• The Journal of Industrial Distribution & Business
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• v.13 no.1
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• pp.1-9
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• 2022

Purpose: This study aims to find empirical evidence and clarity on the phenomenon of the direct and indirect effect of perceived behavioral control on fraud prevention through whistleblowing intention. This study also aims to understand the influence of organizational ethical culture moderating between whistleblowing intention and fraud prevention. Research design, data, methodology: The samples of this research are 236 respondents consisting of the Head of the Finance Subdivision and Head of the Reporting Planning Subdivision and the finance staff who were determined using the purposive sampling method. The data obtained were analyzed using the Structural Equation Modeling technique. Results: The study results show that perceived behavioral control positively and significantly affects whistleblowing intention. In addition, perceived behavioral control does not affect fraud prevention mediated by whistleblowing intention. Furthermore, organizational ethical culture moderates whistleblowing intention and has a positive and significant effect on fraud prevention. Conclusions: This study concludes that the phenomenon of scandal that often occurs on a television is not a habit that must be followed. It requires an active role from the community as a form of concern for whistleblowing. Futher researchers can add other construct variables, such as good corporate governance to assess the performance improvement of the organizational layers, both internally and externally

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.529-536
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• 2023

The secondhand market in Korea has been showing steady growth. However, the number of fraud cases and the amount of damages from fraudulent activities in secondhand transactions are also increasing. As of 2021, the size of the secondhand market reached 24 trillion won, but the total amount of fraud-related damages reached 360.6 billion won. In order to prevent fraud between individuals, secondhand trading platforms have implemented a safety payment system. However, new types of fraud methods exploiting the safety payment system have emerged, undermining the security of secondhand transaction safety payments. In this study, we aim to utilize text mining to examine the current state of the safety payment system in secondhand transactions and propose improvement measures by analyzing the system through text mining and network analysis.

• 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.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• Communications for Statistical Applications and Methods
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• v.17 no.1
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• pp.1-8
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• 2010

This study tested the applicability of a Data mining tool in the analysis of massive National Pension data for the purpose of developing fraud pension payment prediction model. This study is identified significant variables for fraud pension payment through the statistical analysis process and developed prediction models using data mining methodology.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.400-405
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• 2001

This paper addresses an effective approach of training neural networks classifiers for credit card fraud detection. The proposed approach uses evolutionary programming to trails the neural networks classifiers based on maximization of the detection rate of fraudulent usages on some ranges of the rejection rate, loot minimization of mean square error(MSE) that Is a common criterion for neural networks learning. This approach enables us to get classifier of satisfactory performance and to offer a directive method of handling various conditions and performance measures that are required for real fraud detection applications in the classifier training step. The experimental results on "real"credit card transaction data indicate that the proposed classifiers produces classifiers of high quality in terms of a relative profit as well as detection rate and efficiency.

• ETRI Journal
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• v.31 no.1
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• pp.92-94
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• 2009

We propose a system for mobile-phone fraud detection based on a bidirectional artificial neural network (bi-ANN). The key advantage of such a system is the ability to detect fraud not only by offline processing of call detail records (CDR), but also in real time. The core of the system is a bi-ANN that predicts the behavior of individual mobile-phone users. We determined that the bi-ANN is capable of predicting complex time series (Call_Duration parameter) that are stored in the CDR.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.200-206
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• 2021

Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models.

• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.306-311
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• 2023

As digital advertising continues to dominate the advertising industry and experience substantial growth, associated criminal activities such as advertising fraud and brand safety risks have become increasingly prevalent. Despite the severity and extent of these crimes, the response in Korea has been lackluster, often perceiving them merely as losses in advertising expenses incurred by corporations. However, it is important to note that these issues have direct repercussions on the end-consumer in the form of increased prices for goods and services. Furthermore, illegal and intrusive advertisements not only cause inconvenience to the viewer, but may also indiscriminately target cognitively vulnerable groups, such as children and the elderly, with the intention of manipulating advertising metrics and artificially inflating performance indicators. In this study, we aim to explore the concept and significance of advertising fraud and brand safety, and to evaluate the current measures taken in the Korean market. Additionally, we will delve into the implications of related policies and emphasize the necessity of digital advertising literacy in addressing these issues.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.858-861
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• 2011

최근 VoIP 서비스는 IP 네트워크의 안정화를 기반으로 국내 기업 Legacy PSTN 시장을 빠르게 대체해 가고 있다. 그러나 VoIP 서비스는 기존 인터넷망에서 발생 할 수 있는 보안 취약성 뿐 아니라 인터넷 전화 트래픽의 통과 문제 및 VoIP 스팸이나 도청 같은 기존에 없었던 새로운 이슈들을 발생 시키고 있다. 특히 SIP 인증 취약점을 이용한 Fraud Call 공격은 VoIP 서비스 사용자로 하여금 원하지 않은 호 및 과금을 대량 발생 시키는 공격기법으로 최근 기업의 피해사례가 늘어 나고 있다. 본 논문은 Fraud Call의 공격 기법을 분석하고, 호 인증 측면에서의 보안적 대응방안을 기술하고자 한다.