نقش مدیریت سود در شناسایی صورتهای مالی متقلبانه در شرکتهای پذیرفتهشده در بورس اوراق بهادار تهران

Auditing practices nowadays have to cope with an increasing number of fraudulent financial statements. Data mining techniques can facilitate auditors in accomplishing the task of detection of fraudulent financial statements (FFS). Considering the character of FFS, this paper designs a FFS detection model based on support vector machine. To perform the experiment, we choose 44 FFS according to the auditing reports and 44 non-fraudulent financial statements(non-FFS) according to some specific standards from listed companies in China during 1999–2002 as training data set. Similarly, 73 FFS and 99 non-FFS during 2003–2006 are chosen as testing data set. We train the model using training data set and apply the trained model to the testing data set, good experimental results are obtained.

The study examines conceptually the detection of financial statements fraud using fraud pentagon. The study discusses the relationship between fraud pentagon and financial statement fraud. The methodology adopted in this study is library research whereby relevant and extant literature related to the elements in fraud pentagon were reviewed in relation to financial statements fraud. In the search for literature, it was observed that the various elements of fraud pentagon such as pressure (financial targets, financial stability, institutional ownership and external pressure), opportunity (ineffective monitoring and nature of industry), rationalization (auditor switching and change of director), competence (the changes in the company directorship and the proportion of independent directors in the board) and arrogance (frequent number of CEO's picture and CEO duality) significantly affect financial statements fraud. This study recommends that fraud pentagon should be given the statutory support in order to address financial statements fraud.
 
 Received: 27 August 2023 / Accepted: 19 October 2023 / Published: 5 November 2023

The capital market is one of the main indicators of economic health, which is very sensitive to changes in global and domestic conditions. The aim is to analyze this moderating effect and provide insights into the risks associated with information transparency. Using a quantitative approach and secondary data from 35 technology firms listed on the Indonesia Stock Exchange (IDX) and Yahoo Finance, the data were analyzed through multiple linear regression and moderated regression analysis (MRA). Earning Management (EM) was measured using the Modified Jones Model, Stock Price Synchronicity (SPS) through the Morck et al. approach, Financial Statement Fraud (FSF) via the Beneish M-Score, and profitability using Return on Assets (ROA). The statistical results indicate that neither Stock Price Synchronicity nor Financial Statement Fraud had a significant effect on Earning Management. Moreover, profitability did not significantly moderate the relationship between Stock Price Synchronicity and Earning Management, nor between Financial Statement Fraud and Earning Management. The main findings suggest that, within the context of the sampled Indonesian technology firms, there is no empirical evidence supporting the influence of Stock Price Synchronicity and Financial Statement Fraud on Earning Management nor the moderating role of profitability, indicating the possible presence of other, more influential factors.

This study aims to develop a better Financial Statement Fraud (FSF) detection model by utilizing data from publicly available financial statements of firms in the MENA region. We develop an FSF model using a powerful ensemble technique, the XGBoost (eXtreme Gradient Boosting) algorithm, that helps to identify fraud in a set of sample companies drawn from the Middle East and North Africa (MENA) region. The issue of class imbalance in the dataset is addressed by applying the Synthetic Minority Oversampling Technique (SMOTE) algorithm. We use different Machine Learning techniques in Python to predict FSF, and our empirical findings show that the XGBoost algorithm outperformed the other algorithms in this study, namely, Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), AdaBoost, and Random Forest (RF). We then optimize the XGBoost algorithm to obtain the best result, with a final accuracy of 96.05% in the detection of FSF.

The purpose of this study is to construct a valid and rigorous fraudulent financial statement detection model. The research objects are companies which experienced both fraudulent and non-fraudulent financial statements between the years 2002 and 2013. In the first stage, two decision tree algorithms, including the classification and regression trees (CART) and the Chi squared automatic interaction detector (CHAID) are applied in the selection of major variables. The second stage combines CART, CHAID, Bayesian belief network, support vector machine and artificial neural network in order to construct fraudulent financial statement detection models. According to the results, the detection performance of the CHAID–CART model is the most effective, with an overall accuracy of 87.97 % (the FFS detection accuracy is 92.69 %).

This study aims to analyze the influence of the fraud Pentagon elements on detecting financial statement fraud in BUMN listed on the Indonesia Stock Exchange (IDX) for 2020-2022. The study population was all state-owned enterprises BUMN listed on the Indonesia Stock Exchange. The sample was selected using a purposive sampling method, resulting in 18 companies. Data analysis used multiple regression analysis. The results showed that financial stability and personal financial needs were proven to have no significant effect on financial statement fraud. Financial targets, changes in director and auditor, and the number of CEO photos in the annual report showed a significant positive effect on financial statement fraud. Meanwhile, ineffective monitoring and dualism positions showed a significant negative effect. These findings indicate that several elements in the fraud pentagon framework significantly affect financial statement fraud in BUMN in Indonesia, with the model explaining 62.6% of the variance in fraud detection. ABSTRAK Penelitian ini bertujuan menganalisis pengaruh elemen fraud pentagon terhadap pendeteksian kecurangan laporan keuangan pada BUMN yang terdaftar di Bursa Efek Indonesia (BEI) periode 2020-2022. Populasi penelitian adalah seluruh badan usaha milik negara BUMN yang terdaftar pada Bursa Efek Indonesia. Sampel dipilih menggunakan purposive sampling method sehingga sejumlah 18 perusahaan. Analisis data menggunakan analisis regresi berganda. Hasil penelitian menunjukkan financial stability dan personal financial needs terbukti tidak berpengaruh signifikan terhadap kecurangan laporan keuangan. Financial targets, change in director, change in auditor, dan jumlah foto CEO dalam laporan tahunan menunjukkan pengaruh positif signifikan terhadap kecurangan laporan keuangan. Sementara itu, ineffective monitoring dan dualism position menunjukkan pengaruh negatif signifikan. Temuan ini mengindikasikan bahwa beberapa elemen dalam kerangka fraud pentagon secara signifikan mempengaruhi kecurangan laporan keuangan pada BUMN di Indonesia, dengan model menjelaskan 62,6% varians dalam pendeteksian fraud.

The purpose of this study is to detect the occurrence of fraudulent financial statements through the perspective of the fraud triangle in LQ45 companies listed on the Indonesia Stock Exchange in 2018-2020. Indications of financial statement fraud in this study are proxied by earnings management which is measured using the Modified Jones model. The method used in this research is literature study and documentation. The sample in this study was decided by using a purposive sampling method from LQ45 companies listed on the Indonesia Stock Exchange during 2018-2020, resulting in a total of 57 company observations. The data analysis used in this research is multiple linear regression analysis. The results of this study indicate that the pressure and rationalization variables have no partial effect on financial statement fraud. While the opportunity variable has no partial effect on financial statement fraud. Simultaneously, diamond fraud has a significant effect on financial statement fraud.

Financial statement fraud has been a serious concern for many investors and other stakeholders, and this motivated many researchers in developing traditional regression analysis in order to detect financial statement fraud. Intelligent financial statements fraud detection systems were continuously developed to uncover any potential for fraud signals which could assist the stakeholders in taking their decisions. This study reviews and identifies the gaps in the previous studies that tested financial statement fraud detection using data mining techniques. The study expands the knowledge of this research topic among researchers, forensic accountants, auditors and regulators and provides organizations with useful information regarding the various types of financial fraud and data mining techniques available.

This study aims to examine the effect of Fraud Hexagon on Financial Statement Fraud in mining companies listed on the Indonesia Stock Exchange (IDX). Fraud Hexagon consists of pressure, opportunity, rationalization, capability, arrogance, and collusion, while Financial Statement Fraud is measured using the Beneish M-Score. This study uses a quantitative approach with logistic regression analysis and purposive sampling techniques based on the companies' financial statements and annual reports for the observation period. The results show that simultaneously, the Fraud Hexagon influences Financial Statement Fraud, but partially, not all variables have a significant influence. These findings are expected to contribute to the detection and prevention of financial statement fraud in the mining secto. Keyword: Fraud Hexagon, Financial Statement Fraud, Beneish M-Score, Pertambangan.

The objective of this research is to analysis the effect of auditor characteristics that represented by professional scepticism and auditor experiences toward financial statement fraud detection through artificial intelligence in data analytic. This study builds upon previous research where respondents were auditors who had used big data analytics and incorporate artificial intelligence, specifically big data analytic as mediator between auditor characteristics and the detection of financial statement fraud. Quantitative research was used in this research by obtaining primary data through 66 respondents from auditor at several public accounting firms in the Jakarta area. Our findings reveal that auditor experience has direct influence effect on financial statement fraud detection while professional scepticism has no effect. But surprisingly, the role of artificial intelligence in data analytics is only able to mediate professional scepticism on financial statement fraud detection, while this does not apply to auditor experience. This study provides empirical evidence that the role of artificial intelligence, namely big data analytic, can act as a catalyst that transforms professional scepticism from mere doubt to evidence-based action, by enhancing the auditor’s ability to identify data patterns and anomalies, reducing cognitive bias and increasing objectivity.

This study aimed to determine: (1) the influence of Independent Commisioner on Fraudulent Financial Statement, (2) the influence of Audit Committee on Fraudulent Financial Statement, (3) the influence of Managerial Ownership on Fraudulent Financial Statement, (4) the influence of Institusional Ownership on Fraudulent Financial Statement, (5) the influence of Independent Commisioner, Audit Commitee, Managerial Ownership and Institusional Ownership on Fraudulent Financial Statement with earnings management as moderating variable. Population used in this study is manufacture company listed on BEI. The samples used purposive sampling, because observations obtained this study 132 (3 years. Data Analysis conducted by SPSS version 21.0 . The result of this study is Independent Commisioner, Audit Commitee, Managerial Ownership and Institusional Ownership have influence to Fraudulent Financial Statement but not signifficant and Earnings Management become moderating variable between the Independent Commisioner, Audit Commitee, Managerial Ownership and Institusional Owenership to Fraudulent Financial Statements, but not signifikan too.

Financial statement fraud has been one of the biggest challenges in the modern business world. Financial accounting fraud detection (FAFD) has become an emerging topic of great importance for academic, research and industries. In this paper, the effectiveness of Data Mining (DM) classification techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS are explored. Our study investigates the usefulness of Data Mining techniques including Decision Trees, Neural Networks and Bayesian Belief Networks in the identification of fraudulent financial statements. At last, we compare the three models in terms of their performances.

The purpose of this study aims to detect financial statement fraud in property and real estate companies listed on the Indonesia Stock Exchange from 2015 to 2020. The type of data in this study is secondary data. The sampling method used is purposive sampling with a total sample of 43 companies. The data analysis technique uses the Beneish Model which consists of the Beneish Ratio Index and the Beneish M-Score. The results showed that there were 21 companies indicated as manipulators in 2016, 26 companies indicated as manipulators in 2017, 21 companies indicated as manipulators in 2018, 17 companies indicated as manipulators in 2019, and 14 companies indicated as manipulators in 2020. There were 22 companies indicated as non-manipulators in 2016, 17 companies indicated as non-manipulators in 2017, 22 companies indicated as non-manipulators in 2018, 26 companies indicated as non-manipulators in 2019, and 29 companies indicated as non-manipulators in 2020. There are no companies indicated as gray companies. The results also show that property and real estate companies listed on the Indonesia Stock Exchange experience earning manipulation, and the growth rate of financial statement fraud has decreased from 2016 to 2020. Keywords: financial statement fraud, beneish ratio index, beneish m-score, earning manipulation.

Financial statement fraud has been a difficult problem for both the public and government regulators, so various data mining methods have been used for financial statement fraud detection to provide decision support for stakeholders. The purpose of this study is to propose an optimized financial fraud detection model combining feature selection and machine learning classification. The study indicated that random forest outperformed the other four methods. As to two feature selection methods, Xgboost performed better. And according to our research, 2 or 5 variables are more acceptable for models in this paper.

This study aims to examine the influence of external pressure, financial target, ineffective monitoring, rationalization, and capability on financial statement fraud. Financial Statement Fraud is measured by earnings management using discretionary accruals. This study uses secondary data were taken from the manufacturing companies which are listed in Indonesian Stock Exchange. Sample selection is done by purposive sampling method and sample obtained from this research were 45 of a total 135 companies. Method of analysis used multiple linear regression analysis. The result show that simultanoeusly of external pressure, financial target, ineffective monitoring, rationalization, and capability have a significant influence on financial statement fraud. Partially, external pressure, financial target, rationalization, and have no significant influence on financial statement fraud. Ineffective monitoring partially has a significant influence on financial statement fraud.