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Relevant bibliographies by topics / Credit card

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Academic literature on the topic 'Credit card'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 June 2025

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Journal articles on the topic "Credit card"

1

Jovanović, Slobodan. "Credit card insurance." Tokovi osiguranja 38, no. 2 (2022): 52–74. http://dx.doi.org/10.5937/tokosig2202052j.

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This paper first briefly discusses the legal nature and legal framework of the loan and credit card agreement, its specific mandatory elements, and the relationship between such agreement and the credit card insurance agreement, followed by particular aspects of a credit card, its economic importance, and wide spread presence. The second part of the paper deals with the classification of this non-life insurance service, and the nature and scope of risks covered by insurance. The accessory nature of the contract for credit card insurance is pointed out. The author divides the insurance of the credit cardholders into insurance in favour of the credit card issuer and insurance in favour of the credit cardholder, and then analyses the specifics of insurance in the event of unemployment and accident suffered by the insured person. The author concludes that the insurance of credit cardholders is carried out exclusively using the method of "named risks", whereas Serbian insurance terms and conditions have deficiencies in terms of defining covered and excluded risks, while there are circumstances on the foreign market for which no coverage is provided.

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Berghel, Hal. "Credit Card Forensics." Communications of the ACM 50, no. 12 (2007): 11–14. http://dx.doi.org/10.1145/1323688.1323708.

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Cohen-Cole, Ethan. "Credit Card Redlining." Review of Economics and Statistics 93, no. 2 (2011): 700–713. http://dx.doi.org/10.1162/rest_a_00052.

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Partridge, R. D. "Which credit card?" In Practice 14, no. 3 (1992): 156. http://dx.doi.org/10.1136/inpract.14.3.156.

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EASTERN, JOSEPH S. "Credit Card Processing." Skin & Allergy News 38, no. 11 (2007): 54. http://dx.doi.org/10.1016/s0037-6337(07)70898-7.

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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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Hadi, Sholikul, Didin Hafidudhin, and Hendri Tanjung. "Comparison of Conventional Systems Credit Card and Credit Card Shariah as Alternative Construction Credit Card on Banking System." Jurnal Manajemen 8, no. 1 (2018): 1. http://dx.doi.org/10.32832/jm-uika.v8i1.733.

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<p>This study was motivated by the presence of different views on whether or not<br />allowed to use Islamic credit cards, although the Indonesian Council of Ulama (MUI) has issued a fatwa on the permissibility of the card. Given these differences, this study examines the operating system on credit cards in terms of Islamic law, a difference of conventional and Islamic Credit Card and alternative solutions Credit Card reconstruction system in accordance with the Islamic Shari'ah and can be applied in the modern economy.The results showed (1) Credit Card operating system in terms of Sharia Islamic law indicates permitted<br />use; (2) Found some fundamental differences between Islamic and conventional Credit Card; (3) An alternative solution in the reconstruction of the Credit Card in accordance with Islamic law, including: (a) the credit card must be received recognition from banks with partnershipnya; (b) the credit card should be simple, both in the process of obtaining and using it; (c) The credit card issuer must be heavily promoting.</p>

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Limbu, Yam B. "Credit card knowledge, social motivation, and credit card misuse among college students." International Journal of Bank Marketing 35, no. 5 (2017): 842–56. http://dx.doi.org/10.1108/ijbm-04-2016-0045.

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Purpose By applying the information-motivation-behavioral (IMB) skills model, the purpose of this paper is to examine the direct and indirect effects of credit card knowledge and social motivation on credit card misuse behavior mediated through credit card self-efficacy among college students in the USA. Design/methodology/approach A sample of 427 participants was surveyed. Structural equation modeling was used to assess the hypothesized model. Findings Credit card knowledge and social motivation were inversely associated with credit card misuse mediated through credit card self-efficacy. Credit card knowledge had a direct negative relationship with credit card misuse. The results confirm the theoretical relationships in the IMB model. Practical implications The results offer several implications for bank marketers and policy makers. The IMB model could be used to predict credit card abuse among college students; credit card literacy programs should incorporate strategies that can enhance students’ knowledge, social motivation, and behavioral skills with regard to responsible use of credit cards. Originality/value This study is unique in that it applies the IMB model to examine predictors of credit card misuse among college students.

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Manokari, G. Murali, and Dr R. Ganapathi Dr. R. Ganapathi. "Credit Card Usage in Coimbatore." Indian Journal of Applied Research 1, no. 7 (2011): 122–26. http://dx.doi.org/10.15373/2249555x/apr2012/40.

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Brink, Sophia. "The accounting treatment of credit card rewards programmes: a South African perspective (Part 2)." Journal of Economic and Financial Sciences 10, no. 2 (2017): 206–34. http://dx.doi.org/10.4102/jef.v10i2.14.

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Most credit card issuers offer their card holders participation in a customer loyalty programme. On 1 July 2007 the IASB issued IFRIC 13 Customer Loyalty Programmes to give specific guidance to suppliers on the accounting treatment of customer loyalty programme transactions. Despite the fact that credit card rewards programmes are specifically included in the scope of this Interpretation, in practice not all credit card rewards programmes currently account for award credits under the revenue deferral model (IFRIC 13). These divergent practices make one question the relevance of the current guidance provided in IFRIC 13 to credit card rewards programmes; otherwise what is the reason behind credit card rewards programmes accounting for these transactions differently? During May 2014 the IASB and the United States Financial Accounting Standards Board (FASB), published IFRS 15 Revenue from Contracts with Customers intended to replace six existing Standards and Interpretations, including IFRIC 13. The aim of IFRS 15 is to streamline accounting for revenue across all industries and to correct inconsistencies in existing Standards and practices. Credit card rewards programme respondents raised many queries and uncertainties based on the proposed model but despite these concerns the Boards decided against providing any additional guidance to credit card rewards programmes. They indicated that they leave it up to management

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Dissertations / Theses on the topic "Credit card"

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Blunt, Gordon. "Mining credit card data." Thesis, n.p, 2002. http://ethos.bl.uk/.

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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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Martinez, John Brett. "Credit card credit scoring and risk based lending at XYZ Credit Union." CSUSB ScholarWorks, 2000. https://scholarworks.lib.csusb.edu/etd-project/1752.

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Chun, Pan, and Qiu Tian. "Credit card business analysis of Bank of China : Is BOC credit card competitive?" Thesis, Mälardalens högskola, Akademin för hållbar samhälls- och teknikutveckling, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-9981.

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Ekici, Tufan. "An investigation of credit card debt." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1141228519.

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Thesis (Ph. D.)--Ohio State University, 2006.<br>An investigation of credit card debt: the effect of price and income expectations and the impact on consumption. Includes bibliographical references (p. 108-111).

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FILHO, ELIAS DIAS LOPES. "THIRD AGE: CREDIT CARD USE PROFILE." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2006. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9443@1.

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O aumento da expectativa de vida dos brasileiros é cada vez maior e, conseqüentemente, também sua presença na economia. Assim como o público idoso, o mercado de cartões de crédito também cresce em ritmo acelerado. Este trabalho estuda as características do portador de cartão de crédito do segmento da Terceira Idade, ou melhor, pessoas com mais de 60 anos, assim como seu comportamento em relação ao uso do cartão de crédito. Aplicou-se uma pesquisa qualitativa, através de entrevistas em profundidade, que levou à análise de outras faixas etárias para fins de comparação do comportamento de uso do cartão de crédito, assim como a aplicação de um questionário no aspecto quantitativo. Os resultados apontaram para características conservadoras por parte da Terceira Idade no que se refere ao uso do cartão de crédito.<br>The increase of life expectation of the Brazilians grows bigger and bigger and, consequently, also their presence in the country's economy. Just as the aged public, the credit card market also grows in an accelerated rhythm. This work studies the characteristics of the Third Age credit card bearer segment, in other words, people over the sixties, as well as their behavior regarding the credit card use. A qualitative research was applied, by means of in- depth interviews, which lead to the analysis of other age groups aiming at the comparison of the credit card use behavior, as well as the application of a quantitative wise questionnaire. The results point towards conservative characteristics on the part of the Third Age as far as the credit card use is concerned.

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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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Shui, Haiyan. "Time inconsistency in the credit card market." College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/2082.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2004.<br>Thesis research directed by: Economics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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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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Sangsutisearee, Wanna. "Credit Card Usage and Knowledge in Thailand." DigitalCommons@USU, 1993. https://digitalcommons.usu.edu/etd/2808.

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This study was conducted to determine the relationship between the characteristics of credit card holders and the extent of credit card usage, the level of credit card knowledge, and the consumer's choice perspectives. The characteristics of credit card holders studied were (a) gender, (b) age, (c) marital status, (d) education, (e) income, and (f) occupation. Data for this study were collected in Bangkok, Thailand by telephone interviews during July- August 1993. The sample consisted of 150 Bangkok Bank credit card holders. Chi-square and Cramer's V were used to analyze the hypotheses. For all statistical analyses the level of significance was set at .05. As a result of the 18 specific null hypothesis tests, only two independent variables were found to have a statistically significant relationship with the extent of credit card usage. The findings suggested a positive relationship between the extent of credit card usage and occupation and income. Respondents with higher occupational status and income tended to use credit cards to a greater extent than those with lower occupational status and income. Gender, age, marital status, and education were not related to the extent of credit card usage in this study. No significant differences were found among the demographic characteristics of credit card holders and the level of credit card knowledge, and consumer's choice perspectives. Gender, age, marital status. education, occupation, and income were not related to the level of credit card knowledge and the consumer's choice. The results revealed that most Thai credit card holders were not knowledgeable about credit card terms. They were clearly influenced by environmental stimuli, and the effects of society and group norms when they applied for a credit card. They generally chose a card with incomplete information about the alternatives.

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Books on the topic "Credit card"

1

Jali, Farkhanda. Credit card. Ferozsons, 1992.

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United States. Federal Trade Commission. Office of Consumer and Business Education, ed. Credit card blocking. Office of Consumer & Business Education, Bureau of Consumer Protection, Federal Trade Commission, 1994.

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Grady, Bill. Credit card marketing. A & S Publishers, 1992.

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Credit card marketing. J. Wiley, 1995.

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Credit-card Carole. Scribner, 1987.

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Klass, Sheila Solomon. Credit-card Carole. Bantam Books, 1989.

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

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Nelson, Richard Warren. Credit card risk management. Warren Taylor Publications, 1997.

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Ohio. Attorney General's Consumer Protection Section, ed. Evaluating credit card solicitations. Attorney General's Consumer Protection Section, 2001.

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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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Book chapters on the topic "Credit card"

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McElravey, John N. "Credit Card ABS." In Structured Products and Related Credit Derivatives. John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119197836.ch4.

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Stango, Victor, and Julian Wright. "Credit Card Industry." In The New Palgrave Dictionary of Economics. Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_2286.

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Stango, Victor, and Julian Wright. "Credit Card Industry." In The New Palgrave Dictionary of Economics. Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/978-1-349-95121-5_2286-1.

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Darie, Cristian, and Karli Watson. "Credit Card Transactions." In Beginning ASP.NET 1.1 E-Commerce: From Novice to Professional. Apress, 2004. http://dx.doi.org/10.1007/978-1-4302-0690-3_15.

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Chung, Lawrence, Brian A. Nixon, Eric Yu, and John Mylopoulos. "A Credit Card System." In Non-Functional Requirements in Software Engineering. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-5269-7_11.

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Jiang, Liangjun. "Pay with Credit Card." In iOS eCommerce App Development with Parse. Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-1317-9_12.

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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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Bussing-Burks, Marie. "A Huge Credit Card." In Deficit. Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4840-8_2.

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Bussing-Burks, Marie. "A Huge Credit Card." In Deficit. Apress, 2011. http://dx.doi.org/10.1007/978-1-4302-3660-3_2.

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Soni, Arun. "Debit/Credit Card Security." In The Cybersecurity Self-Help Guide. CRC Press, 2021. http://dx.doi.org/10.1201/9781003148678-10.

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Conference papers on the topic "Credit card"

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Okay Toprak, Aslı, Canan Özge Eğri, and Güldenur Çetin. "The Usage of Credit Cards: An Empirical Analysis on Turkish College Students." In International Conference on Eurasian Economies. Eurasian Economists Association, 2019. http://dx.doi.org/10.36880/c11.02263.

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In recent years, there has been a dramatic increase in the number of credit card usage among university students. Credit cards can be a convenient payment tool that gives university students a number of advantages and benefits to learn financial responsibility when it can be used in a controlled and responsible manner. On the other hand, using credit cards also have serious financial consequences when mismanagedly used. The excessive credit card debt and overdue payments give burden on university students’ shoulders before starting their full-time jobs. Besides that, when the other debts such as education credits are added, inevitable stress and anxiety make negative impacts on their newly started adult life. Also, lack of experience on using credit cards and personal financial information, tend to put some students at a higher financial risk due to a large and perhaps unmanageable debt burden. Therefore, rising number of students who use credit cards increases the concern for these long-term negative results of the credit card. In this context, we aim to evaluate the basic demographic and socio-economic factors that affect the attitudes of Kırklareli University students towards credit card ownership, credit card usage, and to evaluate the students' ability to manage their financial situation.

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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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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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Graefe, Jonathan M., Laurel Lashley, Mario A. M. Guimaraes, et al. "Credit card transaction security." In the 4th annual conference. ACM Press, 2007. http://dx.doi.org/10.1145/1409908.1409928.

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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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Altman, Erik. "Synthesizing credit card transactions." In ICAIF'21: 2nd ACM International Conference on AI in Finance. ACM, 2021. http://dx.doi.org/10.1145/3490354.3494378.

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Radolović, Oliver, and Filip Komljen. "Credit Cards as an Instrument of Payment in Tourism and Hospitality." In Interdisciplinarity Counts. University of Maribor, University Press, 2023. http://dx.doi.org/10.18690/um.fov.3.2023.69.

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Credit cards are one of the most used instruments of payment today. The advantages they have are the simplicity of their use and the mandatory relationship of a short-term credit contract. Nowadays, credit cards are increasingly used in tourism and hospitality. Many banks around the world cooperate with global card organizations such as VISA, MasterCard, American Express and Diners Club, so the cards they issue are widely accepted in hotels, travel agencies, hospitality objects and shopping centers around the world. This paper deals with the role of the credit card as a global instrument of payment in tourism from a legal and economic point of view, and its goal is to see what all the characteristics of a credit card are and what benefits its use brings. Its specificity for use in tourism and hospitality is mainly its simplicity (1), high security while reducing the risk of carrying cash (2) and the international character of the legal relations that are related to it (3).

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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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Rippen, Marc, Harry Braunstein, Craig Nelson, Carl Schuck, Philippe Guillaud, and Philippe Blott. "Interactive Multitask Credit Card Technology." In 2007 IEEE International Conference on Portable Information Devices. IEEE, 2007. http://dx.doi.org/10.1109/portable.2007.66.

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Yildiz, Muhammet, and Mehmet Göktürk. "Combining Biometric ID Cards and Online Credit Card Transactions." In 2010 Fourth International Conference on the Digital Society (ICDS). IEEE, 2010. http://dx.doi.org/10.1109/icds.2010.12.

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Almudaires, Fajer, and Mohammed Almaiah. "Data an Overview of Cybersecurity Threats on Credit Card Companies and Credit Card Risk Mitigation." In 2021 International Conference on Information Technology (ICIT). IEEE, 2021. http://dx.doi.org/10.1109/icit52682.2021.9491114.

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Reports on the topic "Credit card"

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Eastlake, D., B. Boesch, S. Crocker, and M. Yesil. CyberCash Credit Card Protocol Version 0.8. RFC Editor, 1996. http://dx.doi.org/10.17487/rfc1898.

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Agarwal, Sumit, John Driscoll, Xavier Gabaix, and David Laibson. Learning in the Credit Card Market. National Bureau of Economic Research, 2008. http://dx.doi.org/10.3386/w13822.

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Galashin, Misha, Martin Kanz, and Ricardo Perez-Truglia. Macroeconomic Expectations and Credit Card Spending. National Bureau of Economic Research, 2020. http://dx.doi.org/10.3386/w28281.

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Ru, Hong, and Antoinette Schoar. Do Credit Card Companies Screen for Behavioral Biases? National Bureau of Economic Research, 2016. http://dx.doi.org/10.3386/w22360.

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Kramer, MItchell. Research, Compare, and Select a Travel Rewards Credit Card. Patricia Seybold Group, 2007. http://dx.doi.org/10.1571/td04-12-07cc.

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Butaru, Florentin, QingQing Chen, Brian Clark, Sanmay Das, Andrew Lo, and Akhtar Siddique. Risk and Risk Management in the Credit Card Industry. National Bureau of Economic Research, 2015. http://dx.doi.org/10.3386/w21305.

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Hayashi, Fumiko, Aditi Routh, Sam Baird, and Jalen Nichols. Credit and Debit Card Interchange Fees in Various Countries. Federal Reserve Bank of Kansas City, 2023. http://dx.doi.org/10.18651/icf/variouscountries2023update.

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Hayashi, Fumiko, and Sam Baird. Credit and Debit Card Interchange Fees in Various Countries. Federal Reserve Bank of Kansas City, 2022. http://dx.doi.org/10.18651/icf/variouscountries2022update.

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Hayashi, Fumiko, and Vanessa Nimmo. Credit and Debit Card Interchange Fees in Various Countries. Federal Reserve Bank of Kansas City, 2021. http://dx.doi.org/10.18651/icf/variouscountries2021update.

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Slemrod, Joel, Brett Collins, Jeffrey Hoopes, Daniel Reck, and Michael Sebastiani. Does Credit-card Information Reporting Improve Small-business Tax Compliance? National Bureau of Economic Research, 2015. http://dx.doi.org/10.3386/w21412.

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