scholarly journals Bayesian inference of local government audit outcomes

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261245
Author(s):  
Wilson Tsakane Mongwe ◽  
Rendani Mbuvha ◽  
Tshilidzi Marwala

The scandals in publicly listed companies have highlighted the large losses that can result from financial statement fraud and weak corporate governance. Machine learning techniques have been applied to automatically detect financial statement fraud with great success. This work presents the first application of a Bayesian inference approach to the problem of predicting the audit outcomes of financial statements of local government entities using financial ratios. Bayesian logistic regression (BLR) with automatic relevance determination (BLR-ARD) is applied to predict audit outcomes. The benefit of using BLR-ARD, instead of BLR without ARD, is that it allows one to automatically determine which input features are the most relevant for the task at hand, which is a critical aspect to consider when designing decision support systems. This work presents the first implementation of BLR-ARD trained with Separable Shadow Hamiltonian Hybrid Monte Carlo, No-U-Turn sampler, Metropolis Adjusted Langevin Algorithm and Metropolis-Hasting algorithms. Unlike the Gibbs sampling procedure that is typically employed in sampling from ARD models, in this work we jointly sample the parameters and the hyperparameters by putting a log normal prior on the hyperparameters. The analysis also shows that the repairs and maintenance as a percentage of total assets ratio, current ratio, debt to total operating revenue, net operating surplus margin and capital cost to total operating expenditure ratio are the important features when predicting local government audit outcomes using financial ratios. These results could be of use for auditors as focusing on these ratios could potentially speed up the detection of fraudulent behaviour in municipal entities, and improve the speed and quality of the overall audit.

2021 ◽  
Author(s):  
Ahmed M. Khedr ◽  
Magdi El Bannany ◽  
Sakeena Kanakkayil

Fraudulent financial statements are deliberate furnishing and/or reporting incorrect statistics, and this has become a major economic and social concern as the global market is witnessing an upsurge in financial accounting fraud, costing businesses billions of dollars a year. Identifying companies that manipulate financial statements remains a challenge for auditors, as fraud strategies have become increasingly sophisticated over the years. We evaluate machine learning techniques for financial statement fraud detection, particularly a powerful ensemble technique, the XGBoost algorithm, that help to identify fraud on a set of sample companies drawn from the MENA region. The issue of the class imbalance in the dataset is addressed by applying the SMOTE algorithm. We found that XGBoost algorithm outperformed other algorithms in this study: Logistic Regression (LR), Decision Tree (DT), Vector Machine Support (SVM), Adaboost, and RandomForest. The XGBoost algorithm is then optimised to obtain the optimum performance.


Author(s):  
Priyastiwi Priyastiwi

This study aimed to examine the effect of demographic factors and organizational climate on the intention of internal whistleblowing. The sample was an accountant who had worked as an auditor. Data collection methods using questionnaires with financial statement fraud case scenarios. This research use ANOVA data analysis method to examine demographic factors include age, gender, and experience, as well as organizational climate on the intention of internal whistleblowing. The results showed only the experience of demographic factors that influence internal whistleblowing. Besides internal whistlelowing also influenced by organizational climate in the company.Keyword: Demographics, Organizational Climate, Whistleblowing


Author(s):  
Nguyen Tien Hung ◽  
Huynh Van Sau

The study was conducted to identify fraudulent financial statements at listed companies (DNNY) on the Ho Chi Minh City Stock Exchange (HOSE) through the Triangular Fraud Platform This is a test of VSA 240. At the same time, the conformity assessment of this model in the Vietnamese market. The results show that the model is based on two factors: the ratio of sales to total assets and return on assets; an Opportunity Factor (Education Level); and two factors Attitude (change of independent auditors and opinion of independent auditors). This model is capable of accurately forecasting more than 78% of surveyed sample businesses and nearly 72% forecasts for non-research firms.  Keywords Triangle fraud, financial fraud report, VSA 240 References Nguyễn Tiến Hùng & Võ Hồng Đức (2017), “Nhận diện gian lận báo cáo tài chính: Bằng chứng thực nghiệm tại các doanh nghiệp niêm yết ở Việt Nam”, Tạp chí Công Nghệ Ngân Hàng, số 132 (5), tr. 58-72.[2]. Hà Thị Thúy Vân (2016), “Thủ thuật gian lận trong lập báo cáo tài chính của các công ty niêm yết”, Tạp chí tài chính, kỳ 1, tháng 4/2016 (630). [3]. Cressey, D. R. (1953). Other people's money; a study of the social psychology of embezzlement. New York, NY, US: Free Press.[4]. Bộ Tài Chính Việt Nam, (2012). Chuẩn mực kiểm toán Việt Nam số 240 – Trách nhiệm của kiểm toán viên đối với gian lận trong kiểm toán báo cáo tài chính. [5]. Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of financial economics, 3(4), 305-360.[6]. Võ Hồng Đức & Phan Bùi Gia Thủy (2014), Quản trị công ty: Lý thuyết và cơ chế kiểm soát, Ấn bản lần 1, Tp.HCM, Nxb Thanh Niên.[7]. Freeman, R. E. (1984). Strategic management: A stakeholder approach. Boston: Pitman independence on corporate fraud. Managerial Finance 26 (11): 55-67.[9]. Skousen, C. J., Smith, K. R., & Wright, C. J. (2009). Detecting and predicting financial statement fraud: The effectiveness of the fraud triangle and SAS No. 99. Available at SSRN 1295494.[10]. Lou, Y. I., & Wang, M. L. (2011). Fraud risk factor of the fraud triangle assessing the likelihood of fraudulent financial reporting. Journal of Business and Economics Research (JBER), 7(2).[11]. Perols, J. L., & Lougee, B. A. (2011). The relation between earnings management and financial statement fraud. Advances in Accounting, 27(1), 39-53.[12]. Trần Thị Giang Tân, Nguyễn Trí Tri, Đinh Ngọc Tú, Hoàng Trọng Hiệp và Nguyễn Đinh Hoàng Uyên (2014), “Đánh giá rủi ro gian lận báo cáo tài chính của các công ty niêm yết tại Việt Nam”, Tạp chí Phát triển kinh tế, số 26 (1) tr.74-94.[13]. 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.[14]. Amara, I., Amar, A. B., & Jarboui, A. (2013). Detection of Fraud in Financial Statements: French Companies as a Case Study. International Journal of Academic Research in Accounting, Finance and Management Sciences, 3(3), 40-51.[15]. Beasley, M. S. (1996). An empirical analysis of the relation between the board of director composition and financial statement fraud. Accounting Review, 443-465.[16]. Beneish, M. D. (1999). The detection of earnings manipulation. Financial Analysts Journal, 55(5), 24-36.[17]. Persons, O. S. (1995). Using financial statement data to identify factors associated with fraudulent financial reporting. Journal of Applied Business Research (JABR), 11(3), 38-46.[18]. Summers, S. L., & Sweeney, J. T. (1998). Fraudulently misstated financial statements and insider trading: An empirical analysis. Accounting Review, 131-146.[19]. Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1996). Causes and consequences of earnings manipulation: An analysis of firms subject to enforcement actions by the SEC. Contemporary accounting research, 13(1), 1-36.[20]. Loebbecke, J. K., Eining, M. M., & Willingham, J. J. (1989). Auditors experience with material irregularities – Frequency, nature, and detectability. Auditing – A journal of practice and Theory, 9(1), 1-28. [21]. Abbott, L. J., Park, Y., & Parker, S. (2000). The effects of audit committee activity and independence on corporate fraud. Managerial Finance, 26(11), 55-68.[22]. Farber, D. B. (2005). Restoring trust after fraud: Does corporate governance matter?. The Accounting Review, 80(2), 539-561.[23]. Stice, J. D. (1991). Using financial and market information to identify pre-engagement factors associated with lawsuits against auditors. Accounting Review, 516-533.[24]. Beasley, M. S., Carcello, J. V., & Hermanson, D. R. (1999). COSO's new fraud study: What it means for CPAs. Journal of Accountancy, 187(5), 12.[25]. Neter, J., Wasserman, W., & Kutner, M. H. (1990). Applied statistical models.Richard D. Irwin, Inc., Burr Ridge, IL.[26]. Gujarati, D. N. (2009). Basic econometrics. Tata McGraw-Hill Education.[27]. McFadden, D. (1974). Conditional Logit Analysis of Qualita-tive Choice Behavior," in Frontiers in Econometrics, P. Zarenm-bka, ed. New York: Academic Press, 105-42.(1989). A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration," Econometrica, 54(3), 1027-1058.[28]. DA Cohen, ADey, TZ Lys. (2008), “Accrual-Based Earnings Management in the Pre-and Post-Sarbanes-Oxley Periods”. The accounting review.


2020 ◽  
Vol 25 (1) ◽  
pp. 29-44
Author(s):  
Mariati ◽  
Emmy Indrayani

Company’s financial condition reflected in the financial statements. However, there are many loopholes in the financial statements which can become a chance for the management and certain parties to commit fraud on the financial statements. This study aims to detect financial statement fraud as measured using fraud score model that occurred in issuers entered into the LQ-45 index in 2014-2016 with the use of six independent variables are financial stability, external pressure, financial target, nature of industry, ineffective monitoring and rationalization. This study using 27 emiten of LQ-45 index during 2014-2016. However, there are some data outlier that shall be removed, thus sample results obtained 66 data from 25 companies. Multiple linear regression analysis were used in this study. The results showed that the financial stability variables (SATA), nature of industry (RECEIVBLE), ineffective monitoring (IND) and rationalization (ITRENDLB) proved to be influential or have the capability to detect financial statement fraud. While the external pressure variables (DER) and financial target (ROA) are not able to detect the existence of financial statement fraud. Simultaneously all variables in this study were able to detect significantly financial statement fraud.


2015 ◽  
Author(s):  
Teguh Puspandoyo ◽  
Farida Hendrastuti ◽  
Fitriana Kurniawati ◽  
Eka Setyorini ◽  
mariani mariani ◽  
...  

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