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Journal articles on the topic 'Credit card'
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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 (December 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 (May 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 (May 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 (November 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 (May 31, 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 (August 30, 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 (July 3, 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 (October 1, 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 (November 6, 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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Grodzicki, Daniel, and Sergei Koulayev. "Sustained credit card borrowing." Journal of Consumer Affairs 55, no. 2 (April 5, 2021): 622–53. http://dx.doi.org/10.1111/joca.12360.
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Brevoort, Kenneth P. "Credit Card Redlining Revisited." Finance and Economics Discussion Series 2009, no. 39 (October 2009): 1–50. http://dx.doi.org/10.17016/feds.2009.39.
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Trivedi, Ishu, Monik M, and Mrigya Mridushi. "Credit Card Fraud Detection." IJARCCE 5, no. 1 (January 30, 2016): 39–42. http://dx.doi.org/10.17148/ijarcce.2016.5109.
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Gurav, Prof R. B., Mrs Shraavani Mandar Badhe, Mrs Sakshi Nagtilak, Mr Sarthak Pandit Sonawane, and Mr Siddhant Agarwal. "Credit Card Fraud Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2009–12. http://dx.doi.org/10.22214/ijraset.2022.41594.
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Abstract: Now a day’s online transactions have become an important and necessary part of our lives. It is vital that credit card companies are able to identify fraudulent credit card transactions so that customers are not charged for items that they did not purchase. As frequency of transactions is increasing, number of fraudulent transactions are also increasing rapidly. Such problems can be tackled with Machine Learning with its algorithms. This project intends to illustrate the modelling of a data set using machine learning with Credit Card Fraud Detection. The Credit Card Fraud Detection Problem includes modelling past credit card transactions with the data of the ones that turned out to be fraud. This model is then used to recognize whether a new transaction is fraudulent or not. Our objective here is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications. Credit Card Fraud Detection is a typical sample of classification. In this process, we have focused on analyzing and preprocessing data sets as well as the deployment of multiple anomaly detection algorithms such as Local Outlier Factor and Isolation Forest algorithm on the PCA transformed Credit Card Transaction data. Keywords: Credit Card, Python, Detection
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Gurav, Prof R. B., Mrs Shraavani Mandar Badhe, Mrs Sakshi Nagtilak, Mr Sarthak Pandit Sonawane, and Mr Siddhant Agarwal. "Credit Card Fraud Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2009–12. http://dx.doi.org/10.22214/ijraset.2022.41594.
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Abstract: Now a day’s online transactions have become an important and necessary part of our lives. It is vital that credit card companies are able to identify fraudulent credit card transactions so that customers are not charged for items that they did not purchase. As frequency of transactions is increasing, number of fraudulent transactions are also increasing rapidly. Such problems can be tackled with Machine Learning with its algorithms. This project intends to illustrate the modelling of a data set using machine learning with Credit Card Fraud Detection. The Credit Card Fraud Detection Problem includes modelling past credit card transactions with the data of the ones that turned out to be fraud. This model is then used to recognize whether a new transaction is fraudulent or not. Our objective here is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications. Credit Card Fraud Detection is a typical sample of classification. In this process, we have focused on analyzing and preprocessing data sets as well as the deployment of multiple anomaly detection algorithms such as Local Outlier Factor and Isolation Forest algorithm on the PCA transformed Credit Card Transaction data. Keywords: Credit Card, Python, Detection
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Gurav, Prof R. B., Shraavani Mandar Badhe, Sarthak Pandit Sonawane, Siddhant Agarwal, and Sakshi Nagtilak. "Credit Card Fraud Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 3499–509. http://dx.doi.org/10.22214/ijraset.2022.42920.
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Abstract: Now a day’s online transactions have become an important and necessary part of our lives. It is vital that credit card companies are able to identify fraudulent credit card transactions so that customers are not charged for items that they did not purchase. As frequency of transactions is increasing, number of fraudulent transactions are also increasing rapidly. Such problems can be tackled with Machine Learning with its algorithms. This project intends to illustrate the modelling of a data set using machine learning with Credit Card Fraud Detection. The Credit Card Fraud Detection Problem includes modelling past credit card transactions with the data of the ones that turned out to be fraud. This model is then used to recognize whether a new transaction is fraudulent or not. Our objective here is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications. Credit Card Fraud Detection is a typical sample of classification. In this process, we have focused on analyzing and preprocessing data sets as well as the deployment of multiple anomaly detection algorithms such as Local Outlier Factor and Isolation Forest algorithm on the PCA transformed Credit Card Transaction data. Keywords: Credit Card, Python, Detection
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Lawton, Graham. "A carbon credit card." New Scientist 252, no. 3357 (October 2021): 26. http://dx.doi.org/10.1016/s0262-4079(21)01888-1.
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Wong, King Yin, and Michael Lynn. "Credit card cue effect." International Journal of Bank Marketing 38, no. 2 (July 23, 2019): 368–83. http://dx.doi.org/10.1108/ijbm-01-2019-0010.
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Purpose The extant literature has mixed results regarding the credit card cue effect. Some showed that credit card cues stimulate spending, whereas others were unable to replicate the findings or found that cues discourage consumer spending. The purpose of this paper is to investigate how consumers’ sensitivity to the pain of payment affects their mental associations about credit cards and how the differences in credit card associations moderate the credit card cue effect on spending, providing a possible explanation for the mixed results in the literature. Furthermore, this paper examines the role of consumers’ perceived financial well-being, measured by their perceptions of current and future wealth and their sense of financial security, in mediating this moderation effect. Design/methodology/approach An experimental study was conducted with a sample of 337 participants to test the hypothesized model. Findings After being shown credit card cues, spendthrift participants had more spending-related thoughts and less debt-related thoughts, perceived themselves as having better financial well-being and consequently spent more than tightwad participants. Originality/value To the authors’ knowledge, this is the first study to investigate the direct link between an exposure to credit card cues and perceived financial well-being, and one of the few to show evidence of the moderating effect of consumers’ sensitivity to the pain of payment on spending when credit card cues are present. This study suggests that marketers may use credit card cues to promote consumer spending, whereas consumers, especially spendthrifts, should be aware of how credit card cues may inflate their perceived financial well-being and stimulate them to spend more.
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Brevoort, Kenneth P. "Credit Card Redlining Revisited." Review of Economics and Statistics 93, no. 2 (May 2011): 714–24. http://dx.doi.org/10.1162/rest_a_00173.
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Yamashita, Kumi. "Credit Card Portraits, 1993." Rethinking Marxism 15, no. 3 (July 2003): 424–25. http://dx.doi.org/10.1080/0893569032000131992.
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Breitrose, Charlie. "Credit card file hacked." Computer Fraud & Security 1997, no. 9 (September 1997): 6. http://dx.doi.org/10.1016/s1361-3723(97)82874-6.
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Rochet, Jean-Charles, and Julian Wright. "Credit card interchange fees." Journal of Banking & Finance 34, no. 8 (August 2010): 1788–97. http://dx.doi.org/10.1016/j.jbankfin.2010.02.026.
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Lau, Susan. "Selling a credit card." Bankfachklasse 28, no. 12 (December 2006): 25–26. http://dx.doi.org/10.1007/bf03255298.
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Shevgaonkar, Sanskriti, Priyanka Khadse, Omkar Shinde, Tanmay Kulkarni, and Prof Avinash Gondal. "Credit Card Fraud Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (November 30, 2022): 988–93. http://dx.doi.org/10.22214/ijraset.2022.47456.
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Dellalana, Laura, Reid A. Waldman, Steven D. Waldman, and Jane M. Grant-Kels. "Credit card on file." Journal of the American Academy of Dermatology 82, no. 2 (February 2020): 528–29. http://dx.doi.org/10.1016/j.jaad.2018.12.016.
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Swami, Mrs M. M., Rushikesh Ghuge, Gurshan Singh, Harsh Tiwari, and Rohan Kalaskar. "Credit Card Fraud Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 2368–71. http://dx.doi.org/10.22214/ijraset.2022.47769.
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Abstract: Due to exponential growth in the field of online transactions, credit cards are widely used in most financial aspects and hence there are more risks of fraudulent transactions. These fraudulent transactions can be shown by analysing several behaviours of credit card users from earlier transaction history datasets. If any abnormality is noticed in the behaviour from the existing patterns, there is the possibility of fraudulent transaction. In this project the proposed will use Ensemble Learning Algorithms (XGBoost). By using these models, the proposed system will predict if the transaction is fraudulent or genuine. Therefore, by the implementation of this methodology in fraud detection systems, monetary losses which are caused due to fraudulent transactions can be decreased.
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Toraman, Cengiz, Yunus Kılıç, and Mehmet Fatih Buğan. "Credit Card Literacy Levels And Credit Card Usage Behaviors Of College Students." Journal of Business Research - Turk 8, no. 4 (December 30, 2016): 266. http://dx.doi.org/10.20491/isarder.2016.218.
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Gan, Christopher E. C., David A. Cohen, Baiding Hu, Minh Chau Tran, Weikang Dong, and Annie Wang. "The relationship between credit card attributes and the demographic characteristics of card users in China." International Journal of Bank Marketing 34, no. 7 (October 3, 2016): 966–84. http://dx.doi.org/10.1108/ijbm-09-2015-0133.
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Purpose The purpose of this paper is to investigate the impact that several of these factors have on a consumer’s decision to hold a credit card, as well as those involved in determining the level of credit card limit. Design/methodology/approach Potential explanatory variables were identified in the literature, then used to build a binary logit model to test the impact of the card and consumer characteristics on credit card ownership. Data were collected via a structured interview of 409 consumers living in Hebei Province, China. Findings The results indicate that convenience in use, level of credit card interest rates, the application process, number of people in the household, a rewards programme, marital status, credit limit and age influence the likelihood of the respondent holding a credit card. Further, an anaylsis shows that the number of credit cards held, duration of holding a credit card, monthly credit card purchasing volume and having a degree at the tertiary level, are significantly and positively related to different levels of credit limit. Originality/value In summary, in order to attract more consumers to credit card use, the banks and credit card companies should consider making it more convenient for consumers to use their credit cards. Moreover, banks can increase their networking and degree of cooperation with merchants to increase the acceptance of payment by credit card. The most heavily used businesses such as supermarkets and smaller retailers, where consumers purchase goods frequently, would be good targets for banks’ attention. In addition, banks might also improve credit card reward programmes to make these more efficient and perhaps increase the size of the rewards customers can earn through card use.
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Brink, Sophia. "The accounting treatment of credit card rewards programmes: a South African perspective (Part 1)." Journal of Economic and Financial Sciences 10, no. 1 (June 6, 2017): 107–24. http://dx.doi.org/10.4102/jef.v10i1.8.
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Credit card rewards programmes are a common phenomenon in the South African market. On 1 July 2007 the International Accounting Standards Board (IASB) issued IFRIC 13 Customer Loyalty Programmes to give specific guidance to suppliers on the accounting treatment of customer loyalty programme transactions. Although 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). 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. Currently there is uncertainty whether or not a credit card rewards programme transaction falls within the scope of IFRS 15. Despite concerns raised the Boards decided against providing any additional guidance to credit card rewards programmes and indicated that they leave it up to management
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Winter, Raymond. "Euro-based credit card contains in-card sensor." Biometric Technology Today 8, no. 5 (May 2000): 4. http://dx.doi.org/10.1016/s0969-4765(00)05006-2.
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., Aakash, Akash Kuma, and Ravi Pratap Singh. "Credit Card Fraud Detection System." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 3850–52. http://dx.doi.org/10.22214/ijraset.2022.43240.
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Abstract: In today’s world everything is online which also increases the chances of fraud. There are approximately 36.4 percent fraud related to commercial cards which include credit card, debit card, etc. In 2022 there are 64 million people who uses credit card to initiate the transaction, therefore they are also prone to the card fraud. So, in this document we talk about the various machine learning algorithms to predict the credit card fraud based on the previous transaction pattern. Keywords: K nearest neighbour, Machine learning, Credit card, decision tree, random forest
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Dewri, Leo Vashkor, Md Rashidul Islam, and Netai Kumar Saha. "Behavioral Analysis of Credit Card Users in a Developing Country: A Case of Bangladesh." International Journal of Business and Management 11, no. 4 (March 29, 2016): 299. http://dx.doi.org/10.5539/ijbm.v11n4p299.
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<p>Economic structure plays a vital role to drive consumer spending attitudes in different countries. Bangladesh is considering as a lower middle income country that indicates citizen of Bangladesh doesn’t have significant ability to increase their spending habits. Bangladeshis’ are experiencing credit card not more than two decades. Also accepting credit card by wider merchants in lieu of payment is also comparatively new practice in Bangladesh. In this regard, the financial institutions / credit card issuers and retailers are experiencing new spending behaviors of credit card holders. Primarily, the research attempts to investigate behavioural usage patterns of credit card users in the emerging economics. Secondly, how the external factors are influencing the credit card users to use credit cards in their day-to-day life. To conduct the research 500 credit card holders are approached of which 393 credit card holders responded and been analyzed. The research concludes that there is significant relationship among – earnings and using full credit limit; different age group has diverse tendency to use credit card and repayment attitudes; profession and usage behavior of credit card; e-repayment attitudes to pay bill by different age groups. This study also reveals that there is no-significant relationships among gender differences is not an concern for using credit card; single credit card features are truly not motivating credit card users to use credit card frequently; interestingly external factors (like: discount offers or other card facilities) are not only driving force to encourage credit card holders to use their credit cards frequently. The study recommends fragmenting the credit card market in Bangladesh based on consumer demographics and attitudes towards using short/midterm debt. </p>
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Mohd Dali, Nuradli Ridzwan Shah, Shumaila Yousafzai, and Hanifah Abdul Hamid. "Credit cards preferences of Islamic and conventional credit card." Journal of Islamic Marketing 6, no. 1 (March 9, 2015): 72–94. http://dx.doi.org/10.1108/jima-05-2013-0039.
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Purpose – The main aim of this paper is to identify whether certain consumers behave irrationally when it comes to select banking products. This paper builds on one of the most significant banking products that is the credit card. Design/methodology/approach – This is an exploratory research paper. Therefore, only descriptive analysis on the differences between three credit card user groups such as the Islamic credit card users, conventional card users and users who decide to use both credit cards, conventional and Islamic, were presented. Findings – The demographic and psychographic factors for the three different groups differ. In addition, there are four factors that influence the credit card selection. The factors are insurance/takaful provided by the credit issuers, cost associated with the credit card, the reward points programme offered and the convenience factors. Furthermore, the study found that three of the factors except insurance/takaful are significantly different between three credit card groups. Research limitations/implications – This paper is limited to the context of Malaysia and the respondents are mostly from the same ethnic. Therefore, it could not be generalised in the context of other countries and further studies comparing different culture or ethnic could benefit and enrich the topic of study. Practical implications – The Islamic and conventional banks could focus on several factors influencing customers’ selection and could focus to improve certain lacking areas as perceived by the consumers. The ability to increase the perceptions of the consumers regarding their credit cards will enable their products to be chosen in the market. Originality/value – There was a significant amount of literature discussed in the Islamic banking selection factors. However, little attention being paid to the selection of a specific bank’s product. This study offers a study that looks into the selection of the credit card offered by the banks in respect to the irrational behaviours of the religious consumers in economic activities as compared to the conventional economists. This paper will contribute to the body of existing literature of banking selection.
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Kim, Moon-Yong. "Evaluation, Satisfaction, and Loyalty in the Context of Green Credit Card Services: Green Ethics as a Moderator." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (April 11, 2021): 387–91. http://dx.doi.org/10.17762/turcomat.v12i5.971.
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This research aims to examine the relationships among evaluation of green credit card services, overall satisfaction with green credit card services, and loyalty to green credit cards.In addition, this search examines whether consumers’ green ethics moderates the relationship between evaluation of green credit card services and loyalty to green credit cards. An online survey (N = 2,000) was conducted to test the hypotheses. Consistent with all the hypotheses, the results indicate that (1) evaluation of green credit card services has a positive effect on overall satisfaction with green credit card services (hypothesis 1); (2) evaluation of green credit card services has a positive effect on loyalty to green credit cards (hypothesis 2); (3) overall satisfaction with green credit card services has a positive effect on loyalty to green credit cards (hypothesis 3); (4) overall satisfaction with green credit card services mediates the relationship between evaluation of green credit card services and loyalty to green credit cards (hypothesis 4); and (5) the magnitude of the positive effect of evaluation of green credit card services on loyalty to green credit cards increases as individuals’ green ethics decreases (hypothesis 5).
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Awanis, Sandra, and Charles Chi Cui. "Consumer susceptibility to credit card misuse and indebtedness." Asia Pacific Journal of Marketing and Logistics 26, no. 3 (June 3, 2014): 408–29. http://dx.doi.org/10.1108/apjml-09-2013-0110.
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Purpose – Prior research suggests that payment mechanisms are imbued with cues that affect purchase evaluation and future spending behavior. Credit cards are distinguished from other payment mechanisms as they elicit greater willingness to spend, prompt weaker recollections of past credit expenses and overvaluation of available funds – a phenomena the authors call as “credit card effect.” Little is known about the individuals’ differential exposure to the credit card effect. The purpose of this paper is to present a new concept and measure of susceptibility to the credit card misuse and indebtedness (SCCMI). Design/methodology/approach – The study focussed on young credit card users (aged 18-25) from Malaysia, Singapore, and the UK as they represent varying levels of credit card issuance and consumer protection regulations. The authors conducted confirmatory factor analysis and invariance tests to assess the validity, reliability and parsimony of the proposed scale in the three countries. Further, the authors examined the prediction power of SCCMI on consumer tendency to become a revolving credit card debtor. Findings – Results show that the SCCMI scale is valid, reliable and parsimonious across the multi-country context. The paper provided additional validity support through known-group comparison among various payers of credit card bills. Research limitations/implications – The convenience sampling used for the study is the main limitation. The findings bear important implications for more socially responsible marketing practice and better public policies in credit carder regulation for protecting young credit card users. Practical implications – The new concept and measurement scale can be used for identifying the vulnerable individuals in credit card use, assisting consumer knowledge training, improving policies for credit card regulation, and helping credit card providers in socially responsible marketing practice. Social implications – The cross-country validity of the SCCMI scale provides a unique contribution for monitoring and auditing consumer vulnerability in credit card misuse in Asian and European market conditions. Originality/value – SCCMI offers an original concept that is distinct from previous research in that SCCMI focusses solely on the state of likelihood to commit credit card abuse rather than the behavioral manifestations of credit card misuse. SCCMI provides a new tool for marketers and public policy makers for ethically responsible credit card marketing and regulation to protect youths’ benefits.
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Selvanathan, Mahiswaran, Dineswary Nadarajan, Yong Wei Yee, and Faria Rabbi. "Credit Card Debt in Kota Damansara, Selangor: An Investigation of Credit Card Debt Determinants and Factors." International Journal of Human Resource Studies 6, no. 3 (October 1, 2016): 87. http://dx.doi.org/10.5296/ijhrs.v6i3.10091.
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This paper explores factors influencing credit card debts in Kota Damansara, Selangor, Malaysia. Specifically, variables such as attitude, income, financial knowledge and bank policies are examined. The questionnaire in the study was distributed among 120 credit card holders. The study serves as guide researchers to extend the research work covering more variables in different economies. The data is collected using simple random sampling techniques. The results indicate that attitude, income, financial knowledge and bank policies have significant relationshipwith credit card debts. The contribution to this result can help to developing market economies or even developed countries where credit card debt is high. It gives awareness to the banker on understanding their credit card consumers, as well as providing insights to the credit card holders.
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Boehme, Rodney, Timothy Craft, and Richard LeCompte. "Credit Card Lending and the Performance of U.S. Credit Unions." International Journal of Finance & Banking Studies (2147-4486) 12, no. 2 (June 14, 2023): 01–12. http://dx.doi.org/10.20525/ijfbs.v12i2.2605.
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Is credit card lending by credit unions within the United States primarily a service to members or a profit generating product? This paper examines the impact of credit card lending on the performance of U.S. credit unions from 2000-2017. A panel data approach using fixed effects regression methodology is undertaken to make comparative analyses of credit union performance across several dimensions including the percentage of the firm’s assets in credit card loans and percent of members with a credit card. Credit unions are stratified into deciles by size and significant results are found using this methodology. Controlling for delinquencies and charge-offs among other variables, credit card lending significantly increases ROA for both large and small credit unions, but only after the Financial Crisis of 2008, and the establishment of the CARD Act in 2009. Interestingly, the ROA of small credit unions significantly increases with the percentage of members using the institution’s credit card. This result suggests that small credit unions would benefit by increasing the penetration of credit cards within their membership base.
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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 (April 30, 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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Mitra, Anurag, Mukul Siddhant, and Gururama Senthilvel P. "Credit Card Fraud Detection using Autoencoders." YMER Digital 21, no. 06 (June 12, 2022): 337–42. http://dx.doi.org/10.37896/ymer21.06/32.
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In today’s life or economy credit card plays a veryimportant role. Credit card becomes a necessary part of business, household and bank transactions. Using of credit card carefully and responsibly gives a enormous benefit to the user, fraudulent activities happen and give financial damage to the user or card holder. The growth of E-commerce industry led to use of credit card or many platform for online purchase or different transaction because of this the fraudulent activities was also increased. Bank facing many issues for detecting the credit card fraudulent transactions. For finding the fraud of credit card machine learning plays a vital role. For predicting these fraud detection of credit card we use many machine learning methods or algorithms, past data we collect and analyze it and make a machine learning model to detect the fraudulent activities which going to happen. The performance of fraud detecting in credit card transaction is greatly affected by sampling approach on data-set, selection of variable and detection technique used. This project objective to use of efficient approach to detect automatic fraud related to banks or insurance company using deep learning algorithm called autoencoder. We are using the European card holder real-time dataset of September 2013. The data was unbalanced in the dataset so for this autoencoder is perfect to provide the accurate results. We can reconstruct the normal data through the autoencoder and anomalies was detected at the time of reconstruction error threshold and consider the anomalies. Keyword: fraud detection, unsupervised learning, autoencoder, credit card
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Zhang, Jiayin. "Credit Card Fraud Detection Using Predictive Model." BCP Business & Management 38 (March 2, 2023): 2820–26. http://dx.doi.org/10.54691/bcpbm.v38i.4196.
Full textAbstract:
Credit card is a sign of credit that is given to customers with good credit by a commercial bank or credit card firm. It takes the shape of a card with signature blank space on the back and the name of the dissipated bank, expiration date, CVS number, and cardholder name on the front. A credit card is a payment card that can give the cardholders’ abilities to enable the cardholder to exchange for goods and services based on their credibility and debt score. In this paper, it will explore the credit card fraud detection predictive model to avoid fraudulent activity. Through different algorithms, the study could easily show the potentially fraudulent activities in the given dataset. In order to effectively combat credit card fraud, a number of techniques have been developed and put into practice, including different supervised and unsupervised machine learning algorithms to predict fraudulent activities. These techniques will be used to compare between the actual dataset and estimated models to illustrate the full picture. The credit card fraud is a challenging problem, especially it is prevalent during college students.
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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 (April 30, 2023): 1559–63. http://dx.doi.org/10.21474/ijar01/16824.
Full textAbstract:
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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Yu, Chenyao. "Credit Card Customers' Data Visualization." Advances in Education, Humanities and Social Science Research 5, no. 1 (May 12, 2023): 336. http://dx.doi.org/10.56028/aehssr.5.1.336.2023.
Full textAbstract:
Nowadays, there are many people who have credit cards, but the frequency of using them varies according to some personal factors, so many bank managers are worried because of the loss of some customers. Therefore, in this report, I have used a data set of 10,000 credit card holders' personal information and then selected and visualised this data. This will give a better understanding of the current cardholders and perhaps some banks or credit card companies can use the data to come up with effective solutions to recover some of their lost customers and find some potential credit card customers. According to the data analysis, it is not difficult to find that the current credit card users are mainly middle-age, with undergraduate degrees, and have an income below 40k dollars, with a total transfer amount between 2k-3k dollars or between 4k-5k dollars.
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Hussain, M. Zubair. "The determinant of behavioral factors which influence on credit card usages." Journal of Educational Paradigms 2, no. 2 (December 15, 2020): 143–45. http://dx.doi.org/10.47609/0202052020.
Full textAbstract:
The purpose of this paper is to examine which behavior factor effect on credit card use. Among different behavior factors, which factor more positively and significantly influence credit card use? The data are collected from individuals who have a credit card through the Lahore questionnaire. The finding shows a strong influence of behavior factor on credit card uses especially perceived usefulness. And perceived risk has a positive impact on credit card use. The study is limited to only the service sector as a credit card. Convinces sampling technique is used. For further research, influence on customer or client income also takes into consideration. This research is no precious in Pakistan's credit card, which studies behavior influences credit card use.
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LOKE, YIING JIA, STEVEN T. YEN, and ANDREW K. G. TAN. "CREDIT CARD OWNERSHIP AND DEBT STATUS IN MALAYSIA." Singapore Economic Review 58, no. 03 (September 2013): 1350016. http://dx.doi.org/10.1142/s0217590813500161.
Full textAbstract:
This paper examines the role of socio-demographic and credit consumption tendencies in affecting credit card ownership and debt status. Based on a sample of 938 individuals from three major cities in Malaysia, card holders' debt status is measured in relation to credit card expenditure, which in turn is categorized into convenience users, low-risk credit revolvers and high-risk credit revolvers. While socio-demographic factors play significant roles in determining card ownership, card holders' credit consumption tendencies, such as past debt history and type of loan possessed, have varying adverse effects on the card holder's debt status.
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Saini, Rashi, and Prof Bipin Pandey. "Credit Card Fraud Detection project." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2113–18. http://dx.doi.org/10.22214/ijraset.2022.41704.
Full textAbstract:
Abstract: For some time, there has been a strong interest in the ethics of banking (Molyneaux, 2007; George, 1992), as well as the moral complexity of fraudulent behavior (Clarke, 1994). Fraud means obtaining services/goods and/or money by unethical means, and is a growing problem all over the world nowadays. Fraud deals with cases involving criminal purposes that, mostly, are difficult to identify. Credit cards are one of the most famous targets of fraud but not the only one; fraud can occur with any type of credit products, such as personal loans, home loans, and retail. Furthermore, the face of fraud has changed dramatically during the last few decades as technologies have changed and developed. A critical task to help businesses and financial institutions including banks is to take steps to prevent fraud and to deal with it efficiently and effectively, when it does happen (Anderson, 2007). Anderson (2007) has identified and explained the different types of fraud, which are as many and varied as the financial institution’s products and technologies, such as Transaction products: credit and debit cards and checks, Relationship to accounts first, second and third parties, Business processes: application and transaction, Manner and timing short versus long term, Identify misrepresentation: embellishment, theft and fabrication, Handling of transaction: lost or stolen, not received, skimming and at hand, Utilization counterfeit, not present, altered or unaltered, Technologies ATM and Internet. Solutions for integrating sequential information in the feature set exist in the literature. The predominant one consists in creating a set of features which are descriptive statistics obtained by aggregating the sequences of transactions of the cardholders (sum of amount, count of transactions, location from where the payment is being made etc..). We used this method as a benchmark feature engineering method for credit card fraud detection. However, this feature engineering strategy raised several research questions. First of all, we assumed that these descriptive statistics cannot fully describe the sequential properties of fraud and genuine patterns and that modelling the sequences of transactions could be beneficial for fraud detection. Moreover, the creation of these aggregated features is guided by expert knowledge whereas sequence modelling could be automated thanks to the class labels available for past transactions. Finally, the aggregated features are point estimates that may be complemented by a multi-perspective univariate description of the transaction context. We proposed a multi-perspective HMM-based automated feature engineering strategy in order to incorporate a broad spectrum of sequential information in the transactions feature sets. In fact, we model the genuine and fraudulent behaviors of the merchants and the card-holders according to two univariate features: the country from where the payment is being made and the amount of each of the transactions being made. Moreover, the HMMbased features are created in a supervised way and therefore lower the need of expert knowledge for the creation of the fraud detection system. In the end, our multiple perspectives HMM-based approach offers automated feature engineering to model temporal correlations so as to complement and possibly supplement the use of transaction aggregation strategies in order to improve the effectiveness of the classification task. Experiments conducted on a large real world credit card transaction dataset (46 million transactions from belgium card-holders between March and May 2015) have shown that the proposed HMMbased feature engineering allows for an increase in the detection of fraudulent transactions when combined with the state-ofthe-art expert-based feature engineering strategy for credit card fraud detection. To conclude, this work leads to a better understanding of what can be considered contextual knowledge for a credit card fraud detection task and how to include it in the classification task in order to get an increase in fraud detection. The method proposed can be extended to any supervised task with sequential datasets. The main aims are, firstly, to identify the different types of credit card fraud, and, secondly, to review alternative techniques that have been used in fraud detection. Indeed, transaction products, including credit cards, are the most vulnerable to fraud. On the other hand, other products such as personal loans and retail are also at risk, and have serious ethical conflicts. Keywords: Behavior and Location Analysis (BLA); Fraud Detection System (FDS); Automated Teller Machine (ATM); Credit Card Fraud Detection; DB: Database.
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Saini, Rashi, and Prof Bipin Pandey. "Credit Card Fraud Detection project." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2113–18. http://dx.doi.org/10.22214/ijraset.2022.41704.
Full textAbstract:
Abstract: For some time, there has been a strong interest in the ethics of banking (Molyneaux, 2007; George, 1992), as well as the moral complexity of fraudulent behavior (Clarke, 1994). Fraud means obtaining services/goods and/or money by unethical means, and is a growing problem all over the world nowadays. Fraud deals with cases involving criminal purposes that, mostly, are difficult to identify. Credit cards are one of the most famous targets of fraud but not the only one; fraud can occur with any type of credit products, such as personal loans, home loans, and retail. Furthermore, the face of fraud has changed dramatically during the last few decades as technologies have changed and developed. A critical task to help businesses and financial institutions including banks is to take steps to prevent fraud and to deal with it efficiently and effectively, when it does happen (Anderson, 2007). Anderson (2007) has identified and explained the different types of fraud, which are as many and varied as the financial institution’s products and technologies, such as Transaction products: credit and debit cards and checks, Relationship to accounts first, second and third parties, Business processes: application and transaction, Manner and timing short versus long term, Identify misrepresentation: embellishment, theft and fabrication, Handling of transaction: lost or stolen, not received, skimming and at hand, Utilization counterfeit, not present, altered or unaltered, Technologies ATM and Internet. Solutions for integrating sequential information in the feature set exist in the literature. The predominant one consists in creating a set of features which are descriptive statistics obtained by aggregating the sequences of transactions of the cardholders (sum of amount, count of transactions, location from where the payment is being made etc..). We used this method as a benchmark feature engineering method for credit card fraud detection. However, this feature engineering strategy raised several research questions. First of all, we assumed that these descriptive statistics cannot fully describe the sequential properties of fraud and genuine patterns and that modelling the sequences of transactions could be beneficial for fraud detection. Moreover, the creation of these aggregated features is guided by expert knowledge whereas sequence modelling could be automated thanks to the class labels available for past transactions. Finally, the aggregated features are point estimates that may be complemented by a multi-perspective univariate description of the transaction context. We proposed a multi-perspective HMM-based automated feature engineering strategy in order to incorporate a broad spectrum of sequential information in the transactions feature sets. In fact, we model the genuine and fraudulent behaviors of the merchants and the card-holders according to two univariate features: the country from where the payment is being made and the amount of each of the transactions being made. Moreover, the HMMbased features are created in a supervised way and therefore lower the need of expert knowledge for the creation of the fraud detection system. In the end, our multiple perspectives HMM-based approach offers automated feature engineering to model temporal correlations so as to complement and possibly supplement the use of transaction aggregation strategies in order to improve the effectiveness of the classification task. Experiments conducted on a large real world credit card transaction dataset (46 million transactions from belgium card-holders between March and May 2015) have shown that the proposed HMMbased feature engineering allows for an increase in the detection of fraudulent transactions when combined with the state-ofthe-art expert-based feature engineering strategy for credit card fraud detection. To conclude, this work leads to a better understanding of what can be considered contextual knowledge for a credit card fraud detection task and how to include it in the classification task in order to get an increase in fraud detection. The method proposed can be extended to any supervised task with sequential datasets. The main aims are, firstly, to identify the different types of credit card fraud, and, secondly, to review alternative techniques that have been used in fraud detection. Indeed, transaction products, including credit cards, are the most vulnerable to fraud. On the other hand, other products such as personal loans and retail are also at risk, and have serious ethical conflicts. Keywords: Behavior and Location Analysis (BLA); Fraud Detection System (FDS); Automated Teller Machine (ATM); Credit Card Fraud Detection; DB: Database.
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Madkaikar, Kartik, Manthan Nagvekar, Preity Parab, Riya Raika, and Supriya Patil. "Credit Card Fraud Detection System." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 2 (July 30, 2021): 158–62. http://dx.doi.org/10.35940/ijrte.b6258.0710221.
Full textAbstract:
Credit card fraud is a serious criminal offense. It costs individuals and financial institutions billions of dollars annually. According to the reports of the Federal Trade Commission (FTC), a consumer protection agency, the number of theft reports doubled in the last two years. It makes the detection and prevention of fraudulent activities critically important to financial institutions. Machine learning algorithms provide a proactive mechanism to prevent credit card fraud with acceptable accuracy. In this paper Machine Learning algorithms such as Logistic Regression, Naïve Bayes, Random Forest, K- Nearest Neighbor, Gradient Boosting, Support Vector Machine, and Neural Network algorithms are implemented for detection of fraudulent transactions. A comparative analysis of these algorithms is performed to identify an optimal solution.
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Peela, Harsha Vardhan, Tanuj Gupta, Nishit Rathod, Tushar Bose, and Neha Sharma. "Prediction of Credit Card Approval." International Journal of Soft Computing and Engineering 11, no. 2 (January 30, 2022): 1–6. http://dx.doi.org/10.35940/ijsce.b3535.0111222.
Full textAbstract:
Credit risk as the board in banks basically centers around deciding the probability of a customer's default or credit decay and how expensive it will end up being assuming it happens. It is important to consider major factors and predict beforehand the probability of consumers defaulting given their conditions. Which is where a machine learning model comes in handy and allows the banks and major financial institutions to predict whether the customer, they are giving the loan to, will default or not. This project builds a machine learning model with the best accuracy possible using python. First we load and view the dataset. The dataset has a combination of both mathematical and non-mathematical elements, that it contains values from various reaches, in addition to that it contains a few missing passages. We preprocess the dataset to guarantee the AI model we pick can make great expectations. After the information is looking great, some exploratory information examination is done to assemble our instincts. Finally, we will build a machine learning model that can predict if an individual's application for a credit card will be accepted. Using various tools and techniques we then try to improve the accuracy of the model. This project uses Jupyter notebook for python programming to build the machine learning model. Using Data Analysis and Machine Learning, we attempted to determine the most essential parameters for obtaining credit card acceptance in this project. The machine learning model we built gave an 86 % accuracy for predicting whether the credit card will be approved or not, considering the various factors mentioned in the application of the credit card holder. Even though we achieved an accuracy of 86%, we conducted a grid search to see if we could increase the performance even further. However, using both the machine learning models: random forest and logistic regression, the best we could get from this data was 86 percent.
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Mathew, Alexander, Gayatri Moindi, Ketan Bende, and Neha Singh. "Credit Card Fraud Prediction System." Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 2, no. 3 (March 31, 2015): 30–35. http://dx.doi.org/10.53555/nncse.v2i3.481.
Full textAbstract:
Identity crime is common, and pricey, and credit card fraud is a specific case of identity crime. The existing systems of known fraud matching and business rules have restrictions. To remove these negative aspects in real world, this paper proposes a data mining approach: Communal Detection (CD) and Spike Detection (SD). CD finds real social relationships to reduce the suspicion score, and is impervious to fake social relationships. This approach on a fixed set of attributes is whitelist-oriented. SD increases the suspicion score by finding discrepancies in duplicates. These data mining approaches can detect more types of attacks and removes the unnecessary attributes.
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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 (March 31, 2022): 776–80. http://dx.doi.org/10.22214/ijraset.2022.40744.
Full textAbstract:
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