Payment card fraud detection using neural network committee and clustering

Bekirev, A. S.; Klimov, Valentin; Kuzin, M. V.; Shchukin, Boris A.

doi:10.3103/s1060992x15030030

Cited by 19 publications

(7 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Shen et al proposed a deep learning model composed of a long-short-term-memory (LSTM) network and the adaptive boosting (AdaBoost) algorithm for evaluating credit risk and applied an improved synthetic minority oversampling technique (SMOTE) to address dataset imbalances effectively [13]. Bekirev et al proposed the Financial Service scenario Deep learning based Behavior data representation method for Clustering (FinDeepBehaviorCluster) approach for detecting fraudulent e-commerce transactions using unstructured behavioral sequence data [14]. Wassan et al evaluated the detection performances of fraudulent transaction detection methods based on various machine learning algorithms [15], including SVM, k-nearest neighbors clustering, RF, decision tree (DT), and a multilayer perceptron (MLP) network, when applied to large e-commerce and e-banking datasets, and the method based on the MLP network attained the highest precision of 97%.…”

Section: Related Researchmentioning

confidence: 99%

Enhanced E-commerce Fraud Prediction Based on a Convolutional Neural Network Model

Xie¹,

Liu²,

Sun³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

The rapidly escalating sophistication of e-commerce fraud in recent years has led to an increasing reliance on fraud detection methods based on machine learning. However, fraud detection methods based on conventional machine learning approaches suffer from several problems, including an excessively high number of network parameters, which decreases the efficiency and increases the difficulty of training the network, while simultaneously leading to network overfitting. In addition, the sparsity of positive fraud incidents relative to the overwhelming proportion of negative incidents leads to detection failures in trained networks. The present work addresses these issues by proposing a convolutional neural network (CNN) framework for detecting ecommerce fraud, where network training is conducted using historical market transaction data. The number of network parameters reduces via the local perception field and weight sharing inherent in the CNN framework. In addition, this deep learning framework enables the use of an algorithmiclevel approach to address dataset imbalance by focusing the CNN model on minority data classes. The proposed CNN model is trained and tested using a large public e-commerce service dataset from 2018, and the test results demonstrate that the model provides higher fraud prediction accuracy than existing state-of-the-art methods.

show abstract

Section: Related Researchmentioning

confidence: 99%

Enhanced E-commerce Fraud Prediction Based on a Convolutional Neural Network Model

Xie¹,

Liu²,

Sun³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…As emerging of new technologies, new innovations are inevitable for detecting the fraudulent transaction because fraudsters are committing fraudulent transaction in a new way every time. The various techniques are used to find the fraudulent transaction such as hidden Markov model (Agrawal et al, 2015;Prakash and Chandrasekar, 2015;Dhok and Bamnote, 2012;Prakash and Chandrasekar, 2012;Khan et al, 2014b), artificial neural network (Behera and Panigrahi, 2015;Bekirev et al, 2015;Carneiro et al, 2015;Van Vlasselaer et al, 2015;Khan et al, 2014a;Brause et al, 1999;Aleskerov et al, 1997), big data (Chen et al, 2015), genetic (Assis et al, 2014;Ramakalyani and Umadevi, 2012;Duman and Ozcelik, 2011), artificial immune system (Halvaiee and Akbari, 2014;Soltani et al, 2012), Bayesian (Singh and Singh, 2015;Renuga et al, 2014;Panigrahi et al, 2009), migrating bird optimization , data mining (Philip and Sherly, 2012) and others (Sánchez et al, 2009;Quah and Sriganesh, 2008;Bentley et al, 2000;Ganji and Mannem,2012). Every methodology has its own characteristics with their Automated fraud detection techniques advantages and disadvantages.…”

Section: Fraud Detection Techniquesmentioning

confidence: 99%

Parameters of automated fraud detection techniques during online transactions

Khattri

Singh

2018

JFC

View full text Add to dashboard Cite

Purpose This paper aims to provide information of parameters and techniques used in the automated fraud detection system during online transaction. With the increase in the use of online transactions, the concerns regarding data security have also increased. To tackle the frauds, lot of research has been done and plethora of papers are available on the related topics. The purpose of this paper is to provide the clear pathway for researchers to move in the direction of development of automated fraud detection system to prevent the fraud during online transaction. Design/methodology/approach This literature review analyses and compares the different types of techniques for detecting fraud during online transaction. An in-depth study of the most prominent journals has been done and the core methodology of the papers has been presented. This article also shed some light on different types of parameters used in fraud detection techniques during online transaction. Findings There are vast varieties of various fraud detection techniques, and every technique has completed task in its own way. After studying approximately 41 research papers, 14 books and four reports, in total 30 parameters have been identified and a detailed study of the parameters has been presented. The parameters are also listed with their details that how these parameters are used in the security system for detecting online transaction fraud. Research limitations/implications This paper provides empirical insight about the parameters and their prominence in the development of automated fraud detection security system of online transaction. This paper encourages the researchers to development of improved fraud detection system. Practical implications This paper will pave the way for researchers to do a focused research on the fraud detection methodologies. The analysis will help in zeroing down the most prevalent topic of research in this field. The researchers will be able to understand the internal details of parameters and techniques used in the fraud detection systems. This literature also helps the research to think in a variety of ways that how these parameters will be used in the development of fraud detection system. Originality/value This paper is one of the most comprehensive reviews in its field. It tries and attempts to fill a void created because of lack of compilation of the laid fraud detection parameters.

show abstract

“…Other two supervised approaches are [44], and [3]. The first proposes a dynamic model which is updated with a sliding window approach.…”

Section: Related Workmentioning

confidence: 99%

“…To solve our optimization problem, we formalized it with the "AMPL modeling language" 3 . As solver we used the "IBM CPLEX solver" 4 thakt exploits the well known simplex method to solve optimization problems.…”

Section: Definition 61 (Mimicry Attack)mentioning

confidence: 99%

Security Evaluation of a Banking Fraud Analysis System

Carminati

Polino

Continella

et al. 2018

ACM Trans. Priv. Secur.

View full text Add to dashboard Cite

The significant growth of banking frauds, fueled by the underground economy of malware, raised the need for effective detection systems. Therefore, in last the years, banks have upgraded their security to protect transactions from frauds. State-of-the-art solutions detect frauds as deviations from customers' spending habits. To the best of our knowledge, almost all existing approaches do not provide an in-depth model's granularity and security analysis against elusive attacks. In this paper, we examine Banksealer, a decision support system for banking fraud analysis, evaluating the influence on the detection performance of the granularity at which the spending habits are modeled and its security against evasive attacks. First, we compare user-centric modeling, which builds a model for each user, with system-centric modeling, which builds a model for the entire system, from the point of view of the detection performance. Then, we assess the robustness of Banksealer against malicious attackers that are aware of the structure of the models in use. To this end, we design and implement a proof-of-concept attack tool that performs mimicry attacks, emulating a sophisticated attacker that cloaks frauds to avoid detection. We experimentally confirm the feasibility of such attacks, their cost and the effort required to an attacker in order to perform them. In addition, we discuss possible countermeasures. We provide a comprehensive evaluation on a large, real-world dataset obtained from one of the largest Italian banks.

show abstract

Payment card fraud detection using neural network committee and clustering

Cited by 19 publications

References 3 publications

Enhanced E-commerce Fraud Prediction Based on a Convolutional Neural Network Model

Enhanced E-commerce Fraud Prediction Based on a Convolutional Neural Network Model

Parameters of automated fraud detection techniques during online transactions

Security Evaluation of a Banking Fraud Analysis System

Contact Info

Product

Resources

About