An Empirical Analysis of Fraud Detection in Online Auctions: Credit Card Phantom Transaction

Chae, Myungsin; Shim, Seonyoung; Cho, Hyungjun; Lee, Byungtae

doi:10.1109/hicss.2007.61

Cited by 10 publications

(8 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, Chae et al (2007) elucidate that online transactions are vulnerable to many types of fraudulent conduct because online marketplace easily attracts fraud as it originates from a unique characteristic of Internet transactions. Therefore, Chae et al (2007) equally support that the anonymity of the Internet may be preferred by honest traders for the protection of their privacy, while the opportunistic may take advantage of it and commit fraud. Therefore, Chang and Chang (2012) suggest that online consumers need a more proactive approach to protect their profits, such as an early fraud detection system.…”

Section: Perceived Fraud Risk and Investor Trustmentioning

confidence: 99%

Navigating selected perceived risk elements on investor trust and intention to invest in online trading platforms

Maziriri

Mapuranga

Madinga

2019

J. econ. financ. sci.

View full text Add to dashboard Cite

Orientation: In the contemporary global business environment, online trading is a new distribution channel and trading platforms are products of investment and financial services companies.Research purpose: This study determined the influence of perceived risk elements on investor trust and the intention to invest in online trading platforms among students at a selected university in South Africa.Motivation for the study: The significance of how perceived risk elements may influence an investor’s trust and the intention to invest in online trading has largely been overlooked in previous research. Therefore, the principal objective of this study was to address this research gap.Research approach, design and method: A structural equation modelling (SEM) procedure was applied to perform the hypotheses testing using the AMOS (version 25.0) package.Main findings: All the perceived risk elements had a positive and a significant impact on investor trust as well as the intention to invest in online trading platforms.Practical/managerial implications: This study provides fruitful implications to practitioners. The collective knowledge on dimensions of perceived risk will be advantageous to managers in understanding consumer risk perception and how students attempt to reduce these risks. Hence, this will also help managers to implement risk-reduction strategies in order to increase the prospect of students investing in their online trading platforms.Contribution/value-add: This study stands to add new knowledge to the present body of investment management and financial management literature in Africa – a context that has not received much research attention in developing countries.

show abstract

Section: Perceived Fraud Risk and Investor Trustmentioning

confidence: 99%

Navigating selected perceived risk elements on investor trust and intention to invest in online trading platforms

Maziriri

Mapuranga

Madinga

2019

J. econ. financ. sci.

View full text Add to dashboard Cite

show abstract

“…Fraud detection through data mining has been researched for several years in various ways. For example, by utilizing a Neural Network algorithm [4], the Dempster-Shafer theory and Bayesian Learning algorithms [5], a Self-organizing Maps algorithm [6], classification models [7], web service collaboration [8], and empirical analysis [9]. Furthermore, fraud detection through process mining has been done utilizing control flow analysis, role analysis and performance analysis [3,10,11], hybrid Association Rule Learning (ARL) and process mining [12], and fuzzy Multi Attribute Decision Making [13].…”

Section: Introductionmentioning

confidence: 99%

Increasing Accuracy of Process-based Fraud Detection Using a Behavior Model

Huda¹,

Sarno²,

Ahmad³

2016

IJSEIA

View full text Add to dashboard Cite

Process-based fraud (PBF) is fraud caused by deviation from a business process model. Some studies have proposed methods for PBF detection; however, these are still not able to fully detect the occurrence of fraud. In this context, we propose a new method of PBF detection which carries out the behavior of the originators (users who perform events) to adjust the levels of fraud occured in the events. In this research, we propose a method of PBF detection with behavior model in order to increase accuracy. This is done firstly by analyzing the business processes that correspond to those in the standard operating system (SOP). Secondly, by calculating the event execution performed by the originator and his/her relations within the organization, whose behavior is then analyzed. Thirdly, by using the number of deviations and the originator behavior to calculate the attribute value. By using attribute importance weights, an attribute rating of each originator is kept. Finally, Multi Attribute Decision Making is used to decide the PBF rating of a case, on the basis of which it is decided whether fraud occurred or not. The experimental results show that this behavior model is able to reduce false positive and false negative, therefore, the method can increase the accuracy level by 0.01.

show abstract

“…Taobao shows great interest in solving fraud transactions. Some studies have been made on auction fraud, such as the fraud types [27], motivations behind fraud [68], the influence of bogus websites [25], Chae et al [14] studied on the consequence of fraud detection in online transaction community, implicit behavioral evidences [34], and using supervised learning to detect online auction collusions [66]. There are some researches in fraud detection.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Product Characteristics for Online Collusive Detection in Big Data Transactions

Luo

Wan

2019

IEEE Access

View full text Add to dashboard Cite

Online fraud transaction has been a big concern for e-business platform. As the development of big data technology, e-commerce users always evaluate the sellers according to the reputation scores supplied by the platform. The reason why the sellers prefer chasing high reputation scores is that high reputations always bring high profit to sellers. By collusion, fraudsters can acquire high reputation scores and it will attract more potential buyers. It has been a crucial task for the e-commerce website to recognizing the fake reputation information. E-commerce platforms try to solve this continued and growing problem by adopting data mining techniques. With the high development of the Internet of Things, big data plays a crucial role in economic society. Big data brings economic growth in different domains. It supplies support to the management and decision-making ability in e-business through analyzing operational data. In online commerce, the big data technology also helps in providing users with a fair and healthy reputation system, which improves the shopping experience. This paper aims to put forward a conceptual framework to extract the characteristics of fraud transaction, including individual-and transaction-related indicators. It also contains two product features: product type and product nature. The two features obviously enhance the accuracy of fraud detection. A real-world dataset is used to verify the effectiveness of the indicators in the detection model, which is put forward to recognize the fraud transactions from the legitimate ones.

show abstract

An Empirical Analysis of Fraud Detection in Online Auctions: Credit Card Phantom Transaction

Cited by 10 publications

References 14 publications

Navigating selected perceived risk elements on investor trust and intention to invest in online trading platforms

Navigating selected perceived risk elements on investor trust and intention to invest in online trading platforms

Increasing Accuracy of Process-based Fraud Detection Using a Behavior Model

Leveraging Product Characteristics for Online Collusive Detection in Big Data Transactions

Contact Info

Product

Resources

About