Finding the needle: A risk-based ranking of product listings at online auction sites for non-delivery fraud prediction

Almendra, Vinicius

doi:10.1016/j.eswa.2013.02.027

Cited by 26 publications

(7 citation statements)

References 13 publications

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“…The account creation date was expected to be a relevant predictor, as past research relates this characteristic to trustworthiness [82]. We can also interpret that Twitter early adopters are more experienced in similar peer-to-peer platforms.…”

Section: Prediction Modelmentioning

confidence: 77%

“…Based on these frameworks, for the sake of this work, we have processed the following features, as shown in Table 1: • Profile: we selected the account creation date (in seconds since epoch) based on the hypothesis that users with active accounts for a long time are likely to be more trustable [82]. • Behavior: based on previous research [83,84], we have selected activity metrics: most frequent tweeting hours, tweets count, number of tweets marked as favorites by the user, and the tweet average length.…”

Section: Feature Generationmentioning

confidence: 99%

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Predicting Reputation in the Sharing Economy with Twitter Social Data

Prada

Iglesias

2020

Applied Sciences

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In recent years, the sharing economy has become popular, with outstanding examples such as Airbnb, Uber, or BlaBlaCar, to name a few. In the sharing economy, users provide goods and services in a peer-to-peer scheme and expose themselves to material and personal risks. Thus, an essential component of its success is its capability to build trust among strangers. This goal is achieved usually by creating reputation systems where users rate each other after each transaction. Nevertheless, these systems present challenges such as the lack of information about new users or the reliability of peer ratings. However, users leave their digital footprints on many social networks. These social footprints are used for inferring personal information (e.g., personality and consumer habits) and social behaviors (e.g., flu propagation). This article proposes to advance the state of the art on reputation systems by researching how digital footprints coming from social networks can be used to predict future behaviors on sharing economy platforms. In particular, we have focused on predicting the reputation of users in the second-hand market Wallapop based solely on their users’ Twitter profiles. The main contributions of this research are twofold: (a) a reputation prediction model based on social data; and (b) an anonymized dataset of paired users in the sharing economy site Wallapop and Twitter, which has been collected using the user self-mentioning strategy.

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Section: Prediction Modelmentioning

confidence: 77%

Section: Feature Generationmentioning

confidence: 99%

Predicting Reputation in the Sharing Economy with Twitter Social Data

Prada

Iglesias

2020

Applied Sciences

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“…Malpractice can also take place through non-delivery Fraud which has been acknowledged as a particular problem for online auction websites. In this type of fraud, an online offender will list a non-existent product for sale, receive payment for the goods and later disappear without any trace (Almendra, 2013; Van Wilsem, 2011). The FBI recorded in its 2019 IC3 report 61,832 complaints from victims of non-delivery fraud with accumulated losses totalling $196,563,497.…”

Section: Cybercrime and Non-delivery Fraudmentioning

confidence: 99%

Understanding pet scams: A case study of advance fee and non-delivery fraud using victims’ accounts

Whittaker

Button

2020

Australian & New Zealand Journal of Criminology

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Advance fee and non-delivery frauds have become very common with the growing preference for online shopping and the new opportunities this brings for online offenders. This article uses unique access to a volunteer group’s database focused on preventing pet scams to explore this type of crime. Distances, among other factors, make the purchase of pets online common in countries such as the USA, Australia and South Africa. This modality of purchase has been exploited by organized criminals largely based in Cameroon to conduct advance fee and non-delivery frauds. The article uses data from the volunteer group Petscams.com to provide unique insights on the techniques of the offenders with particular reference to the strategies used to maximize victimization by using real accounts of victims of such frauds. It also briefly notes how the COVID-19 crisis has been used to adapt this type of scam. The article’s discussion identifies the need for a more nuanced assessment into the role of victim oriented voluntary organizations.

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“…In contrast, network-level features describe the auction relationships between users, such as number of times a seller has a bidder in common with other sellers [33]. The majority of behaviorbased techniques focus only on user-level features [2,7,32,35], and do not consider network-level features. Obviously, network-level features alone are not effective for detecting sophisticated collusive auction frauds.…”

Section: Related Workmentioning

confidence: 99%

Incremental collusive fraud detection in large-scale online auction networks

et al. 2020

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An online auction network (OAN) is a community of users who buy or sell items through an auction site. Along with the growing popularity of auction sites, concerns about auction frauds and criminal activities have increased. As a result, fraud detection in OANs has attracted renewed interest from researchers. Since most real OANs are large-scale networks, detecting fraudulent users is usually difficult, especially when multiple users collude with each other and new online auctions are continuously added. Although collusive auction frauds are not as popular as other types of auction frauds, they are more horrible and catastrophic because they often bring huge financial losses. To tackle this issue, some techniques have been proposed to detect collusive frauds in OANs. While all of the techniques have demonstrated promising results, they often suffer from low detection performance or slow convergence, especially in large-scale OANs. In this paper, we overcome these deficiencies by presenting ICAFD, a novel technique that recasts the problem of detecting collusive frauds in large-scale OANs as an incremental semi-supervised anomaly detection problem. In this technique, we propagate reputations from a small set of labeled benign users to unlabeled users along the auction relationships between them and then incrementally update reputations when a new auction gets added to the OAN. This increases the convergence of ICAFD and allows it to avoid wasteful recalculation of reputations from scratch. Our experimental results show that ICAFD can successfully detect different types of collusive auction frauds in a reasonable detection time.

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Finding the needle: A risk-based ranking of product listings at online auction sites for non-delivery fraud prediction

Cited by 26 publications

References 13 publications

Predicting Reputation in the Sharing Economy with Twitter Social Data

Predicting Reputation in the Sharing Economy with Twitter Social Data

Understanding pet scams: A case study of advance fee and non-delivery fraud using victims’ accounts

Incremental collusive fraud detection in large-scale online auction networks

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