Incremental collusive fraud detection in large-scale online auction networks

2020 ◽  
Vol 76 (9) ◽  
pp. 7416-7437 ◽  
Author(s):  
Mahila Dadfarnia ◽  
Fazlollah Adibnia ◽  
Mahdi Abadi ◽  
Ali Dorri
Keyword(s):  
Large Scale ◽  
Fraud Detection ◽  
Online Auction

Two Step graph-based semi-supervised Learning for Online Auction Fraud Detection

2015 ◽  
pp. 165-179 ◽  
Author(s):  
Phiradet Bangcharoensap ◽  
Hayato Kobayashi ◽  
Nobuyuki Shimizu ◽  
Satoshi Yamauchi ◽  
Tsuyoshi Murata
Keyword(s):  
Supervised Learning ◽  
Fraud Detection ◽  
Online Auction

scholarly journals Online Credit Payment Fraud Detection via Structure-Aware Hierarchical Recurrent Neural Network

2021 ◽  
Author(s):  
Wangli Lin ◽  
Li Sun ◽  
Qiwei Zhong ◽  
Can Liu ◽  
Jinghua Feng ◽  
...  
Keyword(s):  
Large Scale ◽  
Structural Information ◽  
Critical Role ◽  
Fraud Detection ◽  
Web Pages ◽  
Intrinsic Structure ◽  
Structure Information ◽  
Multi Scale ◽  
Sequential Information ◽  
Scale Behavior

Online credit payment fraud detection plays a critical role in financial institutions due to the growing volume of fraudulent transactions. Recently, researchers have shown an increased interest in capturing users’ dynamic and evolving fraudulent tendencies from their behavior sequences. However, most existing methodologies for sequential modeling overlook the intrinsic structure information of web pages. In this paper, we adopt multi-scale behavior sequence generated from different granularities of web page structures and propose a model named SAH-RNN to consume the multi-scale behavior sequence for online payment fraud detection. The SAH-RNN has stacked RNN layers in which upper layers modeling for compendious behaviors are updated less frequently and receive the summarized representations from lower layers. A dual attention is devised to capture the impacts on both sequential information within the same sequence and structural information among different granularity of web pages. Experimental results on a large-scale real-world transaction dataset from Alibaba show that our proposed model outperforms state-of-the-art models. The code is available at https://github.com/WangliLin/SAH-RNN.

scholarly journals iBGP: A Bipartite Graph Propagation Approach for Mobile Advertising Fraud Detection

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Jinlong Hu ◽  
Junjie Liang ◽  
Shoubin Dong
Keyword(s):  
Bipartite Graph ◽  
Large Scale ◽  
Learning Algorithm ◽  
Mobile Apps ◽  
Fraud Detection ◽  
Weighted Graph ◽  
Power Law Distribution ◽  
Mobile Advertising ◽  
Initial Score ◽  
Graph Propagation

Online mobile advertising plays a vital financial role in supporting free mobile apps, but detecting malicious apps publishers who generate fraudulent actions on the advertisements hosted on their apps is difficult, since fraudulent traffic often mimics behaviors of legitimate users and evolves rapidly. In this paper, we propose a novel bipartite graph-based propagation approach, iBGP, for mobile apps advertising fraud detection in large advertising system. We exploit the characteristics of mobile advertising user’s behavior and identify two persistent patterns: power law distribution and pertinence and propose an automatic initial score learning algorithm to formulate both concepts to learn the initial scores of non-seed nodes. We propose a weighted graph propagation algorithm to propagate the scores of all nodes in the user-app bipartite graphs until convergence. To extend our approach for large-scale settings, we decompose the objective function of the initial score learning model into separate one-dimensional problems and parallelize the whole approach on an Apache Spark cluster. iBGP was applied on a large synthetic dataset and a large real-world mobile advertising dataset; experiment results demonstrate that iBGP significantly outperforms other popular graph-based propagation methods.

scholarly journals Online Auction Fraud Detection in Privacy-Aware Reputation Systems

Entropy ◽  
2017 ◽  
Vol 19 (7) ◽  
pp. 338 ◽  
Author(s):  
Jun-Lin Lin ◽  
Laksamee Khomnotai
Keyword(s):  
Fraud Detection ◽  
Reputation Systems ◽  
Online Auction

scholarly journals Extent Of Utilization Of Analytical Techniques And Susbsitantive Test By Accounting Staff For Fraud Detection In Large Scale Business Organisations In Delta State

2020 ◽  
Vol 7 (2) ◽  
pp. 48-58
Author(s):  
J. I. Ezenwafor ◽  
O. Frank Udukeke
Keyword(s):  
Large Scale ◽  
Rating Scale ◽  
Fraud Detection ◽  
Analytical Techniques ◽  
Sampling Technique ◽  
Analytical Technique ◽  
Point Rating Scale ◽  
Research Questions ◽  
Level Of Significance ◽  
Descriptive Survey

This study on extent of utilization of analytical techniques and susbsitantive test by accounting staff for fraud detection in large scale business organisations in Delta State was necessitated by the growing incidence frauds that are crippling businesses and socio-economic development of the state. Two research questions guided the study and two null hypotheses were tested. Related literature to the study were reviewed. Descriptive survey research design was adopted for the study. The population of the study was 260 accounting staff. A sample size of 160 was drawn for the study using stratified sampling technique. A four-point rating scale questionnaire developed by the researchers containing 23 items in two clusters was used for data collection.  Internal consistency method was used to determine the reliability of the questionnaire with Cronbach Alpha and this yielded reliability coefficient values of 0.91 and 0.84 respectively for the sections with an overall reliability of 0.88. Data were analyzed using mean and standard deviation to answer the research questions and determine the homogeneity of the respondents’ view while t-test and analysis of variance were used to test the hypotheses at 0.05 level of significance. The results showed that the accounting staff moderately utilized analytical technique for fraud detection but highly utilized substantive test.  Furthermore, it was found that types and status of organization in NSE did not significantly influence the respondents’ ratings. Based on the findings of the study, it was concluded that the accounting staff are not adequately utilizing techniques that could facilitate fraud detection in large-scale business organisations as required to combact the menance. It was therefore recommended among others, that, management of LSBOs should sponsor their accounting staff on training and that sharesholders should insist that the techniques are adequately utilized by holding the management accountable for future incidence of fraud.

Credit Card Fraud Detection Using K-Means and Fuzzy C-Means

2021 ◽  
pp. 216-240
Author(s):  
Arti Jain ◽  
Archana Purwar ◽  
Divakar Yadav
Keyword(s):  
Credit Card ◽  
Large Scale ◽  
Fraud Detection ◽  
Online Banking ◽  
Small Scale ◽  
Classification Rate ◽  
Credit Card Fraud ◽  
Fuzzy C Means ◽  
Fcm Clustering ◽  
F Measure

Machine learning (ML) proven to be an emerging technology from small-scale to large-scale industries. One of the important industries is banking, where ML is being adapted all over the world by employing online banking. The online banking is using ML techniques in detecting fraudulent transactions like credit card fraud detection, etc. Hence, in this chapter, a Credit card Fraud Detection (CFD) system is devised using Luhn's algorithm and k-means clustering. Moreover, CFD system is also developed using Fuzzy C-Means (FCM) clustering instead of k-means clustering. Performance of CFD using both clustering techniques is compared using precision, recall and f-measure. The FCM gives better results in comparison to k-means clustering. Further, other evaluation metrics such as fraud catching rate, false alarm rate, balanced classification rate, and Mathews correlation coefficient are also calculated to show how well the CFD system works in the presence of skewed data.

scholarly journals InfDetect: a Large Scale Graph-based Fraud Detection System for E-Commerce Insurance

2019 ◽  
Author(s):  
Cen Chen ◽  
Chen Liang ◽  
Jianbin Lin ◽  
Li Wang ◽  
Ziqi Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Detection System ◽  
Fraud Detection

scholarly journals Collaboration Based Multi-Label Propagation for Fraud Detection

2020 ◽  
Author(s):  
Haobo Wang ◽  
Zhao Li ◽  
Jiaming Huang ◽  
Pengrui Hui ◽  
Weiwei Liu ◽  
...  
Keyword(s):  
Theoretical Analysis ◽  
Large Scale ◽  
Fraud Detection ◽  
General Purpose ◽  
Label Propagation ◽  
Empirical Results ◽  
Challenging Issue ◽  
Label Correlations

Detecting fraud users, who fraudulently promote certain target items, is a challenging issue faced by e-commerce platforms. Generally, many fraud users have different spam behaviors simultaneously, e.g. spam transactions, clicks, reviews and so on. Existing solutions have two main limitations: 1) the correlations among multiple spam behaviors are neglected; 2) large-scale computations are intractable when dealing with an enormous user set. To remedy these problems, this work proposes a collaboration based multi-label propagation (CMLP) algorithm. We first introduce a general-purpose version that involves collaboration technique to exploit label correlations. Specifically, it breaks the final prediction into two parts: 1) its own prediction part; 2) the prediction of others, i.e. collaborative part. Then, to accelerate it on large-scale e-commerce data, we propose a heterogeneous graph based variant that detects communities on the user-item graph directly. Both theoretical analysis and empirical results clearly validate the effectiveness and scalability of our proposals.

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