scholarly journals Detection of Financial Statement Fraud Using Deep Learning for Sustainable Development of Capital Markets under Information Asymmetry

2021 ◽  
Vol 13 (17) ◽  
pp. 9879
Author(s):  
Chyan-Long Jan
Keyword(s):  
Deep Learning ◽  
Information Asymmetry ◽  
Capital Markets ◽  
Short Term Memory ◽  
Performance Indicator ◽  
Financial Data ◽  
Financial Statement ◽  
Financial Statement Fraud ◽  
Financial Reports ◽  
Financial Status

Information asymmetry is everywhere in financial status, financial information, and financial reports due to agency problems and thus may seriously jeopardize the sustainability of corporate operations and the proper functioning of capital markets. In this era of big data and artificial intelligence, deep learning is being applied to many different domains. This study examines both the financial data and non-financial data of TWSE/TEPx listed companies in 2001–2019 by sampling a total of 153 companies, consisting of 51 companies reporting financial statement fraud and 102 companies not reporting financial statement fraud. Two powerful deep learning algorithms (i.e., recurrent neural network (RNN) and long short-term memory (LSTM)) are used to construct financial statement fraud detection models. The empirical results suggest that the LSTM model outperforms the RNN model in all performance indicators. The LSTM model exhibits accuracy as high as 94.88%, the most frequently used performance indicator.

scholarly journals Fraud Diamond Theory in Detecting Financial Reporting Manipulation: A Case of Mining Companies Listed on the Indonesia Stock Exchange

2021 ◽  
Vol 9 ◽  
pp. 61-68
Author(s):  
Mafudi ◽  
Atiek Sri Purwati ◽  
Agung Praptapa ◽  
Sugiarto ◽  
Yonatan Daya Persada
Keyword(s):  
Financial Reporting ◽  
Financial Stability ◽  
Stock Exchange ◽  
Financial Statement ◽  
Linear Regression Method ◽  
Financial Statement Fraud ◽  
Mining Sector ◽  
Financial Reports ◽  
Purposeful Sampling ◽  
Triangle Theory

Forensic accounting helps auditor in collecting information while conducting necessary assessment to discover fraud practice. One popular theory in the field is the fraud diamond theory. This study implements the theory to detect the existence of financial statement fraud on mining sector in Indonesia. The diamond fraud model as the enhancement of the triangle theory of fraud concerns budget priorities, financial stability, inefficient monitoring, adjustments to the auditor and changes to the manager. As a dependent variable, financial statement manipulation funded by income control is used. The sampling of 9 companies listed on the Indonesian Stock Exchange in the mining sector in 2017-2019 was chosen using purposeful sampling methods, resulting in 27 data observations. The data testing was performed by a multi-linear regression method. This study showed that financial targets and financial stability affect the occurrence of fraud in financial reports. Simultaneously, insufficient monitoring, auditing and change of the director have no impact on the financial statements.

scholarly journals THE VALUE OF RESTATEMENT TO FRAUD PREDICTION

2019 ◽  
Vol 20 (6) ◽  
pp. 1210-1237
Author(s):  
Shi Qiu ◽  
Hong-Qu He ◽  
Yuan-sheng Luo
Keyword(s):  
Balance Sheet ◽  
Financial Statements ◽  
Primary Objective ◽  
Financial Statement ◽  
Financial Statement Fraud ◽  
Financial Reports ◽  
Financial Report ◽  
Financial Restatements ◽  
Fraud Prediction ◽  
Different Characteristics

A financial report restatement reflects errors in the previous financial statement, and thus it increases investors’ doubt about the credibility of the financial statement. The primary objective of this paper is to examine whether restatement announcements imply increased fraud risks in Chinese firms in the context that up to one quarter of listed companies have restated their financial reports in China, and explore the implications of the content, severity and reasons for restatements with respect to fraud. In this paper, firms with financial restatements prove to be more likely to be labeled as fraudulent by regulators in China. Second, the following results also are revealed: (1) financial statements, except balance sheet restatements, provide insights into the revelation of fraudulent behaviors, (2) the severity of restatements is positively correlated with future fraud disclosures, and (3) restatements due to negligence are positively correlated with future fraud occurrences. These results imply that restatement announcements and their different characteristics provide important information for detecting financial statement fraud.

scholarly journals Impact of Intellectual Capital on Earnings Management: Financial Statement Fraud In Indonesia

2021 ◽  
Vol 04 (06) ◽  
Author(s):  
I Made Laut Mertha Jaya ◽  
Keyword(s):  
Intellectual Capital ◽  
Value Added ◽  
Financial Statement ◽  
Test Model ◽  
Financial Statement Fraud ◽  
Financial Reports ◽  
Structural Capital ◽  
Capital Efficiency ◽  
Feasibility Test ◽  
Research Criteria

Many previous studies have interpreted the concept of intellectual capital based on one's values. However, if the company's management uses their intelligence intellectually to commit fraud by manipulating earnings in financial reports, this condition is certainly interesting to discuss further. This research method includes comparative quantitative. The research test applied descriptive statistics, normality test, multicollinearity test, t test, model feasibility test (F test) and multiple linear regresssscions. A total of 70 companies were in accordance with the research criteria during the 11 years of observation. Based on the numbers, the observations were made on 770 data. The results of this study concluded that intellectual capital as measured by using the value added method of intellectual capital (capital employed efficiency, human capital efficiency and structural capital efficiency) was proven to have a significant effect on earning management behavior or financial statement fraud in the company's financial statements.

Feature Selection for Dimension Reduction of Financial Data for Detection of Financial Statement Frauds in Context to Indian Companies

2020 ◽  
pp. 097215092092866
Author(s):  
Sonika Gupta ◽  
Sushil Kumar Mehta
Keyword(s):  
Feature Selection ◽  
Selection Process ◽  
Fraud Detection ◽  
Original Data ◽  
Financial Data ◽  
Financial Statement ◽  
Financial Fraud ◽  
Financial Statement Fraud ◽  
Financial Variables ◽  
Input Dataset

The financial fraud detection problem involves analysis of the large financial datasets. Financial statement fraud detection process is concentrated on two major aspects: first, identification of the financial variables and ratios, also termed as features. Second, applying the data mining methods to classify the organizations into two broad categories: fraudulent and non-fraudulent organizations. If the input dataset contains large number of irrelevant and correlated features, the computational load of the machine learning technique increases and the effectiveness of the classification outcomes decreases. The feature selection process selects a subset of most significant attributes or variables that can be the representative of original data. This selected subset can help in learning the pattern in data at much less time and with accuracy, in order to produce useful information for decision-making. This article briefly states the methods applied in the prior studies for selecting the features for financial statement fraud detection. This article also presents an approach to feature selection using correlation-based filter selection methods in which feature selection is performed based on ensemble model, and tests the outcome of the approach by applying the mean ratio analysis on financial data of Indian companies.

scholarly journals Improving Deep Learning for Forecasting Accuracy in Financial Data

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Shih-Lin Lin ◽  
Hua-Wei Huang
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Real Data ◽  
Financial Data ◽  
Complex Nature ◽  
Intrinsic Mode Functions ◽  
Financial Forecasting ◽  
Analytical Results ◽  
The Future ◽  
The Time Domain

Financial forecasting is based on the use of past and present financial information to make the best prediction of the future financial situation, to avoid high-risk situations, and to increase benefits. Such forecasts are of interest to anyone who wants to know the state of possible finances in the future, including investors and decision-makers. However, the complex nature of financial data makes it difficult to get accurate forecasts. Artificial intelligence, which has been shown to be suitable for analyzing very complex problems, can be applied to financial forecasting. Financial data is both nonlinear and nonstationary, with broadband frequency features. In other words, there is a large range of fluctuation, meaning that predictions made only using long short-term memory (LSTM) are not enough to ensure accuracy. This study uses an LSTM model for analysis of financial data, followed by a comparison of the analytical results with the actual data to see which has a larger root-mean-square-error (RMSE). The proposed method combines deep learning with empirical mode decomposition (EMD) to understand and predict financial trends from financial data. The financial data for this study are from the Taiwan corporate social responsibility (CSR) index. First, the EMD method is used to transform the CSR index data into a limited number of intrinsic mode functions (IMF). The bandwidth of these IMFs becomes narrower, with regular cyclic, periodic, or seasonal components in the time domain. In other words, the range of fluctuation is small. LSTM is a good way to forecast cyclic or seasonal data. The forecast result is obtained by adding all the IMFs together. It has been verified in past studies that only the LSTM and LSTM combined with the EMD can be used. The analytical results show that smaller RMSEs can be obtained using the LSTM combined with EMD compared to real data.

scholarly journals Volatility Forecasting for High-Frequency Financial Data Based on Web Search Index and Deep Learning Model

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 320
Author(s):  
Bolin Lei ◽  
Boyu Zhang ◽  
Yuping Song
Keyword(s):  
Deep Learning ◽  
Index System ◽  
High Frequency ◽  
Short Term Memory ◽  
Learning Model ◽  
Financial Data ◽  
Volatility Forecasting ◽  
Investor Attention ◽  
Search Index ◽  
Deep Learning Model

The existing index system for volatility forecasting only focuses on asset return series or historical volatility, and the prediction model cannot effectively describe the highly complex and nonlinear characteristics of the stock market. In this study, we construct an investor attention factor through a Baidu search index of antecedent keywords, and then combine other trading information such as the trading volume, trend indicator, quote change rate, etc., as input indicators, and finally employ the deep learning model via temporal convolutional networks (TCN) to forecast the volatility under high-frequency financial data. We found that the prediction accuracy of the TCN model with investor attention is better than those of the TCN model without investor attention, the traditional econometric model as the generalized autoregressive conditional heteroscedasticity (GARCH), the heterogeneous autoregressive model of realized volatility (HAR-RV), autoregressive fractionally integrated moving average (ARFIMA) models, and the long short-term memory (LSTM) model with investor attention. Compared with the traditional econometric models, the multi-step prediction results for the TCN model remain robust. Our findings provide a more accurate and robust method for volatility forecasting for big data and enrich the index system of volatility forecasting.

Deep learning for detecting financial statement fraud

2020 ◽  
Vol 139 ◽  
pp. 113421
Author(s):  
Patricia Craja ◽  
Alisa Kim ◽  
Stefan Lessmann
Keyword(s):  
Deep Learning ◽  
Financial Statement ◽  
Financial Statement Fraud

scholarly journals Pengaruh Fraud Pentagon terhadap Kecurangan Laporan Keuangan dengan Komite Audit sebagai Variabel Moderasi

2021 ◽  
Vol 3 (1) ◽  
pp. 15-30
Author(s):  
Afifah Sentani Rahma Nia Luhri ◽  
◽  
Ayunita Ajengtiyas S Mashuri ◽  
Husnah Nur Laela Ermaya ◽  
◽  
...  
Keyword(s):  
Quantitative Research ◽  
Audit Committee ◽  
Secondary Data ◽  
Financial Statements ◽  
Financial Statement ◽  
Annual Reports ◽  
Financial Statement Fraud ◽  
Financial Reports ◽  
Fraudulent Financial Statements ◽  
Positive Effect

Abstract Purpose: This study aims to determine the effect of the five components from fraud pentagon, namely pressure, opportunity, rationalization, competence, and arrogance moderated by the audit committee on fraudulent financial statements. Research Methodology: This research is a type of quantitative research using secondary data in the form of annual reports and company financial reports. The regression model used in this study is logistic regression which is processed using the STATA version 16. Results: The results of this study are pressure has a significant positive effect on fraudulent financial statements. Meanwhile, opportunities, rationalization, competence, and arrogance do not have an effect on fraudulent financial statements, besides that the audit committee also cannot moderate the effect of the pressure, opportunities, rationalization, competence, and arrogance on fraudulent financial statements. Limitations: The lack of supporting literature obtained by the authors regarding the audit committee that oversees management in the company is used as a moderating variable on the topic of financial statement fraud. Contribution: This study's results can be used as a reference for further researchers and take into consideration for company management, investors, and creditors in making decisions.

scholarly journals Literature Review: Research Reflection of Financial Statements Fraud Detection Methods in Indonesia

2021 ◽  
Vol 6 (4) ◽  
pp. 355-358
Author(s):  
Putri Intan Prastiwi ◽  
. Payamta
Keyword(s):  
Fraud Detection ◽  
Financial Statements ◽  
Financial Statement ◽  
Detection Methods ◽  
Fraud Triangle ◽  
Literacy Studies ◽  
Financial Statement Fraud ◽  
Literature Study ◽  
Financial Reports ◽  
Triangle Method

This study aims to identify methods in the detection of fraud in financial statements conducted by researchers in Indonesia. This research has been published on the website of the Ministry of Research and Technology with the SINTA 1 and SINTA 2 indexes. This research was conducted with a literature study on financial statement fraud in Indonesia. The research method used is a descriptive qualitative method by taking data from literacy studies on the research of fraud detection methods in Indonesia. The results of this study indicate that the fraud detection method used in financial reports in Indonesia is using the fraud Triangle method. The article of these studies is expected to provide input, insight, and information to all parties such as company management, auditors, and users of financial statements about various methods of detecting financial statement fraud in Indonesia.

Pengaruh Faktor Demografi Dan Iklim Organisasi Terhadap Niat Whistleblowing Internal

2017 ◽  
Vol 4 (1) ◽  
pp. 12-22
Author(s):  
Priyastiwi Priyastiwi
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Organizational Climate ◽  
Demographic Factors ◽  
Financial Statement ◽  
Research Use ◽  
Analysis Method ◽  
Financial Statement Fraud ◽  
Internal Whistleblowing ◽  
Collection Methods

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

Sign in / Sign up

Export Citation Format

Share Document