correlation of complex evidence in forensic accounting using data mining. Journal of Forensic Accounting, 8(1), 53-88.
Mehta, A., & Bhavani, G. (2017). Application of forensic tools to detect fraud: The case of Toshiba. Journal of Forensic and Investigative Accounting, 9(1), 692-710.
Mehta, G. S., & Mathur, T. (2007). Preventing Financial Fraud ThroughForensic Accounting'. CHARTERED ACCOUNTANT-NEW DELHI-, 55(10), 1575.
Miller, S. J. (2008). Benford’s Law and Fraud Detection, or: Why the IRS Should Care About Number Theory!.
Miller, S. J., & Nigrini, M. (2008). Theory and applications of Benford’s law to fraud detection, or: Why the IRS should care about number theory!.
Nichols, D. C., & Wahlen, J. M. (2004). How do earnings numbers relate to stock returns? A review of classic accounting research with updated evidence. Accounting Horizons, 18(4), 263-286.
Pan, G., & Seow, P. S. (2016). Preparing accounting graduates for digital revolution: A critical review of information technology competencies and skills development. Journal of Education for business, 91(3), 166-175.
Perols, J. (2011). Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Auditing: A Journal of Practice & Theory, 30(2), 19-50.
Phua, C., Lee, V., Smith, K., & Gayler, R. (2010). A comprehensive survey of data mining-based fraud detection research. arXiv preprint arXiv:1009.6119.
Sharma, A., & Panigrahi, P. K. (2013). A review of financial accounting fraud detection based on data mining techniques. arXiv preprint arXiv:1309.3944.
Sikka, P., Puxty, A., Willmott, H., & Cooper, C. (1998). The impossibility of eliminating the expectations gap: some theory and evidence. Critical perspectives on accounting, 9(3), 299-330.
Singleton, T. W. (2010). Fraud auditing and forensic accounting (Vol. 11). John Wiley & Sons.
Syan, C. S., & Menon, U. (Eds.). (2012). Concurrent engineering: concepts, implementation and practice. Springer Science & Business Media.
Vladu, A. B., Amat, O., & Cuzdriorean, D. D. (2017). Truthfulness in accounting: How to discriminate accounting manipulators from non-manipulators. Journal of Business Ethics, 140(4), 633-648.
Zhou, W., & Kapoor, G. (2011). Detecting evolutionary financial statement fraud. Decision Support Systems, 50(3), 570-575.