Credit Card Holders Research Articles

Marketing to multiple audiences simultaneously using strategic ambiguity: A study of credit card choice behavior

ABSTRACT In marketing efforts, messages are commonly tailored to audiences to ensure effective outcomes. One way to tailor messages consists in providing arguments that appeal to the characteristics of a specific target audience. However, this strategy can decrease the message’s appeal to a non-target audience. Drawing on the concept of strategic ambiguity, an alternative strategy to tailor messages is proposed and tested. Strategically ambiguous messages provide arguments that appeal to several audiences (multiple audience tailoring). These messages combine attributes that are relevant to distinct audiences, and audiences are expected to pay selective attention to attributes that are most relevant for them. The effectiveness of one-audience and multiple-audience tailoring was compared in an experiment with credit-card holders. Single audience tailoring was the most effective form of tailoring. Multiple audience tailoring was more effective than unmatched tailoring in various conditions. The study enhances our understanding of message tailoring using ambiguity to increase marketing effectiveness.

Enhancing Security in Digital Payments: A Comparative Evaluation of Machine Learning Models for Credit Card Fraud Detection

Nowadays, credit card transactions are exponentially increasing as a form of online payment. That creates technical pressure on the financial institution to incur losses due to credit card fraud. It is no wonder that credit card fraud is making people feel insecure and unsafe about using the services provided by banks. Data mining reasons on offline and online transactions can cause fraud detection. Number one is the changes in the behaviour of the frauds that are changing, and number two is the fraudulent dataset, which is asymmetric. In addition, the variables and techniques used by the researcher can impact fraud detection activities for credit cards. Therefore, the paper intends to investigate the suitability of the k-nearest-neighbour, decision tree, support vector machine, logistic regression, and Catboost. The data set has a total of 284807 transactions coming from the European credit card holder. Evaluation of the performance can be measured through the specificity, accuracy, sensitivity, precision, and lastly, the recall rate. After completing the comprehensive cross-validation, it was discovered that the catboost’s accuracy was extraordinary, which amounted to 93.39%, leaving the other classification matrix far behind.

CREDIT CARD FRAUD DETECTION USING A STACKING ENSEMBLE APPROACH WITH LSTM AND RANDOM FOREST MACHINE LEARNING TECHNIQUES

Credit cards play an essential role in today’s digital economy, and their usage has recently grown tremendously, accompanied by a corresponding increase in credit card fraud. Machine learning (ML) algorithms have been utilized for credit card fraud detection. However, the dynamic shopping patterns of credit card holders and the class imbalance problem have made it difficult for ML classifiers to achieve optimal performance. This research project aims to develop a reliable credit card fraud detection system through a stacking ensemble method, integrating LSTM and Random Forest machine learning techniques. This approach aims to enhance fraud detection accuracy by leveraging the diverse strengths of both models. The system will undergo rigorous evaluation to ensure its efficiency in identifying fraudulent transactions while minimizing false positives. By combining the temporal sequencing capabilities of LSTM with the decision-making process of Random Forest, the proposed approach seeks to achieve heightened sensitivity to fraudulent patterns while maintaining computational efficiency. Ultimately, the objective is to bolster security measures and protect financial institutions and consumers from potential fraud risks.

Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms

People can use credit cards for online transactions as it provides an efficient and easy-to-use facility. With the increase in usage of credit cards, the capacity of credit card misuse has also enhanced. Credit card frauds cause significant financial losses for both credit card holders and financial companies. In this research study, the main aim is to detect such frauds, including the accessibility of public data, high-class imbalance data, the changes in fraud nature, and high rates of false alarm. The relevant literature presents many machines learning based approaches for credit card detection, such as Extreme Learning Method, Decision Tree, Random Forest, Support Vector Machine, Logistic Regression and XG Boost. However, due to low accuracy, there is still a need to apply state of the art deep learning algorithms to reduce fraud losses. The main focus has been to apply the recent development of deep learning algorithms for this purpose. Comparative analysis of both machine learning and deep learning algorithms was performed to find efficient outcomes. The detailed empirical analysis is carried out using the European card benchmark dataset for fraud detection. A machine learning algorithm was first applied to the dataset, which improved the accuracy of detection of the frauds to some extent. Later, three architectures based on a convolutional neural network are applied to improve fraud detection performance. Further addition of layers further increased the accuracy of detection. A comprehensive empirical analysis has been carried out by applying variations in the number of hidden layers, epochs and applying the latest models. The evaluation of research work shows the improved results achieved, such as accuracy, f1-score, precision and AUC Curves having optimized values of 99.9%,85.71%,93%, and 98%, respectively. The proposed model outperforms the state-of-the-art machine learning and deep learning algorithms for credit card detection problems. In addition, we have performed experiments by balancing the data and applying deep learning algorithms to minimize the false negative rate. The proposed approaches can be implemented effectively for the real-world detection of credit card fraud

Impact of customer satisfaction and service quality on membership renewal: A study on Malaysian credit card holders

With the aim of finding out the antecedents of credit card renewal among the customers of the private banks in Malaysia. This study is specifically targeted to sort out the impact of five mostly practiced dimensions of service quality and customer satisfaction on credit card renewals. With a deductive approach, 384 observations as primary data were collected through an online survey by utilizing a self-administered, fully structured questionnaire, and the data has been analyzed with the help of SPSS and SmartPLS. There is a well-rounded and productive connection between the satisfaction of the customer and the tangible dimension. The link between customer satisfaction and empathy shows a positive connection and relationship between variables, for example, empathy and customer satisfaction. A significant relationship between satisfaction and renewal of credit card membership has been observed when independent variables are service quality, empathy, reliability, tangible assurance, and responsiveness. The area and premises of customer satisfaction are extremely diverse, and even when much research could be done, all the aspects of psychology, sociology, or behaviors have been covered and are beneficial for both authors and practitioners. Limitations can be highlighted as the gaps, restraints, and occurrences that could emerge while doing the research work, and they partially affect the end result and outcomes of the research.

Enhancing Credit Card Fraud Detection: An Ensemble Machine Learning Approach

In the era of digital advancements, the escalation of credit card fraud necessitates the development of robust and efficient fraud detection systems. This paper delves into the application of machine learning models, specifically focusing on ensemble methods, to enhance credit card fraud detection. Through an extensive review of existing literature, we identified limitations in current fraud detection technologies, including issues like data imbalance, concept drift, false positives/negatives, limited generalisability, and challenges in real-time processing. To address some of these shortcomings, we propose a novel ensemble model that integrates a Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Bagging, and Boosting classifiers. This ensemble model tackles the dataset imbalance problem associated with most credit card datasets by implementing under-sampling and the Synthetic Over-sampling Technique (SMOTE) on some machine learning algorithms. The evaluation of the model utilises a dataset comprising transaction records from European credit card holders, providing a realistic scenario for assessment. The methodology of the proposed model encompasses data pre-processing, feature engineering, model selection, and evaluation, with Google Colab computational capabilities facilitating efficient model training and testing. Comparative analysis between the proposed ensemble model, traditional machine learning methods, and individual classifiers reveals the superior performance of the ensemble in mitigating challenges associated with credit card fraud detection. Across accuracy, precision, recall, and F1-score metrics, the ensemble outperforms existing models. This paper underscores the efficacy of ensemble methods as a valuable tool in the battle against fraudulent transactions. The findings presented lay the groundwork for future advancements in the development of more resilient and adaptive fraud detection systems, which will become crucial as credit card fraud techniques continue to evolve.

Implementasi Perlindungan Hukum Dan Penyelesaian Perselisihan Transaksi Fraud Bagi Pemegang Kartu Kredit

The aim of this research is to discover, analyze and examine the practice/ implementation of legal protection for credit card holders and forms of consumer protection in resolving fraudulent transaction disputes.
 The research method used in this research is normative juridical using a qualitative approach. Apart from that, a literature approach and a statutory approach are also used which also include a conceptual approach and a case approach.
 Credit cards are a form of innovation in the banking business which is increasingly developing and banking transactions using credit cards are becoming more massive. However, if negligence or fraud occurs, the bank's position is very dominant which will of course prioritize the bank's own interests. Therefore, currently the position and interests of customers have not been properly protected.
 The demand for banks to act professionally while protecting their credit card holders is increasing, thus encouraging banks to adjust their efforts to serve their customers, one of which is by creating innovations while remaining efficient. This is clearly seen in the agreement between the bank and the customer or the provisions regarding the use of bank services or products which are determined unilaterally by the bank, so that in such conditions if a problem arises later it cannot be resolved quickly with clear responsibilities. One example is the Indonesian Supreme Court Decision No.1527 K/Pdt/2015. regarding the Bank Mandiri Tbk case. VS Mr Sutrisno. Based on this decision, even though Mr. Sutrisno had obtained legal protection that had permanent legal force and fulfilled a sense of justice, this was done in approximately 3 (three) years, where previously Sutrisno as a credit card holder had been harmed by a Bank Mandiri credit card product that had never been he uses but has a number of obligations that must be paid.

Analisis Perilaku Konsumsi terhadap Kartu Kredit PT. Bank Rakyat Indonesia (Persero) TBK Wilayah Sumba Barat

This study aims to analyze consumption behavior towards the use of PT Bank Rakyat Indonesia (BRI) credit cards. This research uses a quantitative approach by collecting data through an online survey to BRI credit card holders. The sample of this study consisted of 32 respondents who were randomly selected. The variables analyzed include factors that influence credit card consumption behavior, such as income, perceptions of credit cards, shopping habits, gender, age, occupation and credit card usage preferences. The purpose of this research is to see how influential the lifestyle and environment in West Sumba Regency which can be said to be still developing, this research uses a descriptive qualitative approach.

A Study On The Awareness, Utilization And Problems Of Using Kisan Credit Card Of Canara Bank [With Special Reference To Kanchepuram District

The KCC instrument would allow farmers to purchase agriculture inputs such as seeds, fertilizers, pesticides and also allow them to withdraw some cash for meeting their other crop production related requirements. Objective of the study (i)To study about the awareness of the Kisan credit card holders and their attitude towards Kisan credit card (ii)To review the extend of utilization of Kisan credit card in Kanchepuram district. The sampling unit is confined to the Kanchepuram district. The primary data were collected from the farmers interview schedule was constructed to elicit information from farmers regarding Kisan credit card. The secondary data necessary for the study have been collected through pamphlets, broachers, annual reports and of the published papers of Canara bank. The sample size for the study is 66 respondents. Even though there are various agricultural credits provided by the banks.

KAJIAN HUKUM PERLINDUNGAN KONSUMEN TERHADAP KONSUMEN YANG MENGGUNAKAN KARTU KREDIT

The dynamics of the development of an increasingly modern global society along with the development of its economy has also influenced the pattern of life of the people themselves. Credit cards as an aspect of modern society's life have become a means of payment in lieu of cash which is increasingly in demand by the public or consumers today, because they have their own prestige, and have even become a lifestyle trend in big cities. This is also supported by the many shopping centers that serve cashless payments. In Law Number 8 of 1999 Article 1 paragraph (1) concerning Consumer Protection, it states that "Consumer protection is all efforts that guarantee legal certainty to provide protection to consumers." The general objective of writing this thesis is to explain how to study consumer protection law for consumers who use credit cards. The increase the use of credit cards is certainly triggered many benefits that can experienced credit card holders, especially the ease of making transactions and security for consumers because they do not need to carry large amounts of cash. The approach to using a credit card is not only carried out in terms of economic needs, but must also be supported by a legal approach, so that it is recognized and applies in economic law relations. This type of research empirical legal research and the nature of research is descriptive qualitative and the data used are primary and secondary data.

CONTACTLESS PAYMENTS FRAUD DETECTION METHODS AND IS SOCIETY PREPARED TO RESIST: A CASE STUDY

The ability to use contactless payment technologies, non-cash payments and credit card payments is becoming almost an essential requirement for consumers and merchants in today's economic conditions. Different market sectors are rapidly adapting to these technologies and looking for the most convenient, secure, and fastest possible solutions that combine intelligent data processing, security, and business management functions. Millions of debit and credit card holders care about secure payments, the businesses that receive these payments are secure in terms of security, and the operators that process such incoming and outgoing payments are interested in innovative solutions that set them apart from the competition. Amid the COVID-19 pandemic, when e-commerce was growing exponentially, the global market for fraud detection and prevention, currently stands at USD 20.9 billion, and is expected to grow, until 2025 will rise to USD 38.2 billion by the end of the year; holds the market at 12.8 % annually. The US remains the dominant region in this market segment, but European countries are also increasingly investing in fraud prevention and detection solutions, which are growing in demand in Europe due to an increase in cybercrime as well as advanced bots and cyber-attack.

Credit Card Customers' Data Visualization

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.

Effect of Credit Card Holders’ Age on the Credit Limit

Credit Limit of the credit cards is one of the affecting factors influencing people’s credit scores, playing a great role in determining their ability of paying debts and could be involved in banks evaluation in mortgage. In order to help people better assess their credit scores quantitatively and be aware of the influencing factors that determine their specific credit limits, this paper analyzed 10127 bank customers’ data. Based on the estimation, the evaluation is presented according to the information given to obtain the formula used to evaluate people’s Credit Limit and its relationship with some influencing factors, e.g., age, gender, education and marital status, and education level. According to the analysis from STATA, it is shown that per unit age increase would increase the credit limit by 1.76 percentage. In addition, impacts of other variables (e.g., gender, education level, marital status, income level, card type, card utilization ratio) are also found. The model presented could be used to help evaluating a persons’ Credit Limit according to the data provided. Overall, these results shed light on guiding further exploration of credit limit designs.

Credit Card Fraud Detection Using LSTM

As credit card becomes the most popular payment mode particularly in the online sector, the fraudu- lent activities using credit card payment technologies are rapidly increasing as a result. The purpose of this work is to develop a novel system for credit card fraud detection based on sequential modeling of data, using attention mechanism Long Short Term Mem- ory(LSTM) deep Recurrent Neural Networks(RNN) and Synthetic Minority Oversampling Technique Edited Nearest Neighbour(SMOTE-ENN). The pro- posed approach aims to capture the historic purchase behavior of credit card holders with the goal of im- proving fraud detection accuracy on new incoming transactions. Experiments show that our proposed model gives strong results and its accuracy is quite high. keywords: Credit Card, SMOTE-ENN, FraudDetection, Machine learning,LSTM.

A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions

Fraudsters are now more active in their attacks on credit card transactions than ever before. With the advancement in data science and machine learning, various algorithms have been developed to determine whether a transaction is fraudulent. We study the performance of three different machine learning models: logistic regression, random forest, and decision trees to classify, predict, and detect fraudulent credit card transactions. We compare these models’ performance and show that random forest produces a maximum accuracy of 96% (with an area under the curve value of 98.9%) in predicting and detecting fraudulent credit card transactions. Thus, we recommend random forest as the most appropriate machine learning algorithm for predicting and detecting fraud in credit card transactions. Credit Card holders above 60 years were found to be mostly victims of these fraudulent transactions, with a greater proportion of fraudulent transactions occurring between the hours of 22:00GMT and 4:00GMT.

BEHAVIORAL BIASES AND CREDIT CARD REPAYMENTS AMONG MALAYSIANS

Analyses done using the survey response of 451 credit cardholders in the northern region of West Malaysia have shown that credit cardholder repayment pattern differed based on their demographic profile. Specifically, the study found that credit cardholders who were younger and earned a lower income tended to make poorer repayment decisions. In contrast, those with higher academic qualifications tended to make better repayment decisions. Further analyses also revealed that behavioral biases were better at predicting those who made full payment of the outstanding balance and those who paid more than the minimum amount, but failed to predict those who only paid the minimum amount or less. In addition, it was found that overspending emerged as the strongest predictor which discriminated among the three repayment groups, followed by risk aversion, myopia and impulsiveness.

LEGAL ASPECTS OF USE OF CREDIT CARD IN TRADE AT BANDA ACEH CITY

Credit cards are one of the new instruments in the trading world that have a monetary value, so they can be used as a tool for transactions. However, credit cards cannot be called securities because they cannot be transferred. As a means of payment, the development of its use is increasingly widespread so that this has legal consequences for the parties involved in the use of the credit card, considering that the legal regulations are still relatively weak. The research method used is qualitative research with a normative juridical approach with descriptive analysis research specifications. This research uses secondary data as the main research material, which is obtained through documents. From the data obtained will be analyzed qualitatively normative. The legal aspect of using credit cards in trade in Banda Aceh City is that every agreement that occurs is incidental (a certain time) agreement, which arises at the time of buying and selling transactions or services. The mechanism of each credit card issuing bank is always different in giving approval, but the terms and procedures are relatively the same. Legal protection for the parties in using credit cards occurs at the time of signing the credit card approval agreement between the issuer and the credit card user, because legally there is no law that explicitly regulates it. It is recommended that there be wider legal protection efforts from the government, not only consumers in the sense of credit card holders, but in certain cases the sellers of goods/services and card issuers are also parties that need to receive balanced legal protection, including the affirmation of the rights of consumers. each party to sue the other party.

A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection

Credit cards play an essential role in today’s digital economy, and their usage has recently grown tremendously, accompanied by a corresponding increase in credit card fraud. Machine learning (ML) algorithms have been utilized for credit card fraud detection. However, the dynamic shopping patterns of credit card holders and the class imbalance problem have made it difficult for ML classifiers to achieve optimal performance. In order to solve this problem, this paper proposes a robust deep-learning approach that consists of long short-term memory (LSTM) and gated recurrent unit (GRU) neural networks as base learners in a stacking ensemble framework, with a multilayer perceptron (MLP) as the meta-learner. Meanwhile, the hybrid synthetic minority oversampling technique and edited nearest neighbor (SMOTE-ENN) method is employed to balance the class distribution in the dataset. The experimental results showed that combining the proposed deep learning ensemble with the SMOTE-ENN method achieved a sensitivity and specificity of 1.000 and 0.997, respectively, which is superior to other widely used ML classifiers and methods in the literature.

A Study of Credit Card Holders’ Repayment Pattern with Reference to Chennai City

In today’s world, everyone would have limited access to credit. But this access is available to all through one of the most valuable financial instruments i.e., Credit Cards. Credit Card is a boon as well as a bane. It can make or destroy the person who is using it. The Important factor of a Credit Card is the repayment of dues on a stipulated period. The study focuses on various aspects of repayment - how much amount will they pay towards credit card dues, how will they arrange funds to repay the debt, etc. The period of study is 2 months with a sample size of 100 through a questionnaire. The study uses ANOVA and Chi-Square Tests to analyze the factors that influence repayment patterns. The study reveals that there is no significant relationship between spending money and repayment of dues on time.

Factors determining the Motives for Using a Credit Card: An Empirical Study

In today's financial transactions, credit cards are a necessary component. Due to their convenience and flexibility, credit cards have been increasingly popular in India in recent years. With the help of a credit card, people can acquire money from a bank to spend on purchases or bill payments. However, depending on a variety of conditions, people's reasons for using credit cards can differ greatly. The person's financial circumstances are one of the key elements that define their reasons for utilizing a credit card. Individuals who have a reliable source of income and a clean credit history can use credit cards to accrue rewards points, cashback, and other incentives. On the other side, those with inconsistent income may resort to using credit cards to pay for unplanned purchases or fill up financial gaps. Spending patterns are another aspect that affects a person's reasons for using a credit card. Others may use credit cards for regular expenses to accrue incentives or establish credit history, while some people use them to make significant purchases that they cannot afford to pay for in full upfront. For consumers and financial institutions to use credit cards responsibly, it is essential that they have a thorough understanding of the variables that affect why people use them. This study makes a comparison of the single and multiple card holders’ motives for using the credit card with 168 credit card holders (84 from each category).