Relevant bibliographies by topics / Credit card fraud

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Credit card fraud'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 June 2025

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Journal articles on the topic "Credit card fraud"

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M.S, Prateeksha, B. Naga Swetha, and Manjula Patil. "CREDIT CARD FRAUD DETECTION USING MACHINE-LEARNING." International Journal of Advanced Research 11, no. 04 (2023): 1559–63. http://dx.doi.org/10.21474/ijar01/16824.

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The recent advances of e-commerce and e-payment systems have sparked an increase in financial fraud cases such as credit card fraud. It is therefore crucial to implement mechanisms that can detect the credit card fraud. Features of credit card frauds play important role when machine learning is used for credit card fraud detection, and they must be chosen properly. This paper proposes a machine learning (ML) based credit card fraud detection engine using ML classifiers: Decision Tree (DT), Logistic Regression (LR), Artificial Neural Network (ANN). To validate the performance, the proposed credit card fraud detection engine is evaluated using a dataset generated from European cardholders. The result demonstrated that our proposed approach outperforms existing systems.
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Banu, Muskan, and Prof Kavitha G. "Credit Card Fraud Detection Using Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 10, no. 10 (2022): 1018–23. http://dx.doi.org/10.22214/ijraset.2022.47127.

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Abstract: Credit Card Fraud can be defined as a case where a person uses someone else’s credit card for personal reasons while the owner and the card-issuing authorities are unawareof the fact that the card is being used. Credit card frauds are easy and friendly targets. E-commerce and many other online sites have increased the online payment modes, increasing the risk for online frauds. In the era of digitalization, the need to identify credit card frauds is necessary. Fraud detection involves monitoring and analyzing the behaviour of various users to estimate, detect or avoid undesirable behaviour. To identify credit card fraud detection effectively, we need to understand the various technologies, algorithms and types involved in detecting credit card frauds. The algorithm can differentiate transactions which are fraudulent or not. To find fraud, we need to pass dataset and knowledge of the fraudulenttransaction. Algorithms analyze the dataset and classify all transactions. Fraud detection involves monitoring the activitiesof populations of users to estimate, perceive or avoidobjectionable behaviour, which consist of fraud, intrusion, and defaulting. Machine learning algorithms are employed to analyse all the authorized transactions and report the suspicious ones. We have taken an imbalanced dataset of transactions to detect the frauds
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Bala, Bala Santhosh, Pasupula Praveen Yadav, and Mogathala Raghavendra Reddy. "An intelligent approach to detect and predict online fraud transaction using XGBoost algorithm." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (2024): 1491. http://dx.doi.org/10.11591/ijeecs.v35.i3.pp1491-1498.

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The most popular payment method in recent years is the credit card. Due to the E-commerce industry’s explosive growth, the usage of credit cards for online purchases have been greatly increased as a result frauds has increased. Banks have been facing challenges to detect the credit card system fraud in recent years. Credit card fraud happens when the card was stolen for any unauthorized purposes or if the fraudster utilizes the credit card information for his own use. In order to prevent credit card fraud, it is essential to build detection measures. While detecting credit card theft with machine learning (ML), the features of credit card frauds play an important and they must be carefully selected. A fraud detection algorithm must be created in order to correctly locate and stop fraudulent activity as technology advances along with the amount of fraud cases. ML methods are essential for identifying fraudulent transactions. The implementation of fraud detection models is particularly difficult because of the sensitive nature of the data, the unbalanced class distributions, and the lack of data. In this work, an intelligent approach to detect and predict online fraud transaction using extreme gradient boosting (XGBoost) algorithm is described. The XGBoost model predicts whether a transaction is fraud or not. This model will achieve better performance interarm of recall, precision, accuracy and F1-score for credit card fraud detection.
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Salian, Prof S. R. "Credit Card Fraudulent Transaction Detection and Prevention." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 3255–60. http://dx.doi.org/10.22214/ijraset.2023.50849.

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Abstract: In recent years, online payment methods have been used widely as an outcome of the rapid increase in non-cash and digital electronic transactions. Credit cards represent one of the electronic payment methods. With the advancement of online payments in various products and services, the likelihood of credit card fraud has risen compared to the decades-long history of credit cards. The credit card frauds can be detected by evaluating the credit card purchasing patterns using the historical data in order to detect the frauds. This data evaluation can help the banks or other organizations offering credit cards to minimize their losses due to the credit card frauds. The historical data evaluation with the current purchasing patterns requires statistical modeling, which can automatically evaluate the fraudulent patterns and alarm the banks about the transactions. This helps the banks for early detection of the frauds, where they can easily eliminate the credit card frauds by declining the suspected transactions. And also blockchain technology is applied to prevent the hacker to view customers details so that fraudsters can't use stolen credit card information to open new accounts, obtain loans, and engage in other illegal activities. Credit card fraud detection and prevention have become essential for banks and other financial institutions to safeguard their customers' financial transactions. This paper presents an overview of credit card fraud detection and prevention techniques.
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Singh, Ajeet, and Anurag Jain. "An Empirical Study of AML Approach for Credit Card Fraud Detection–Financial Transactions." International Journal of Computers Communications & Control 14, no. 6 (2019): 670–90. http://dx.doi.org/10.15837/ijccc.2019.6.3498.

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Credit card fraud is one of the flip sides of the digital world, where transactions are made without the knowledge of the genuine user. Based on the study of various papers published between 1994 and 2018 on credit card fraud, the following objectives are achieved: the various types of credit card frauds has identified and to detect automatically these frauds, an adaptive machine learning techniques (AMLTs) has studied and also their pros and cons has summarized. The various dataset are used in the literature has studied and categorized into the real and synthesized datasets.The performance matrices and evaluation criteria have summarized which has used to evaluate the fraud detection system.This study has also covered the deep analysis and comparison of the performance (i.e sensitivity, specificity, and accuracy) of existing machine learning techniques in the credit card fraud detection area.The findings of this study clearly show that supervised learning, card-not-present fraud, skimming fraud, and website cloning method has been used more frequently.This Study helps to new researchers by discussing the limitation of existing fraud detection techniques and providing helpful directions of research in the credit card fraud detection field.
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Venkata Suryanarayana, S., G. N. Balaji, and G. Venkateswara Rao. "Machine Learning Approaches for Credit Card Fraud Detection." International Journal of Engineering & Technology 7, no. 2 (2018): 917. http://dx.doi.org/10.14419/ijet.v7i2.9356.

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With the extensive use of credit cards, fraud appears as a major issue in the credit card business. It is hard to have some figures on the impact of fraud, since companies and banks do not like to disclose the amount of losses due to frauds. At the same time, public data are scarcely available for confidentiality issues, leaving unanswered many questions about what is the best strategy. Another problem in credit-card fraud loss estimation is that we can measure the loss of only those frauds that have been detected, and it is not possible to assess the size of unreported/undetected frauds. Fraud patterns are changing rapidly where fraud detection needs to be re-evaluated from a reactive to a proactive approach. In recent years, machine learning has gained lot of popularity in image analysis, natural language processing and speech recognition. In this regard, implementation of efficient fraud detection algorithms using machine-learning techniques is key for reducing these losses, and to assist fraud investigators. In this paper logistic regression, based machine learning approach is utilized to detect credit card fraud. The results show logistic regression based approaches outperforms with the highest accuracy and it can be effectively used for fraud investigators.
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Ishak, Nur Amirah, Keng-Hoong Ng, Gee-Kok Tong, Suraya Nurain Kalid, and Kok-Chin Khor. "Mitigating unbalanced and overlapped classes in credit card fraud data with enhanced stacking classifiers system." F1000Research 11 (January 21, 2022): 71. http://dx.doi.org/10.12688/f1000research.73359.1.

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Background: Credit cards remain the preferred payment method by many people nowadays. If not handled carefully, people may face severe consequences such as credit card frauds. Credit card frauds involve the illegal use of credit cards without the owner’s knowledge. Credit card fraud was estimated to exceed a $35.5 billion loss globally in 2020, and results in direct or indirect financial loss to the owners. Hence, a detection system capable of analysing and identifying fraudulent behaviour in credit card activities is highly desirable. Credit card data are not easy to handle due to their inherited problems: (i) unbalanced class distributions and (ii) overlapping classes. General learning algorithms may not be able to address and handle the problems well. Methods: This study addresses these problems using an Enhanced Stacking Classifiers System (ESCS) that comprises two sequential levels. The first level is an excellent classifier for detecting normal credit card transactions (the majority class), while the second level contains stacking classifiers that distinguish credit card frauds (the minority class). The ESCS can improve the fraud detection via the second level, which contains sensitive classifiers to identify the misclassified fraud transactions as normal transactions from the first classifier. The meta-classifier then combines the decisions of the base classifiers from the levels to produce the final detections. Results: We evaluated the ESCS using the benchmark credit card fraud dataset (CCFD) that exhibits the two problems. The highest true positive rate (TPR) for detecting credit card frauds was 0.8841, which outperformed the single classifiers, bagging, boosting, and other researchers’ works. Conclusions: This study proves that the ESCS, with an additional level added to the stacking classifiers, can improve fraud detection on credit card data.
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Atchaya, S. "Credit Card Fraud Detection using ANN." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (2024): 170–75. http://dx.doi.org/10.22214/ijraset.2024.58284.

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Abstract: Frauds in credit card transactions are common today as most of us are using the credit card payment methods more frequently. This is due to the advancement of Technology and increase in online transaction resulting in frauds causing huge financial loss. Therefore, there is need for effective methods to reduce the loss. In addition, fraudsters find ways to steal the credit card information of the user by sending fake SMS and calls, also through masquerading attack, phishing attack and so on. This paper aims in using the multiple algorithms of Machine learning such as support vector machine (SVM), k-nearest neighbor (Knn) and artificial neural network (ANN) in predicting the occurrence of the fraud. Further, we conduct a differentiation of the accomplished supervised machine learning and deep learning techniques to differentiate between fraud and non-fraud transactions.
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More, Anushka, Nidhi Musale, Himani Ranpariya, Sarthak Salunke, and Prof Sujit Tilak. "Credit Card Fraud Detection System." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (2022): 776–80. http://dx.doi.org/10.22214/ijraset.2022.40744.

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Abstract: Credit Cards are quite useful for day to day life. The main aim of this project is to detect fraud accurately. With the increase in fraud rates, researchers have started using different machine learning methods to detect and analyze frauds in online transactions. 'Fraud' in credit card transactions is unauthorized and unwanted usage of an account by someone other than the owner of that account. Fraud detection involves monitoring the activities of users in whole in order to estimate, perceive or avoid objectionable behavior, which consist of fraud, intrusion, and defaulting. The problem itself is more challenging with respect to data science since the number of valid transactions far outnumber fraudulent ones. Also, the transaction patterns often change their statistical properties over the course of time. However, the massive stream of payment requests is quickly scanned by automatic tools that determine which transactions to authorize. Also, Messages are generated to confirm with the owner about the transactions. Machine learning algorithms are employed to analyze all the authorized transactions and report the suspicious ones. These reports are investigated by professionals who contact the cardholders to confirm if the transaction was genuine or fraudulent. This project also designs and develops a novel fraud detection method for Streaming Transaction Data, with an objective, to analyze the past transaction details of the customers and extract the behavioral patterns. To name a few techniques which we are going to implement are Isolation Forest Algorithm, Random Forest Algorithm, Logistic Regression, Confusion Matrix and Sliding-Window method. Keywords: Credit card fraud, isolation forest, local outlier factor, random forest, confusion matrix
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Patil, Pravin. "Card Defender - Credit Card Fraud Detection System." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 4775–80. http://dx.doi.org/10.22214/ijraset.2023.52748.

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Abstract: As the world becomes increasingly digitized, online transactions have become an indispensable part of our daily lives. The increased use of credit cards for online purchases has resulted in a growing concern about credit card fraud, both for businesses and consumers. To combat this issue, we propose a two-factor authentication system that integrates credit card verification with webcam-based face recognition technology to prevent online transaction fraud. Our system provides a reliable and user-friendly solution for credit card fraud detection using face recognition. By implementing a two-factor authentication process, our system reduces the risk of fraud during online transactions and enhances the overall security of online payments
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Dissertations / Theses on the topic "Credit card fraud"

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Jurgovsky, Johannes. "Context-aware credit card fraud detection." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI109.

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La fraude par carte de crédit est devenue un problème majeur dans le secteur des paiements électroniques. Dans cette thèse, nous étudions la détection de fraude basée sur les données transactionnelles et abordons plusieurs de ces défis complexes en utilisant des méthodes d'apprentissage automatique visant à identifier les transactions frauduleuses qui ont été émises illégitimement au nom du titulaire légitime de la carte. En particulier, nous explorons plusieurs moyens d’exploiter les informations contextuelles au-delà des attributs de base d’une transaction, notamment au niveau de la transaction, au niveau de la séquence et au niveau de l'utilisateur. Au niveau des transactions, nous cherchons à identifier les transactions frauduleuses qui présentent des caractéristiques distinctes des transactions authentiques. Nous avons mené une étude empirique de l’influence du déséquilibre des classes et des horizons de prévision sur la performance d d'un classifieur de type random forest. Nous augmentons les transactions avec des attributs supplémentaires extraits de sources de connaissances externes et montrons que des informations sur les pays et les événements du calendrier améliorent les performances de classification, particulièrement pour les transactions ayant lieu sur le Web. Au niveau de la séquence, nous cherchons à détecter les fraudes qui sont difficiles à identifier en elles-mêmes, mais particulières en ce qui concerne la séquence à court terme dans laquelle elles apparaissent. Nous utilisons un réseau de neurone récurrent (LSTM) pour modéliser la séquence de transactions. Nos résultats suggèrent que la modélisation basée sur des LSTM est une stratégie prometteuse pour caractériser des séquences de transactions ayant lieu en face à face, mais elle n’est pas adéquate pour les transactions ayant lieu sur le Web. Au niveau de l'utilisateur, nous travaillons sur une stratégie existante d'agrégation d'attributs et proposons un concept flexible nous permettant de calculer de nombreux attributs au moyen d'une syntaxe simple. Nous fournissons une implémentation basée sur CUDA pour pour accélerer le temps de calcul de deux ordres de grandeur. Notre étude de sélection des attributs révèle que les agrégats extraits de séquences de transactions des utilisateurs sont plus utiles que ceux extraits des séquences de marchands. De plus, nous découvrons plusieurs ensembles d'attributs candidats avec des performances équivalentes à celles des agrégats fabriqués manuellement tout en étant très différents en termes de structure. En ce qui concerne les travaux futurs, nous évoquons des méthodes d'apprentissage artificiel simples et transparentes pour la détection des fraudes par carte de crédit et nous esquissons une modélisation simple axée sur l'utilisateur<br>Credit card fraud has emerged as major problem in the electronic payment sector. In this thesis, we study data-driven fraud detection and address several of its intricate challenges by means of machine learning methods with the goal to identify fraudulent transactions that have been issued illegitimately on behalf of the rightful card owner. In particular, we explore several means to leverage contextual information beyond a transaction's basic attributes on the transaction level, sequence level and user level. On the transaction level, we aim to identify fraudulent transactions which, in terms of their attribute values, are globally distinguishable from genuine transactions. We provide an empirical study of the influence of class imbalance and forecasting horizons on the classification performance of a random forest classifier. We augment transactions with additional features extracted from external knowledge sources and show that external information about countries and calendar events improves classification performance most noticeably on card-not-present transaction. On the sequence level, we aim to detect frauds that are inconspicuous in the background of all transactions but peculiar with respect to the short-term sequence they appear in. We use a Long Short-term Memory network (LSTM) for modeling the sequential succession of transactions. Our results suggest that LSTM-based modeling is a promising strategy for characterizing sequences of card-present transactions but it is not adequate for card-not-present transactions. On the user level, we elaborate on feature aggregations and propose a flexible concept allowing us define numerous features by means of a simple syntax. We provide a CUDA-based implementation for the computationally expensive extraction with a speed-up of two orders of magnitude. Our feature selection study reveals that aggregates extracted from users' transaction sequences are more useful than those extracted from merchant sequences. Moreover, we discover multiple sets of candidate features with equivalent performance as manually engineered aggregates while being vastly different in terms of their structure. Regarding future work, we motivate the usage of simple and transparent machine learning methods for credit card fraud detection and we sketch a simple user-focused modeling approach
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Westerlund, Fredrik. "CREDIT CARD FRAUD DETECTION (Machine learning algorithms)." Thesis, Umeå universitet, Statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-136031.

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Credit card fraud is a field with perpetrators performing illegal actions that may affect other individuals or companies negatively. For instance, a criminalcan steal credit card information from an account holder and then conduct fraudulent transactions. The activities are a potential contributory factor to how illegal organizations such as terrorists and drug traffickers support themselves financially. Within the machine learning area, there are several methods that possess the ability to detect credit card fraud transactions; supervised learning and unsupervised learning algorithms. This essay investigates the supervised approach, where two algorithms (Hellinger Distance Decision Tree (HDDT) and Random Forest) are evaluated on a real life dataset of 284,807 transactions. Under those circumstances, the main purpose is to develop a “well-functioning” model with a reasonable capacity to categorize transactions as fraudulent or legit. As the data is heavily unbalanced, reducing the false-positive rate is also an important part when conducting research in the chosen area. In conclusion, evaluated algorithms present a fairly similar outcome, where both models have the capability to distinguish the classes from each other. However, the Random Forest approach has a better performance than HDDT in all measures of interest.
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deMatos, Richard Bernard. "Floor limits and credit card fraud in the South African credit card industry." Thesis, University of South Africa, 2007. http://hdl.handle.net/10500/48.

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Credit card fraud losses within the South African credit card market in 2006 exceeded R257M. A portion of these losses (R179M) are within the borders of South Africa and its common monetary area partners. This represents a startling 70% of credit card fraud on magnetic stripe cards used within the borders of South Africa. The South African credit card industry adopts floor limits at certain merchants and merchant categories. South Africa is one of a few countries in the world that still adopt floor limits on credit cards within its payment card industry. Credit card transactions on magnetic-stripe cards conducted below the merchant’s designated floor limit do not go to the issuing bank for authorization. The first time the issuing bank acknowledges these transactions is when they are settled on average two days later. The rationale for not adopting zero floor limits within the South African credit card market is the supposed inability of the existing telecommunications infrastructure to handle the volume and frequency of data submitted by merchants for authorization. The impact of reduced fraud and bad debt losses through adopting a zero floor limit in relation to merchant operational costs is the basis of the research. The research also aims to examine the Proposition that the existing telecommunications infrastructure is unable to support a zero floor limit proposal.
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Char, Shik-ngor Stephen. "Counterfeit credit card fraud : the process of professionalization and organisation /." [Hong Kong] : University of Hong Kong, 1994. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13781248.

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Dal, Pozzolo Andrea. "Adaptive Machine Learning for Credit Card Fraud Detection." Doctoral thesis, Universite Libre de Bruxelles, 2015. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/221654.

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Billions of dollars of loss are caused every year by fraudulent credit card transactions. The design of efficient fraud detection algorithms is key for reducing these losses, and more and more algorithms rely on advanced machine learning techniques to assist fraud investigators. The design of fraud detection algorithms is however particularly challenging due to the non-stationary distribution of the data, the highly unbalanced classes distributions and the availability of few transactions labeled by fraud investigators. At the same time public data are scarcely available for confidentiality issues, leaving unanswered many questions about what is the best strategy. In this thesis we aim to provide some answers by focusing on crucial issues such as: i) why and how undersampling is useful in the presence of class imbalance (i.e. frauds are a small percentage of the transactions), ii) how to deal with unbalanced and evolving data streams (non-stationarity due to fraud evolution and change of spending behavior), iii) how to assess performances in a way which is relevant for detection and iv) how to use feedbacks provided by investigators on the fraud alerts generated. Finally, we design and assess a prototype of a Fraud Detection System able to meet real-world working conditions and that is able to integrate investigators’ feedback to generate accurate alerts.<br>Doctorat en Sciences<br>info:eu-repo/semantics/nonPublished
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Dahabiyeh, Laila Ali. "IS security networks in credit card fraud prevention." Thesis, University of Warwick, 2017. http://wrap.warwick.ac.uk/88609/.

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In our increasingly connected world, maintaining the security of information systems is challenging. Today’s interconnected business environment calls for a change in how IS security is achieved to include thinking about the entire networks of relationships involved in preventing threats rather than just focusing on individual organizational security processes. Despite acknowledging the role of distributed and heterogeneous actors in achieving a secure environment, there is a lack of knowledge of how these actors actually prevent security threats. Moreover, the heterogeneity of actors involved gives rise to the issue of incentives needed to align their interests to ensure successful collective security efforts. This PhD thesis addresses these issues by zooming in on security networks, defined as collective efforts pursued by distributed actors to develop and adopt prevention measures to achieve security, to explain how these networks prevent security threats and identify the incentive mechanisms for converging the network’s heterogeneous actors. I challenge equilibrium and linearity assumptions identified in the current literature and argue for the need to adopt different theoretical and methodological approaches to uncover the dynamics in these networks. Through a historical case study of credit card fraud and how its prevention measures evolved over the last 55 years, I develop a process model of prevention encounters in security networks. The model depicts the dynamic and interactive nature of the prevention process and shows how the three proposed prevention mechanisms, namely, proposing solutions, resolving dissonance, and paving the way, interact to achieve prevention. The thesis further proposes three new forms of incentive mechanisms (transformative, preparatory, and captive) that are crucial for the survival of collective security efforts and show how they interact with the three prevention mechanisms. By this, this research complements the current security networks literature by offering a process model that explains how security networks achieve prevention. In addition, the interplay between the three incentive mechanisms reveals that incentives are not only ready-made structures or one-time event as depicted in the current literature but that they should also be seen as a socially dynamic process.
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Char, Shik-ngor Stephen, and 查錫我. "Counterfeit credit card fraud: the process ofprofessionalization and organisation." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1994. http://hub.hku.hk/bib/B31977583.

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Frank, Mari J. "Identity theft prevention and survival /." [Laguna Niguel, Calif.] : M.J. Frank and Associates, 1999. http://www.identitytheft.org.

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ID-theft survival kit -- Book From victim to victor -- ID theft FAQ -- Audiocassettes -- Identity theft resources -- Testimonials -- ID theft action letters -- About the author -- Media appearances -- Identity theft laws -- Theft Deterrence Act.<br>Title from opening screen, December 28, 1999.
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Lucas, Yvan. "Credit card fraud detection using machine learning with integration of contextual knowledge." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI110.

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La détection de fraude par carte de crédit présente plusieurs caractéristiques qui en font une tâche difficile. Tout d'abord, les attributs décrivant une transaction ignorent les informations séquentielles. Deuxièmement, les comportements d'achat et les stratégies de fraude peuvent changer au fil du temps, rendant progressivement une fonction de décision apprise par un classifieur non pertinente. Nous avons effectué une analyse exploratoire afin de quantifier le dataset shift jour par jour et avons identifé des périodes calendaires qui ont des propriétés différentes au sein du jeu de données. La stratégie principale pour intégrer des informations séquentielles consiste à créer un ensemble d'attributs qui sont des statistiques descriptives obtenues en agrégeant les séquences de transactions des titulaires de carte. Nous avons utilisé cette méthode comme méthode de référence pour la détection des fraudes à la carte de crédit. Nous avons proposé une stratégie pour la création d'attributs basés sur des modèles de Markov cachés (HMM) caractérisant la transaction par différents points de vue afin d'intégrer un large spectre d'informations séquentielles au sein des transactions. En fait, nous modélisons les comportements authentiques et frauduleux des commerçants et des détenteurs de cartes selon deux caractéristiques univariées: la date et le montant des transactions. Notre approche à perspectives multiples basée sur des HMM permet un prétraitement automatisé des données pour modéliser les corrélations temporelles. Des expériences menées sur un vaste ensemble de données de transactions de cartes de crédit issu du monde réel (46 millions de transactions effectuées par des porteurs de carte belges entre mars et mai 2015) ont montré que la stratégie proposée pour le prétraitement des données basé sur les HMM permet de détecter davantage de transactions frauduleuses quand elle est combinée à la stratégie de prétraitement des données par aggrégations<br>The detection of credit card fraud has several features that make it a difficult task. First, attributes describing a transaction ignore sequential information. Secondly, purchasing behavior and fraud strategies can change over time, gradually making a decision function learned by an irrelevant classifier. We performed an exploratory analysis to quantify the day-by-day shift dataset and identified calendar periods that have different properties within the dataset. The main strategy for integrating sequential information is to create a set of attributes that are descriptive statistics obtained by aggregating cardholder transaction sequences. We used this method as a reference method for detecting credit card fraud. We have proposed a strategy for creating attributes based on Hidden Markov Models (HMMs) characterizing the transaction from different viewpoints in order to integrate a broad spectrum of sequential information within transactions. In fact, we model the authentic and fraudulent behaviors of merchants and cardholders according to two univariate characteristics: the date and the amount of transactions. Our multi-perspective approach based on HMM allows automated preprocessing of data to model temporal correlations. Experiments conducted on a large set of data from real-world credit card transactions (46 million transactions carried out by Belgian cardholders between March and May 2015) have shown that the proposed strategy for pre-processing data based on HMMs can detect more fraudulent transactions when combined with the Aggregate Data Pre-Processing strategy
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Ehramikar, Soheila. "The enhancement of credit card fraud detection systems using machine learning methodology." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0023/MQ50338.pdf.

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Books on the topic "Credit card fraud"

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United States. Federal Trade Commission. Office of Consumer and Business Education, ed. Credit and charge card fraud. Federal Trade Commission, Bureau of Consumer Protection, Office of Consumer & Business Education, 1996.

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United States. Federal Trade Commission. Office of Consumer and Business Education., ed. Credit and charge card fraud. Federal Trade Commission, Bureau of Consumer Protection, Office of Consumer & Business Education, 1996.

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United States. Federal Trade Commission. Office of Consumer and Business Education, ed. Credit and charge card fraud. Federal Trade Commission, Bureau of Consumer Protection, Office of Consumer & Business Education, 1996.

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United States. Federal Trade Commission. Division of Consumer and Business Education. Credit card interest rate reduction scams. Federal Trade Commission, Bureau of Consumer Protection, Division of Consumer & Business Education, 2011.

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United States. Federal Trade Commission. Office of Consumer and Business Education, ed. Secured credit card marketing scams. FTC [Bureau of Consumer Protection, Office of Consumer and Business Education, 1996.

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Levi, Michael. The prevention of cheque and credit card fraud. Home Office, 1991.

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Han-Chung sinyong kʻa pŏmni: Credit card. Hanʼguk Haksul Chŏngbo, 2008.

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Nagai, Madoka. Kādo hanzai taisakuhō no saisentan. Nihon Kurejitto Sangyō Kyōkai Kurejitto Kenkyūjo, 2000.

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Nikolai︠u︡k, S. I. Protydii︠a︡ zlochynam, shcho vchyni︠a︡i︠u︡tʹsi︠a︡ iz vykorystanni︠a︡m plastykovykh platiz︠h︡nykh kartok: Naukovo-praktychnyĭ posibnyk. KNT, 2007.

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Hermelo, Oscar. La tarjeta de crédito en el derecho penal. Pensamiento Jurídico Editora, 1985.

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Book chapters on the topic "Credit card fraud"

1

Janbandhu, Ruchika, Shameedha Begum, and N. Ramasubramanian. "Credit Card Fraud Detection." In Advances in Intelligent Systems and Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9515-5_22.

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Damez, Marc, Marie-Jeanne Lesot, and Adrien Revault d’Allonnes. "Dynamic Credit-Card Fraud Profiling." In Modeling Decisions for Artificial Intelligence. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34620-0_22.

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Ullah, Hafya, Aysha Thahsin Zahir Ismail, Lakshmi Babu Saheer, and Mahdi Maktabdar Oghaz. "Credit Card Fraud Using Adversarial Attacks." In Artificial Intelligence XXXIX. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21441-7_26.

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Murugan, Yogamahalakshmi, M. Vijayalakshmi, Lavanya Selvaraj, and Saranya Balaraman. "Credit Card Fraud Detection Using CNN." In Internet of Things and Connected Technologies. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94507-7_19.

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Blackwell, Clive. "Using Fraud Trees to Analyze Internet Credit Card Fraud." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-662-44952-3_2.

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Kushal, Krishna, Greeshma Kurup, and Siddhaling Urolagin. "Data Mining Techniques for Fraud Detection—Credit Card Frauds." In Algorithms for Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4604-8_10.

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Priscilla, C. Victoria, and D. Padma Prabha. "Credit Card Fraud Detection: A Systematic Review." In Learning and Analytics in Intelligent Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38501-9_29.

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Sudha, C., and D. Akila. "Credit Card Fraud Detection Using AES Technic." In Intelligent Computing and Innovation on Data Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3284-9_11.

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Mingxiang, Liu. "Credit card fraud in Chinese criminal law." In Transnational Crime. Routledge, 2018. http://dx.doi.org/10.4324/9781351026826-11.

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Charitha, Sree, Shivani Chowdary, Trupthi Rao, Ashwini Kodipalli, Shoaib Kamal, and B. R. Rohini. "Credit Card Fraud Analysis Using Machine Learning." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-7633-1_21.

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Conference papers on the topic "Credit card fraud"

1

Gowda, Vikas Thammanna. "Credit Card Fraud Detection using Supervised and Unsupervised Learning." In 2nd International Conference on Machine Learning &Trends (MLT 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.111107.

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Abstract:
In the present monetary situation, credit card use has gotten normal. These cards allow the user to make payments online and even in person. Online payments are very convenient, but it comes with its own risk of fraud. With the expanding number of credit card users, frauds are also expanding at the same rate. Some machine learning algorithms can be applied to tackle this problem. In this paper an evaluation of supervised and unsupervised machine learning algorithms has been presented for credit card fraud detection.
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Singh, Shreya, and Ayush Maheshwari. "Credit Card Fraud Detection." In 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N). IEEE, 2022. http://dx.doi.org/10.1109/icac3n56670.2022.10074052.

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Zheng, Wenbo, Lan Yan, Chao Gou, and Fei-Yue Wang. "Federated Meta-Learning for Fraudulent Credit Card Detection." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/642.

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Credit card transaction fraud costs billions of dollars to card issuers every year. Besides, the credit card transaction dataset is very skewed, there are much fewer samples of frauds than legitimate transactions. Due to the data security and privacy, different banks are usually not allowed to share their transaction datasets. These problems make traditional model difficult to learn the patterns of frauds and also difficult to detect them. In this paper, we introduce a novel framework termed as federated meta-learning for fraud detection. Different from the traditional technologies trained with data centralized in the cloud, our model enables banks to learn fraud detection model with the training data distributed on their own local database. A shared whole model is constructed by aggregating locallycomputed updates of fraud detection model. Banks can collectively reap the benefits of shared model without sharing the dataset and protect the sensitive information of cardholders. To achieve the good performance of classification, we further formulate an improved triplet-like metric learning, and design a novel meta-learning-based classifier, which allows joint comparison with K negative samples in each mini-batch. Experimental results demonstrate that the proposed approach achieves significantly higher performance compared with the other state-of-the-art approaches.
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Filippov, V., L. Mukhanov, and B. Shchukin. "Credit card fraud detection system." In 2008 7th IEEE International Conference on Cybernetic Intelligent Systems (CIS). IEEE, 2008. http://dx.doi.org/10.1109/ukricis.2008.4798919.

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Balagolla, E. M. S. W., W. P. C. Fernando, R. M. N. S. Rathnayake, M. J. M. R. P. Wijesekera, A. N. Senarathne, and K. Y. Abeywardhana. "Credit Card Fraud Prevention Using Blockchain." In 2021 6th International Conference for Convergence in Technology (I2CT). IEEE, 2021. http://dx.doi.org/10.1109/i2ct51068.2021.9418192.

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Pawar, Rohan, Hardik Kathuria, and Praveen Joe I. R. "Credit Card Fraud Detection and Analysis." In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2023. http://dx.doi.org/10.1109/icccnt56998.2023.10306410.

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Meng, Chew Chee, Kian Ming Lim, Chin Poo Lee, and Jit Yan Lim. "Credit Card Fraud Detection using TabNet." In 2023 11th International Conference on Information and Communication Technology (ICoICT). IEEE, 2023. http://dx.doi.org/10.1109/icoict58202.2023.10262711.

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Ueng, Shyh-Kuang, and Chun-Yi Peng. "Fraud Detection for Credit Card Transactions." In 2024 10th International Conference on Applied System Innovation (ICASI). IEEE, 2024. http://dx.doi.org/10.1109/icasi60819.2024.10547942.

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Bakshi, Sonali. "Credit Card Fraud Detection : A classification analysis." In 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2018. http://dx.doi.org/10.1109/i-smac.2018.8653770.

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Ceronmani Sharmila, V., Kiran Kumar R., Sundaram R., Samyuktha D., and Harish R. "Credit Card Fraud Detection Using Anomaly Techniques." In 2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT). IEEE, 2019. http://dx.doi.org/10.1109/iciict1.2019.8741421.

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