Credit Card Use Research Articles

Self-control and debt decisions relationship: evidence for different credit options

In recent years, households have become increasingly indebted, which constitutes a matter of concern. Based on a sample from the Spanish Survey of Financial Competences, this paper examines the relationship between self-control and consumer borrowing behaviour and, in so doing, it proposes a new multi-item scale of individual’s self-control in financial matters and considers several types of consumer debt, thus overcoming the main gaps identified in previous research. Empirical evidence reveals that, overall, self-control problems lead to more indebtedness. However, comprehensive analyses have demonstrated that the influence of self-control differs across an exhaustive range of credit options. In this regard, the lack of self-control increases the probability of taking out unsecured personal loans, loans from family or friends, and credit card use. Evidence suggests that individuals’ first impulse leads them to ask their social circle for a loan. Overall, individuals might be tempted to get indebted when they cannot make ends meet. However, this first impulse usually faces barriers from the supply perspective, so the evidence suggests that the effect of self-control on borrowing decisions might be shaped not only by the demand side of credit but also by the supply one.

COMPARATIVE STUDY OF ISLAMIC CREDIT CARDS TO BE ACCEPTED: MUSLIM CONSUMERS’ PERCEPTIONS

The use of standard credit cards, which incorporate the practice of riba in the case that a late payment is made, has resulted in a conflict with the perspectives of Muslim clients, which is to be addressed in choosing the best alternative available cards. The primary purpose of this research is to analyze the many existing models of Islamic credit cards to determine which one is most in line with Sharia law. The kind of study that has been done is descriptive, which employed rigorous literature studies and investigated the existing theories. The holy Quran is the source of the fundamental laws that govern what is and is not allowed in terms of credit and debt. After then, the attributes of the cards that are being presented are analyzed in light of those principles to ensure that they adhere to Sharia, which is a religious law. The Muslim community has access to a wide range of credit card options to pick from. Charge cards, Qard Al-Hasan cards, Bai Al-Inah cards, Mushraka Mutanqisa cards, Tawarruq cards, and Murabahah cards are all included in the bundles. Also had are Tawarruq cards and Murabahah cards. Critiques raised voices against Islamic credit cards. Consequently, there is a need for a payment method that may function like a credit card. However, they are still obligated to maintain complete conformity with Islamic principles. This article takes a closer look at the packages mentioned above to understand better the degree to which the various options are accepted. The study explored the crucial features of the existing Islamic credit cards and evaluated the shariah compliance of those concepts. According to the findings and conclusions of the study, the authors concluded that the 'Mushraka Mutanqisa' is the most Shara-compliant and hence most likely to be approved by Muslim customers.

Credit Card Fraud Detection using Big Data Technologies

The use of credit cards is prevalent in modern day society. But it is obvious that the number of credit card fraud cases is constantly increasing in spite of the chip cards worldwide integration and existing protection systems. This is why the problem of fraud detection is very important now. Credit card fraud detection is the most frequently occurring problem in the present world. This is due to the rise in both online transactions and e-commerce platforms. Credit card fraud generally happens when the card was stolen for any of the unauthorized purposes or even when the fraudster uses the credit card information for his use. In the present world, we are facing a lot of credit card problems. To detect the fraudulent activities the credit card fraud detection system was introduced. This project aims to focus mainly on validating various Business Rules to Identify whether the transaction happened is Fraud/Genuine and report the same accordingly.

Controlling corruption in African countries: innovation, financial inclusion and access to education as alternative measures

PurposeThis study aims to examine the impact of financial inclusion and financial innovation on corruption, considering the moderating role of education, as well as identify the specific modality of digital inclusion and payments that contribute to corruption reduction.Design/methodology/approachThe study uses a representative sample consisting of 46 African countries in three different years 2011, 2014 and 2017. On the data, feasible generalized least squares (FGLS), instrumental variables – two stages least squares (IV-2SLS) and two-stage generalized method of moments (IV-2GMM) model estimation methods were employed.FindingsThe results suggest that financial inclusion and education significantly reduce corruption. As well, the interaction between financial inclusion and education reduces corruption. Additionally, the authors find that the expansion of bank credit and the use of credit and debit cards are the specific modes of financial inclusion and digital payments that can contribute to corruption reduction.Research limitations/implicationsThis study awakens policymakers in African countries about the need to consider education as an alternative measure to support financial inclusion and reduce the use of physical cash in transactions for an effective fight against corruption.Practical implicationsRegarding practical implications, the study shows that financial inclusion besides reducing poverty for households can contribute to macroeconomic stability in Africa.Originality/valueThe study uses a representative sample composed of 46 African countries and considers the role of education in moderating the relationship between financial inclusion and financial innovation on corruption. Furthermore, the study identifies the specific modality of financial inclusion and digital payments that contribute to corruption reduction.

Implementation Graph Database Framework for Credit Card Fraud Detection

Credit card use is becoming more and more commonplace every day. Financial organizations and credit card customers lose a lot of money because of complicated illegal transactions. Fraudsters constantly stay on top of new technology to quickly perpetrate fraud against customer transaction patterns. We analyze credit card transaction networks and identify suspicious patterns, such as transactions connected to multiple accounts or unusual transaction patterns, transactions made at unusual times, and to monitor credit card transactions in real-time and quickly identify suspicious transactions. TigerGraph is used to analyze data, display results on a dashboard, and send notifications via email. One meth’\\Vc 1``13-od commonly used in anomaly detection is to compare data values against the standard deviation. In this research, we explain the use of TigerGraph as a platform for anomaly detection above the standard deviation, as well as the use of the Louvain algorithm in finding merchant communities used by fraudsters. The data used in this study comes from Sparkov simulation data obtained from Kaggle. Our results show that by using TigerGraph, we managed to achieve a very high accuracy rate of 99.77%, precision 82.84%, recall 72.38%, and f1-score 77,26% in predicting transaction fraud on Sparkov simulation data. This is much better than the results reported in a paper that uses the supervised machine learning method with the AdaBoost algorithm which achieves the highest accuracy of 77%.

Credit Card Fraud Detection Methods: A Review

In today’s context, the term “fraud” has become closely intertwined with credit card-related deceit. Recent years have witnessed a notable surge in both credit card utilization and fraudulent activities. Detecting and thwarting fraud necessitates a meticulous analysis of customers’ spending patterns. The ubiquity of credit card use for both online and in-store transactions has un-fortunately led to a parallel rise in recognition valentine scam occurrences. While the primary area of deception discovery is the documentation of sham incidents, the urgency of promptly flagging such events cannot be overstated. The con-temporary landscape heavily favors credit card usage, a trend that inadvert-ently contributes to the annual expansion of fraudulent gains. This unlawful practice exerts a pernicious influence on the global economy at large, exac-erbating its impact year after year. Numerous cutting-edge methods, including as data mining, machine learning, fuzzy logic, genetic programming, sequence alignment, artificial intelligence, and fuzzy logic, have become indispensable in the fight against this threat when it comes to identifying credit card fraud. This study delves into the intricate integration of data mining methodologies, showcas-ing their robust potential to provide comprehensive coverage against fraudu-lent activities while maintaining a controlled balance between false alarms and detection accuracy. Within the financial sector, the challenge of credit card fraud detection remains both persistent and pressing. This paper intro-duces an innovative paradigm aimed at fortifying credit card fraud detection. This is achieved by synergistically harnessing the capabilities of the Artificial Underground Over-sampling Practice (SMOTE), the potency of Adaptive Increasing (ADABoost), and the privacy-enhancing attributes of Federated Learning. The incorporation of federated learning serves a dual purpose: not only does it address prevailing data privacy concerns, but it also significantly augments the precision of fraud detection across a diverse array of geograph-ically distributed data sources

Analisis Kaidah Urf pada Fatwa DSN-MUI No. 54 tentang Syariah Card


 
 Humans as social beings cannot be separated from interaction with others, especially in fulfilling their daily needs, in economic activities, buying and selling transactions have a large enough portion to be discussed and studied, at the beginning of its development, buying and selling that occurs is still traditional, along with the times, found a medium of exchange called money, either in the form of metal or fiat money, the existence of money makes it easier for humans to make transactions. Innovation in the economic world, especially the banking world, is always dynamic, one of the emerging products is the sharia card or sharia credit card. The Indonesian Ulema Council through the National Sharia Council then provides direction through its fatwa No. 54 regarding sharia cards, in its fatwa sheet one of the arguments used is fiqh rules that discuss urf, so this study aims to discuss the relationship between the emergence of sharia card products and the urf argument that be included. Have the needs of the Islamic economic community in Indonesia reached the urf level and explain the mechanism of the sharia card contract listed on the DSN-MUI fatwa sheet No. 54. This research is a qualitative research based on library research with the main reference being turast books based on ushul fiqh and qawaid fiqhiah. After reviewing and paying attention to the concepts of urf in the two branches of science above, it was found that the needs of the community which were the main reason for the birth of this sharia card product had not yet reached the level of urf or habit, it only reached the level of مَا تَعُمُّ بِهِ الْبَلْوَى which is a condition where the use of credit cards in transactions has become common, but the results of this study do not necessarily result in this fatwa being automatically invalidated, because in its reference, DSN-MUI includes arguments and other rules that aim to strengthen sharia principles that apply to sharia cards. or Islamic credit card. This research is expected to be an example of studying fatwa sheets issued by DSN-MUI, so that it can become additional knowledge for those who want to study a fatwa.
 Keywords: Urf, Fatwa, Syariah Card

Investigating the associations of consumer financial knowledge and financial behaviors of credit card use

The growing economic uncertainty makes consumers realize the importance of financial preparedness and management ability. Based on the U.S. National Financial Capability Study dataset from 2009, 2012, 2015, and 2018, this study explores the nexus between consumer financial knowledge and financial behaviors of credit card use. The results indicate that financial knowledge positively affects consumer credit card ownership and desirable financial behaviors of credit card use, and is negatively related to undesirable credit card behaviors. The results are robust to different regression methods and after removing income outliers. The heterogeneity test shows that financial knowledge cannot enhance desirable credit card behaviors because of the income limitations in the low-income group. Therefore, there are implications for policymakers, financial sectors, and consumers to improve consumers' usage of credit cards and cultivate proper consumption habits by enhancing consumer financial knowledge.

Credit constraints in Colombia: evidence from the use of credit cards among low- and middle-income individuals

PurposeThe authors estimate the determinants of the value of purchases of semi-durable goods using permanent and transitory income, and the demographic characteristics of customers. The purpose is to identify whether individuals face remaining liquidity constraints, and how this friction affects their purchases.Design/methodology/approachThis study uses anonymized data of 516,525 credit card holders, with more than 7,501,065 records of purchases between 2010 and 2015. The authors decompose the income of individuals into permanent and transitory components to test the prevalence of the life cycle–permanent income hypothesis (LC–PIH). Determinants of the value of purchases for constrained and unconstrained consumers are estimated, considering the period in which individual characteristics are valid, the decisions not to make purchases in some months, and the potential endogeneity of the interest rate and the transitory component of income.FindingsThe authors present evidence of liquidity constraints for individuals who have used a high percentage of the credit limit on their cards. For these restricted customers, the value of purchases is inelastic to the interest rate, whereas the response is sizable for customers who are less restricted. The restricted customers increase the value of purchases when faced with increases in their credit limit. The elasticity of the value of purchases of semi-durable goods to permanent income is less than that for transitory income; regardless of the constraints, this still supports the LC–PIH.Research limitations/implicationsThis credit card is targeted at low- and middle-income individuals in Bogotá. Although the results might be considered as indicative of the behavior of those with similar characteristics in Colombia, the authors regard this work as the study of a particular case. A limitation of this work is that the authors do not have alternative sources of credit at an individual level.Practical implicationsThe broad credit channel of monetary policy does not apply to the restricted customers. This should be considered not only by the monetary authority, to understand the true extent of this policy, but also by the financial institutions that use this business model. The monetary authority should be cautious not to overreact when intervening in the money market to try to prompt an adequate consumer response.Social implicationsFinancial institutions have the policy of modifying the credit limits of their customers' credit cards which affects the well-being of restricted customers. Given that the card is aimed at low and middle income individuals, the credit limits of customers who use a high percentage of their credit limit might be increased.Originality/valueThis is the first paper to study liquidity restrictions with a retail credit card in Colombia and Latin America using information on customers' characteristics. The results are highly relevant for the implementation of monetary policy.

DETECTING CREDIT CARD FRAUD USING MACHINE LEARNING

Bank fraud is "The unauthorized use of an individual's confidential information to make purchases or withdraw funds from a user's account." E-commerce is growing rapidly, and the world is moving towards digitization, cashless transactions, the use of credit cards, the number of users is rapidly increasing, and with it the number of frauds associated with it. Due to the development of technology and the increase in the number of online transactions, fraud is also increasing, leading to huge financial losses. Therefore, effective methods to reduce losses are needed. In addition, scammers find ways to steal the user's credit card information by sending fake SMS and calls, as well as by masquerade attacks, phishing attacks, and so on. This article aims to use several machine learning algorithms such as Support Vector Machine (SVM), Decision Tree, Bayesian Belief Networks, Logistic Regression, k-Nearest Neighbor (Knn), and Artificial Neural Network (ANN) to predict the occurrence of fraud. In addition, we differentiate between the implemented supervised machine learning and deep learning methods to distinguish between fraudulent and non-fraudulent transactions.

42 Implementation of California's fertility preservation health insurance benefit mandate

California passed legislation in 2019 mandating health insurance coverage of fertility preservation (FP) for patients facing iatrogenic infertility to improve access to care and reduce medical financial hardship. The objectives of this qualitative study are to assess insurer- and clinic-level implementation practices and identify barriers and facilitators to accessing FP insurance benefits that impact service and patient outcomes. Using purposeful sampling, twenty semi-structured interviews were conducted with insurers, physicians, clinic navigators, and financial counselors from eight adult and pediatric oncology and fertility clinics in southern California. Thematic analysis guided by Bullock's (2021) framework for policy implementation and inductive codes was undertaken. At the insurer level, health benefit design standardization is limited therefore downstream training and modification of patient- and clinic-facing resources (benefit verification staff, member benefit handbooks) that reflect FP benefits are heterogeneous. At the clinic level, navigators and financial counselors addressed this heterogeneity by implementing multi-prong, time- and resource-consuming processes in interacting with these insurer resources and report that accessing benefits is complex, inconsistent, prolonged and frequently ineffective. For patient outcomes, clinic navigators report patient psychosocial distress manifested as marital problems or incurring debt by credit card use. Policies intended to improve access to care need effective downstream implementation. Heterogeneous insurer implementation of the state-level FP health insurance benefit mandate has resulted in expenditure of excessive clinic resources to assist patients in accessing urgent FP care that are not feasible, appropriate, or effective. To alleviate medical financial hardship and distress in patients facing iatrogenic infertility, insurers need to improve benefit mandate implementation to support access, and clinics need scalable, effective insurance navigation interventions.

Block Chain Technology-Based Secure E- Wallet System

According to a survey of Forex Bonuses 2017, Sweden and Canada seem to be linked with the cashless economy. An economic system where only minor amounts of currency are used in transactions is known as a cashless economy. The foundation of a cashless economy is the use of credit cards, debit cards, wallets, or other digital payment methods. Although people in India still prefer to carry cash rather than credit or debit cards, the country is transitioning to a "less cash economy" phase. Controlling the shadow economy, corruption, financing of terrorism, trafficking of people and drugs, counterfeit currency, and other issues is crucial. The cashless economy is economical, conducive to company growth and financial inclusion, etc. It is being promoted by the government via the BHIM app, AEPS, Digital, etc. Cashless economy demands strong digitalization. It has various challenges-escaping attitudes of people, poor transaction security mechanism, insufficient infrastructure etc. it is boon to industries like UBER and OLA. On secondary data, more analysis will be performed. Cashless Using BCT, India's economy is feasible and will be more secure. BCT has the ability to eliminate cash in India. both clear and safe.

A Hybrid Machine Learning Approach for Credit Card Fraud Detection

The online banking system is the new trend in the developing digital world. The transferring of a large amount of currency in a millisecond is leading to fast accessing of the banking system as it saves more time at the online payment and digital shopping. The increase in rate of use of banking credit and debit card leads to a large amount of fraud in the field of finance. Machine learning has the new discovering faces in the field of the finance. So, this research work proposed a hybrid model using the logistic regression, multilayer perceptron, and the XgBoost. The study involves both the balance and imbalance dataset to conclude the result based on the accuracy precision and recall. The results show that accuracy of the model is 100%, and precision, recall, and F1-scores are 95.63%, 99.99%, and 97.76% respectively.

Credit Card Fraud Detection Using Logistic Regression and Synthetic Minority Oversampling Technique (SMOTE) Approach.

Financial fraud is a serious threat that is expanding effects on the financial sector. The use of credit cards is growing as digitization and internet transactions advance daily. The most common issues in today's culture are credit card scams. This kind of fraud typically happens when someone uses someone else's credit card details. Credit card fraud detection uses transaction data attributes to identify credit card fraud, which can save significant financial losses and affluence the burden on the police. The detection of credit card fraud has three difficulties: uneven data, an abundance of unseen variables, and the selection of an appropriate threshold to improve the models' reliability. This study employs a modified Logistic Regression (LR) model to detect credit card fraud in order to get over the preceding difficulties. The dataset sampling strategy, variable choice, and detection methods employed all have a significant impact on the effectiveness of fraud detection in credit card transactions. The effectiveness of naive bayes, k-nearest neighbour, and logistic regression on highly skewed credit card fraud data is examined in this research. The accuracy of the logistic regression technique will be closer to 0.98%; with this accuracy, frauds may be easily detected. The fact that LR receives the highest classifier score illustrates how well LR predicts credit card theft.

Precedents of the compulsive use of a credit card: an analysis of university students' buying behavior

PurposeThis study aims to identify the precedents of compulsively using a credit card, analyzing the influence of the following factors: power–prestige, anxiety, distrust and materialism.Design/methodology/approachData collection was performed by the survey method, while the structural equation modeling technique was used for data analysis, adopting the confirmatory factor analysis and path analysis.FindingsThe impact that anxiety has on compulsive use of credit cards was confirmed. Furthermore, the influence of materialism present in an individual on the compulsive use of a credit card was also proven.Research limitations/implicationsAs a limitation, the power–prestige construction did not obtain satisfactory average variance extracted in the modeling. Other limitations can be pointed out; for instance, it was a sample composed of university students and with geographic restrictions.Practical implicationsThis study highlights the importance of promoting public policies oriented toward the conscious use of credit cards. Interference in the approach of financial institutions aimed at attracting new clients in universities is also necessary.Social implicationsThis study aggregates information about the buying behavior of university students, how the precedents affect credit card use behavior and the harmful effects of compulsive use of credit cards.Originality/valueThe originality of this study is in offering a new approach to credit cards by analyzing their usage behavior, more specifically, the compulsive use of credit cards.

The Role Website Quality, Credit Card, Sales Promotion On Online Impulse Buying Behavior

The usage of online marketplace in Indonesia increases due to Covid-19 pandemic and its supporting environment such as payment systems. This investigation was conducted to determine the effect of Website Quality on Online Impulsive Buying Behavior moderated by Sales Promotion and Credit Card Usage in Indonesian marketplace. This study uses quantitative methods with causal analysis. In this research, data was collected through online questionnaires and 275 respondents who used the marketplace website responded. This research uses PLS-SEM data analysis technique. The results of this study showed that three out of five hypotheses are accepted. This study shows that Website Quality, Credit Card Use, and Sales Promotion have positive significant effect on Online Impulse Buying Behavior. However, the result of this study also revealed interesting findings, that there is not enough evidence to support moderation effect of Credit Card use and Sales Promotion in the relationship between web quality and Online Impulse Buying Behavior.

Analysis and Detection of Fraud in Credit Card Using SILOF

Nowadays e-commerce and online transaction is growing rapidly. For online and offline transaction most of the customer uses credit card. Credit card used globally for online transaction, buy goods, product, and payment. The rising use of credit card can increase the chances of fraud in credit card. Credit card system is at risk now. The effect of this fraudulent transaction is on the bank and institute causing a financial loss to them. For the detection of distinguish frauds, several machine learning models are utilized for better prediction. The major objective of this article is to identify the fraudulent transaction and outlier in credit card transaction. The dataset of credit card is unbalanced. There are various techniques by which fraudulent transaction can be detected and we have used these techniques such as isolation forest method, local outlier factor and support vector machine to determine fraud in credit card. We have used different matrices for enhancing the performance and accuracy. At last comparison analysis is done by using isolation forest, support vector machine, and local outlier which give the better result.

Lecturers’ Perceptions on The Use Of Sharia Credit Card

The phenomenon of using credit cards for some people is considered a symbol of status and lifestyle. It has an impact on conventional and Islamic financial institutions to compete to create banking card products that function to attract the attention of potential customers. In a hadith review, the credit card phenomenon is seen as a form of muamalah based on the principle of debt. Therefore, in a hadith review, borrowing is a commendable practice because it includes helping each other. Therefore, the study aims to analyze the perception of lecturers on the use of sharia credit card. The sample of research is people who have a teaching profession at an Islamic University. The methodology is qualitative with a quantitative descriptive approach, where the focus is to explore the problems of the research subject specifically from all aspects of personality related to the object of research. The research is also often referred to as casuistic observation research, then the data collection is the distribution of online questionnaires and literature studies. Then the data is analyzed by an interactive model. The results show that what makes lecturers less interested is that they think conventional and sharia credit cards are the same. Furthermore, the lack of socialization of sharia banks in promoting and explaining in detail about sharia credit cards. Someone who has a teaching profession has the perception that using a sharia credit card will give them financial risk if one day they fail to pay bills. The services and facilities provided by sharia banks have not been maximized when compared to conventional ones.

Credit Card Anti-Fraud Prediction Based on Ensemble earning

In recent years, due to the development of Internet technology in the financial industry, cardless and cashless payments have become increasingly popular. At present, people only need to bind their cards to their cell phones to scan the code for payment. In the meanwhile, as the use of credit cards is vigorously promoted nationwide, younger generations relied more on the credit card debt for consumption. However, although these developments have brought a lot of convenience to people's travel and their life, the virtual nature of the Internet and the "Enjoy First, Pay Later" feature of credit cards have led to a significant increase in potential credit risk in the entire transaction, triggering more and more new credit card fraud incidents. While the overall financial environment in China is relatively healthy, the fact that fraudulent behaviors such as credit card overdrafts, counterfeit cards, and credit card frauds are all personal conduct, making it difficult to detect and prevent these individuals from acting illegally in a timely manner. Although fraud represents a small percentage of the overall transaction size, its bad debt losses to merchants and banks can be significant. In order to make a timely prediction of fraud, there are already many single-model machine learning methods such as decision trees and logistic regression. However, the generalization ability of these models is not good enough in the face of complex user behavior features. In addition, since the probability of fraudulent behavior is very small, resulting in few samples that can be trained, even if a model has a high accuracy rate, there is no guarantee that it can accurately predict fraudulent behavior. Therefore, this paper proposes the Adaboost method based on ensemble learning and uses SMOTE oversampling based on a few classes of fraudulent samples of user features to solve the above existing problems.

Detection of Credit Card Fraud Transactions using Machine Learning based Algorithm

The rapid growth in E-Commerce industry has lead to an exponential increase in the use of credit cards for online purchases and consequently they has been surge in the fraud related to it .In recent years, For banks has become very difficult for detecting the fraud in credit card system. Machine learning plays a vital role for detecting the credit card fraud in the transactions. For predicting these transactions banks make use of various machine learning methodologies, past data has been collected and new features are been used for enhancing the predictive power. The performance of fraud detecting in credit card transactions is greatly affected by the sampling approach on data-set, selection of variables and detection techniques used. This paper investigates the performance of SVM, decision tree and random forest for credit card fraud detection. Dataset of credit card transactions is collected from kaggle and it contains a total of 2,84,808 credit card transactions of a European bank data set. It considers fraud transactions as the “positive class” and genuine ones as the “negative class” .The data set is highly imbalanced, it has about 0.172% of fraud transactions and the rest are genuine transactions. The author has been done oversampling to balance the data set, which resulted in 60% of fraud transactions and 40% genuine ones. The three techniques are applied for the dataset and work is implemented in R language. The performance of the techniques is evaluated for different variables based on sensitivity, specificity, accuracy and error rate. The result shows of accuracy for SVM, Decision tree and random forest classifier are 90.0, 94.3, 95.5 respectively. The comparative results show that the Random forest performs better than the SVM and decision tree techniques.