• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3218-3241
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• 2023

Financial fraud undermines the sustainable development of financial markets. Financial statements can be regarded as the key source of information to obtain the operating conditions of listed companies. Current research focuses more on mining financial digital data instead of looking into text data. However, text data can reveal emotional information, which is an important basis for detecting financial fraud. The audit opinion of the financial statement is especially the fair opinion of a certified public accountant on the quality of enterprise financial reports. Therefore, this research was carried out by using the data features of 4,153 listed companies' financial annual reports and audits of text opinions in the past six years, and the paper puts forward a financial fraud detection model integrating audit opinions. First, the financial data index database and audit opinion text database were built. Second, digitized audit opinions with deep learning Bert model was employed. Finally, both the extracted audit numerical characteristics and the financial numerical indicators were used as the training data of the LightGBM model. What is worth paying attention to is that the imbalanced distribution of sample labels is also one of the focuses of financial fraud research. To solve this problem, data enhancement and Focal Loss feature learning functions were used in data processing and model training respectively. The experimental results show that compared with the conventional financial fraud detection model, the performance of the proposed model is improved greatly, with Area Under the Curve (AUC) and Accuracy reaching 81.42% and 78.15%, respectively.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.1163-1169
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• 2021

This study aims to obtain empirical evidence regarding the effect of the fraud pentagon theory on financial statement fraud. The novelty of this study is the use of factor analysis to consolidate the five elements of the fraud pentagon into just one factor, which, to the knowledge of the researcher, no one else has done to research the effect of pentagon fraud on financial statement fraud. This study uses both agency theory and fraud pentagon theory. The population of this study consists of state-owned companies listed on the Indonesia Stock Exchange. The research period in this study is from 2014 to 2019. The data used in this study is secondary data obtained from the company's annual financial statements. A purposive sampling technique was used to determine the research sample. The selected companies total 20. Factor analysis and simple linear regression analysis method were used as research the methods. Based on the research results, it was found that the fraud pentagon theory had a positive effect on the financial statement fraud of state-owned companies listed on the Indonesia Stock Exchange. High level of the pentagon fraud on a company leads to a higher indication of financial statement fraud.

• Journal of Distribution Science
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• v.21 no.12
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• pp.71-81
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• 2023

Purpose: This research aims to analyze the influence of greed, opportunity, need, exposes on fraudulence financial reporting by using the distribution of political connections as a moderating variable. Research design, data, methodology: Using data collected from 180 respondents who were leaders involved in financial reports in state-owned companies and manufacturing companies in South Sulawesi, Indonesia. Data analysis using SEM PLS. Results: The results of this research show that greed, opportunity, need, exposes, political connections have a significant positive effect on fraudulence financial reporting. Political connection is able to moderate greed, need, exposes to fraudulence financial reporting. Furthermore, political connections are unable to moderate the opportunity for fraudulence financial reporting in company. Conclusion: Greed, opportunities, needs, exposes can influence someone to carry out financial fraud reporting in the company because of internal or external factors that cause someone to commit fraud. Every perpetrator of fraud should be subject to punishment or sanctions if proven to have committed fraud. Political connections can influence fraudulent financial reporting due to the potential for intervention and political pressure that can affect the integrity of financial reporting. Political connections are able to moderate greed, need, exposes against fraudulent financial reporting.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.169-185
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• 2024

Due to levitate and rapid growth of E-Commerce, most of the organizations are moving towards cashless transaction Unfortunately, the cashless transactions are not only used by legitimate users but also it is used by illegitimate users and which results in trouncing of billions of dollars each year worldwide. Fraud prevention and Fraud Detection are two methods used by the financial institutions to protect against these frauds. Fraud prevention systems (FPSs) are not sufficient enough to provide fully security to the E-Commerce systems. However, with the combined effect of Fraud Detection Systems (FDS) and FPS might protect the frauds. However, there still exist so many issues and challenges that degrade the performances of FDSs, such as overlapping of data, noisy data, misclassification of data, etc. This paper presents a comprehensive survey on financial fraud detection system using such data mining techniques. Over seventy research papers have been reviewed, mainly within the period 2002-2015, were analyzed in this study. The data mining approaches employed in this research includes Neural Network, Logistic Regression, Bayesian Belief Network, Support Vector Machine (SVM), Self Organizing Map(SOM), K-Nearest Neighbor(K-NN), Random Forest and Genetic Algorithm. The algorithms that have achieved high success rate in detecting credit card fraud are Logistic Regression (99.2%), SVM (99.6%) and Random Forests (99.6%). But, the most suitable approach is SOM because it has achieved perfect accuracy of 100%. But the algorithms implemented for financial statement fraud have shown a large difference in accuracy from CDA at 71.4% to a probabilistic neural network with 98.1%. In this paper, we have identified the research gap and specified the performance achieved by different algorithms based on parameters like, accuracy, sensitivity and specificity. Some of the key issues and challenges associated with the FDS have also been identified.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.35-40
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• 2022

With the advent of the internet age and the outbreak of COVID-19, many companies have embraced online trade. However, due to the way the cyber economy works, the number of companies engaged in financial fraud by falsifying their transaction amounts and customer numbers has been gradually increasing. The purpose of this study is to analyze financial fraud of companies in the Internet era and to present solutions. Therefore, this study analyzed the financial fraud behavior of Luckin Coffee in China as an example and studied the causes and countermeasures of financial fraud. As a result, it was found that the cause of financial fraud lies in the opacity of cash flows from online transactions. The recommendations proposed by this study is to improve internal control systems in companies, develop risk management system, and establish comprehensive external supervision system

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.6
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• pp.1525-1540
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• 2015

Due to a rapid advancement in the electronic commerce technology, the payment method varies from cash to electronic settlement such as credit card, mobile payment and mobile application card. Therefore, financial fraud is increasing notably for a purpose of personal gain. In response, financial companies are building the FDS (Fraud Detection System) to protect consumers from fraudulent transactions. The one of the goals of FDS is identifying the fraudulent transaction with high accuracy by analyzing transaction data and personal information in real-time. Data mining techniques are providing great aid in financial accounting fraud detection, so it have been applied most extensively to provide primary solutions to the problems. In this paper, we try to provide an overview of the research on data mining based fraud detection. Also, we classify researches under few criteria such as data set, data mining algorithm and viewpoint of research.

• Korean Security Journal
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• no.53
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• pp.37-59
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• 2017

This study aims to contribute to the early detection of occupational fraud in the Korean financial industry by analyzing fraud symptoms. Firstly, the definition, cause of occupational fraud, and fraud symptoms were discussed through literature review. Secondly, survey data were collected from the employees of the financial industry such as bank, insurance, and securities companies to conduct statistical analysis. The result of analysis showed that the symptoms of 'excessive stock investment' and 'unsettled life style' were statistically significant predictors of fraud detection experience. Plus, 'tips and complaints' were the most frequent method for detecting occupational fraud in the Korean financial industry. The financial institutions can minimize the loss of occupational fraud by early detection through educating their employees and vendors on these important symptoms of occupational fraud.

• Korean Security Journal
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• no.56
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• pp.165-185
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• 2018

Because financial industry is closely related to the daily lives of people, internal fraud such as embezzlement by the employees can cause serious damage to the national economy, including credit crunch and contagious bankruptcy, as once demonstrated in the Savings Bank Scandal in 2011. Therefore, the importance of financial industry security is being emphasized and developed into converged security that combines physical, human and cyber security. In this study, to prevent fraud caused by internal employees in Korean financial sector, in-depth semi-structured interviews were conducted with a total of 16 participants including bankers, officials of financial regulators, and security experts, who were in charge of risk management in the industry. The collected data were analyzed at three stratification levels such as individual, organization, and socio-cultural factor. Based on this analysis, policy recommendations were suggested for the development of financial industry security and reducing internal fraud in banking institutions.

• Journal of the Korea Convergence Society
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• v.8 no.4
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• pp.19-24
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• 2017

The growing number of electronic financial transactions (e-banking) has entailed the rapid increase in security threats such as extortion and falsification of financial transaction data. Against such background, rigid security and countermeasures to hedge against such problems have risen as urgent tasks. Thus, this study aims to implement an improved case model by applying the Fraud Detection System (hereinafter, FDS) in a financial corporation 'A' using big data technique (e.g. the function to collect/store various types of typical/atypical financial transaction event data in real time regarding the external intrusion, outflow of internal data, and fraud financial transactions). As a result, There was reduction effect in terms of previous scenario detection target by minimizing false alarm via advanced scenario analysis. And further suggest the future direction of the enhanced FDS.

• Asia pacific journal of information systems
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• v.28 no.4
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• pp.308-319
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• 2018

This paper aims at providing valuable insights on Financial Fraud Detection on a mobile money transactional activity. We have predicted and classified the transaction as normal or fraud with a small sample and massive data set using Azure and Spark ML, which are traditional systems and Big Data respectively. Experimenting with sample dataset in Azure, we found that the Decision Forest model is the most accurate to proceed in terms of the recall value. For the massive data set using Spark ML, it is found that the Random Forest classifier algorithm of the classification model proves to be the best algorithm. It is presented that the Spark cluster gets much faster to build and evaluate models as adding more servers to the cluster with the same accuracy, which proves that the large scale data set can be predictable using Big Data platform. Finally, we reached a recall score with 0.73, which implies a satisfying prediction quality in predicting fraudulent transactions.