Payment Card Research Articles

Legal Problems of Compliance with Bank Secret when Performing Transactions Using Payment Cards

Payment (bank) cards are among the most actively spreading phenomena in the modern world order. However, the problems of preserving information that constitutes banking secrecy in the context of digitalization and the active spread of online payments are of particular importance. Banks are also interested in preventing the disclosure of information constituting a banking secret. However, due to the uncertainty of the current legislation, which establishes the range of information related to bank secrecy, a number of problems arise that need to be analyzed. In the presented article, the author attempts to determine whether the payment card details relate to information constituting a bank secret. Examples from judicial practice in cases arising from disputes between banks and their individual clients regarding the legality of transactions performed by the bank using payment cards are given. It is concluded that the details of the payment cards themselves do not relate to the information constituting a bank secret. They can be considered as a structural element of a banking transaction performed using a payment card.

Bus Network Adjustment Pre-Evaluation Based on Biometric Recognition and Travel Spatio-Temporal Deduction

A critical component of bus network adjustment is the accurate prediction of potential risks, such as the likelihood of complaints from passengers. Traditional simulation methods, however, face limitations in identifying passengers and understanding how their travel patterns may change. To address this issue, a pre-evaluation method has been developed, leveraging the spatial distribution of bus networks and the spatio-temporal behavior of passengers. The method includes stage of travel demand analysis, accessible path set calculation, passenger assignment, and evaluation of key indicators. First, we explore the actual passengers’ origin and destination (OD) stop from bus card (or passenger Code) payment data and biometric recognition data, with the OD as one of the main input parameters. Second, a digital bus network model is constructed to represent the logical and spatial relationships between routes and stops. Upon inputting bus line adjustment parameters, these relationships allow for the precise and automatic identification of the affected areas, as well as the calculation of accessible paths of each OD pair. Third, the factors influencing passengers’ path selection are analyzed, and a predictive model is built to estimate post-adjustment path choices. A genetic algorithm is employed to optimize the model’s weights. Finally, various metrics, such as changes in travel routes and ride times, are analyzed by integrating passenger profiles. The proposed method was tested on the case of the Guangzhou 543 route adjustment. Results show that the accuracy of the number of predicted trips after adjustment is 89.6%, and the predicted flow of each associated bus line is also consistent with the actual situation. The main reason for the error is that the path selection has a certain level of irrationality, which stems from the fact that the proportion of passengers who choose the minimum cost path for direct travel is about 65%, while the proportion of one-transfer passengers is only about 50%. Overall, the proposed algorithm can quantitatively analyze the impact of rigid travel groups, occasional travel groups, elderly groups, and other groups that are prone to making complaints in response to bus line adjustment.

Can carbon labels shift consumers towards sustainable food? Evidence from Chinese consumers

Carbon labels are becoming an essential tool for policymakers in many countries to promote low-carbon consumption. To assess customers’ willingness to pay for five carbon-labeled agricultural products (CAP), we used payment card to conduct a questionnaire survey among 641 respondents in Shanghai, Nanjing, Wuhan, and Xi'an, all located in China. This study quantitatively analyzes the influencing factors and interactive mechanisms of the public's willingness to purchase CAP through the extended Theory of Planned Behavior and Norm Activation Model. The results show that consumers' willingness to pay a premium for carbon-labeled milk, corn, bananas, tomatoes, and eggs is 27.50%, 29.73%, 26.86%, 26.51%, and 24.26% respectively. Perceived behavioral control has the strongest positive influence on purchase intention, followed by subjective norms, attitudes toward the behavior, and personal norms. There is a significant mediating effect between awareness of consequences and personal norms, which indirectly influences personal norms through the ascription of responsibility and subsequently affects purchase intention. Additionally, there is a gap between purchase intention and behavior, and risk perception negatively moderates the relationship between the two. Based on the research findings of this paper, practical and effective policy suggestions are proposed for the government to promote carbon labeling policies and reduce carbon emissions.

Financial Technology Adoption: Network Externalities of Cashless Payments in Mexico

Do coordination failures constrain financial technology adoption? Exploiting the Mexican government’s rollout of 1 million debit cards to poor households from 2009 to 2012, I examine responses on both sides of the market and find important spillovers and distributional impacts. On the supply side, small retail firms adopted point-of-sale terminals to accept card payments. On the demand side, this led to a 21 percent increase in other consumers’ card adoption. The supply-side technology adoption response had positive effects on both richer consumers and small retail firms: richer consumers shifted 13 percent of their supermarket consumption to small retailers, whose sales and profits increased. (JEL E42, L25, L81, O14, O33)

Debt trap dynamics: The moderating role of convenience, financial literacy, and religiosity in credit card usage

In the modern age, the card payment business has flourished in Pakistan. Many multinational and national commercial banks have not only introduced debit cards but also many types of credit cards with distinct payment plans, and they are broadly used as financial instruments in consumer financing. The purpose of this research is to identify the distinctive components that cause credit card debt and how Pakistanis are snared into a debt trap by using credit cards. For this purpose, the study examines the effects of credit card usage (CCU), materialism (M), installment plan (IP), convenience (CONV), religiosity (R), and financial literacy (FL) on a dependent variable, debt trap (DT). Furthermore, the moderation impacts of R, FL, and CONV are tested too. The quantitative research approach and cross-sectional research design were applied. The data was collected from 297 credit card users by using a convenience sampling technique that shows a 79.2 % response rate. The Partial Least Square (PLS)-Structural Equation Modeling (SEM) technique was employed for data analysis. The Partial Least Square (PLS)-Structural Equation Modeling (SEM) direct effect has shown that CCU, M, IP, CONV, and R have a significant relationship with DT while FL has an insignificant relationship with the DP. In other words, the indirect effect has shown that FL is not significantly moderate in the relationship between R and DT. Likely, CONV does not significantly moderate the relationship between CCU and DP, however; other indirect effects show a significant moderated relationship of all other independent and dependent variables. Most of the moderating effects are supported, therefore the moderating effects are significant contributions. The study findings could help banks and regulatory bodies in launching these types of credit cards which are under shariah complaints about captivating the users to use the credit card without any fear. This research is a pioneer study in the context of the Pakistan banking sector along with three moderators, religiosity, financial literacy, and convenience. Based on the findings of this research, policymakers and financial institutions in Pakistan should consider introducing Shariah-compliant credit cards that align with users' religious beliefs, while promoting financial literacy programs to better educate consumers on responsible credit card usage and debt management, thus preventing them from falling into debt traps.

Comparative Analysis of Digital Wallet Usage of Millennials and Generation Z

Today, the development of the digital world is so rapid, that humans have found many new tools and innovations to make daily activities easier. One of them is in buying and selling transactions. When you want to make a payment, you have a digital wallet, commonly called an electronic wallet (e-wallet). This research aims to analyze comparative analysis of digital wallet usage of millennials and generation Z. This type of research uses descriptive. Data collection in this study uses secondary data. Data analysis in this study uses comparative analysis. Research results show that digital wallet usage is an electronic service used to store payment instrument data. The instrument can be a payment card or electronic money, which allows users to make payment transactions. The advantage of using a digital wallet is convenience with digital wallet usage, someone can make transactions easier anywhere and anytime. Digital wallet usage means there is no need to leave the house to make payments, such as buying food, paying bills, to making investments, all of which can be done using a digital wallet. Comparative analysis shows that there are differences between millennials and generation Z regarding the use of digital wallets.

Causal effects of information friction on willingness to pay for hurricane-resistant buildings

We investigate the causal effects of information provision on willingness to pay (WTP) for precautionary building standards. In a randomized stated preference field experiment, we present video evidence that hurricane-resistant building standards outperform traditional building standards to a random sample of homeowners. Our analysis reveals evidence consistent with causal effects of video information on WTP for hurricane-resistant buildings. Overall, alleviating performance information friction increases WTP by $634 on average and three times more among risk-neutral homeowners if the WTP payment card range is not truncated.

Return management in e-commerce firms: A machine learning approach to predict product returns and examine variables influencing returns

The advent of technology with internet penetration has led to exponential growth in sales through online mediums worldwide. Although the growth has increased sales and created opportunities for many firms and sellers to flourish, it also brings in the challenge of customer returns, making it a formidable challenge for businesses. For effective return handling and control, predicting product returns correctly is of utmost importance for firms. The study comprehensively examines the return management process, elucidating its multiple features and characteristics, focusing on the types of returns and the involvement of various stakeholders. Logistic regression, random forest, and gradient-boosting machine learning techniques were used on a data set to scrutinize critical variables influencing returns. Comparing the models using the ROC curve and AUC, gradient boosting performed better in predicting returns than other models. The study identifies significant variables affecting e-commerce return rates, such as total order amount, product category, and cash on delivery charge, an additional fee charged on this mode of payment. Managerial insights recommend that firms conduct due diligence and thorough assessments before introducing products from specific vendors. Quality improvement in certain categories of products, incentivizing card payments, and nominalizing the cash on delivery fees, which could be seen as burdensome by customers, are some of the steps firms need to reduce returns. Strategies like confirming weekend orders and updating replenishment times regularly by vendors can help firms mitigate returns effectively. The study contributes to the academic understanding of return management in e-commerce and offers actionable insights for practitioners.

Using Sensory Analysis and Stated Preference Valuation to Assess the Willingness to Pay for Fresh Tomato Attributes

ABSTRACTThis study combines sensory analysis and stated preference valuation techniques to assess the monetary value of organoleptic attributes of new tomato varieties. The tomatoes evaluated included two new varieties adapted to South Texas growing conditions, a commercial check, and tomatoes imported from Mexico. In a blind sensory test, participants sampled, rated, and valued the attributes of all tomato varieties. Willingness to pay responses were collected using a payment card elicitation approach and jointly analyzed with a multivariate ordered probit model. Price premiums were associated with males, smaller households, younger consumers, higher income, and the liking of different organoleptic attributes.

Artificial intelligence caters to banks and customer needs

Banking customers do not think, speak, text, or communicate as financial institutions do. For humans, banking is as simple as “check balance,” “transfer,” “credit card payment,” and we prefer to talk in that language. When users contact a customer service representative, they want accurate answers without spending too much time over the phone or email. However, during these “new normal” times, the waiting time to reach a customer service rep has tremendously increased. Human agents cannot scale up with the ever-increasing demand to resolve customers’ queries. More often than not, agents feel burned out as they do not have enough tools at their disposal for delivering fast response time. With conversational artificial intelligence (AI) kicking in, the technology uses machine learning (ML) and natural language processing (NLP) to create a better customer experience. With the right set of programs and rules written, AI/ML tools can do much more than chatting and providing basic information to customers. For example, you could also update your contact and residential address, check your credit card balance, activate your card, etc. Chatbots are no longer “bots” just with pre defined answers to limited questions fed into them, with conversational AI and NLP- they are at par with human like responses and answer as accurately as a human would do. The businesses which are time zone independent find them more helpful.

Identification and Analysis of Credit Card Fraud Based on Machine Learning Methods

Credit card has become an indispensable payment tool in People's Daily life. However, credit card fraud has also increased, resulting in huge financial losses for banks and individuals. Based on the real world credit card transaction data, this paper uses a variety of machine learning algorithms to identify and analyze credit card fraud. Firstly, the problem of missing and unbalanced data is solved through data preprocessing and feature selection, and important features are screened out, Such as transaction amount, product code, payment card type, etc. Then, integrated logistic regression, random forest, LightGBM, XGBoost, and Stacking models were constructed, and the models were trained and evaluated through cross-validation and parameter optimization. The experimental results show that the integrated Stacking model performs best in credit card fraud detection, with an AUC(Area Under the Curve) value of 0.960, which can accurately identify fraudulent transactions and provide an effective fraud warning mechanism for financial institutions. This mechanism significantly benefits both financial institutions and credit card users by reducing fraud losses and enhancing transaction security. However, it is acknowledged that the study has limitations, including the potential impact of data imbalance on model performance, the need for further testing of model generalization ability to new fraud patterns, and the importance of optimizing real-time performance to ensure practical applicability.

Investigating Credit Card Payment Fraud with Detection Methods Using Advanced Machine Learning

In the cybersecurity industry, where legitimate transactions far outnumber fraudulent ones, detecting fraud is of paramount significance. In order to evaluate the accuracy of detecting fraudulent transactions in imbalanced real datasets, this study compares the efficacy of two approaches, random under-sampling and oversampling, using the synthetic minority over-sampling technique (SMOTE). Random under-sampling aims for fairness by excluding examples from the majority class, but this compromises precision in favor of recall. To strike a balance and ensure statistical significance, SMOTE was used instead to produce artificial examples of the minority class. Based on the data obtained, it is clear that random under-sampling achieves high recall (92.86%) at the expense of low precision, whereas SMOTE achieves a higher accuracy (86.75%) and a more even F1 score (73.47%) at the expense of a slightly lower recall. As true fraudulent transactions require at least two methods for verification, we investigated different machine learning methods and made suitable balances between accuracy, F1 score, and recall. Our comparison sheds light on the subtleties and ramifications of each approach, allowing professionals in the field of cybersecurity to better choose the approach that best meets the needs of their own firm. This research highlights the need to resolve class imbalances for effective fraud detection in cybersecurity, as well as the need for constant monitoring and the investigation of new approaches to increase applicability.

Riding the wave of change: Buy now, pay later as a disruptive threat to payment cards in the global market

This paper studies changes in the worldwide payment system by examining the growing popularity of buy now, pay later (BNPL). Considering variables related to individual financial personality and socio-demographic traits, we provide an original dataset to investigate whether BNPL could be a valid substitute for the use of payment cards. The results show that the payment card sector should take BNPL seriously as a competitive challenge. The developments of the COVID-19 pandemic suggest a possible change from the usage of payment cards towards BNPL, similar to what happened with cash transactions. This research contributes to the literature on the topic by proposing fresh ideas for payment methods and the financial sector, which continue to evolve in response to consumer needs.

Determinants of parental demand of human papillomavirus vaccination for adolescent daughters in China: Contingent valuation survey.

Several types of human papillomavirus (HPV) vaccines have been approved for use in adolescent girls in China. These vaccines are regulated as non-National Immunisation Program vaccines and are optional and generally fully self-paid by vaccinees. To assess parents' demand for HPV vaccination by eliciting their willingness-to-pay for their adolescent daughters to be vaccinated against HPV and to examine the determinants of demand for HPV vaccination in China. A contingent valuation survey was conducted across three cities in Shandong Province in eastern China. We selected 11 junior middle schools with different socioeconomic features and randomly selected 6 classes in each school, and questionnaires were distributed to all girls aged 12-16 in the 66 classes for their parents to complete. A payment card approach was used to elicit parental willingness-to-pay for HPV vaccination for their daughters. We also collected a wide array of socioeconomic and psychological variables and interval regressions were applied to examine the determinants of parental willingness-to-pay. A total of 1074 eligible parents who completed valid questions were included in analyses. Over 85% of parents believed HPV vaccines were, in general, necessary and beneficiary. However, only around 10% believed that their daughters would be infected by HPV. About 8% of parents would not accept HPV vaccine even if the vaccine were free mainly due to concerns about the potential side effects and vaccine safety and quality issues, and 27.37% would only accept the vaccine if it were free. The median willingness-to-pay was 300 CNY (42 USD). Several factors were positively correlated with higher willingness-to-pay: income, urban residence (relative to rural residence), mothers (relative to fathers), parents' beliefs about vaccine benefits, whether they should make decisions for their daughters, and whether their daughters would be susceptible to HPV. Though education-level was not significantly correlated with willingness-to-pay in the main regressions, a subgroup analysis revealed interesting dynamics in the relation between education and willingness-to-pay across different income-levels. There is a large gap between parents' willingness-to-pay and the market price of HPV vaccine for girls in China. Parents generally believed the HPV vaccines were beneficial and necessary but when asked for their daughters, most parents did not believe their daughters would be infected by HPV despite the high prevalence in China. Future focus should be on ensuring the provision of accurate health information about HPV prevalence, vaccine quality, and safety to promote vaccine uptake, and promotional efforts tailored to different income groups might yield better effects. Government involvement in negotiating more widely acceptable and affordable prices or subsidising may be necessary for protecting high-risk population groups.

Nowcasting Norwegian household consumption with debit card transaction data

SummaryWe use a novel data set covering all domestic debit card transactions in physical terminals by Norwegian households, to nowcast quarterly Norwegian household consumption. These card payments data are not subject to revisions and are available weekly without delays, providing a valuable early indicator of household spending. To account for mixed‐frequency data, we estimate various quantile mixed‐data sampling (QMIDAS) regressions using predictors sampled at monthly and weekly frequency. We evaluate both point and density forecasting performance over the sample 2011Q4–2019Q4. Our results show that MIDAS regressions with debit card transactions data improve both point and density forecast accuracy over competitive standard benchmark models that use alternative high‐frequency predictors. Finally, we illustrate the benefits of using the card payments data by obtaining a timely and relatively accurate nowcast of 2020Q1, a quarter characterized by heightened uncertainty due to the COVID‐19 pandemic. We further show how debit card data have been useful in nowcasting consumption during the four subsequent quarters.

Seniors constraints to travel: analysing discussions on TripAdvisor

ABSTRACT The objective of this study is to employ text mining techniques to examine discussions concerning senior travel topics in the TripAdvisor forums. Through this meticulous examination of discussions, we aim to unravel not only the surface-level themes but also the underlying motivations and concerns that drive seniors to actively participate in travel forums. The prevalent topics include essential travel insurance details, with a particular emphasis on coverage related to the disease of COVID-19. Other recurring subjects involve travel/tour agencies, considerations for cash or card payments, and discussions on customer service, especially in the context of insurance and travel companies/agencies. Results highlight that forum discussions align with prior studies on seniors, identifying three primary constraints: health, safety/security, and specific recommendations. Senior travellers predominantly seek advice on travel companies, insurance providers, services, and destinations.

Cash holding by consumers: The role of the perceived characteristics of money

AbstractThis study examined why consumers who typically pay electronically at points of sale still carry cash and explained the inertia of demand for cash by considering the additional (perceived by consumers) characteristics of cash. The research was motivated by the growing discussion on why cash, despite the increasing importance of e‐payments, remains a significant part of the demand for money. The study focused on psychological factors and consumers' perceptions of certain features of cash, which make it an attractive additional option for precautionary reasons, and therefore, explored beyond the typical discussion of money as a means of payment. The structural equation modeling based on data collected as part of a survey of Polish consumers was used to verify the hypotheses. It was revealed that cash becomes a reserve solution for consumers, which is kept “just in case” to expand the scope of personal freedom and flexibility in making payments and protect against various potential restrictions and threats related to e‐payments. Consumers' holding of cash as an additional option depends on three key factors: attitude toward cash, particularly the flexibility it offers, unusual purchasing situations, and perceived card or mobile payment risk, considered as a moderator. In the debate on the future of cash, the study supports the position that cash has a place in the modern payment system, although it plays a different role. Cash is a supportive backup solution. Eliminating cash significantly reduces consumer welfare.

Financial accessibility in Southern Russia: A study of digitalization and regional differences

Subject. The article addresses the effectiveness of digital innovations in financial inclusion in the Southern Federal District of Russia. Objectives. The aim is to assess the impact of digitalization on the process of financial inclusion in Southern Russia. Methods. The study rests on the analysis of secondary data obtained from the Bank of Russia and regional financial institutions. These included quantitative data on the spread of banking networks, financial inclusion indices, and the adoption of digital financial services in Southern Russia. Furthermore, I conducted a survey of existing literature on financial inclusion and digitalization to provide context and theoretical background. I also applied descriptive statistical methods to identify and analyze patterns and trends in the data, and comparative analysis to assess regional differences in financial inclusion and digitalization in Southern Russia. Results. The study revealed significant regional differences in the availability of digital financial services. The survey found a growing trend towards the adoption of digital payment methods, with a marked increase in the use of payment cards and mobile banking services. However, the pace of digitalization was uneven across regions, influenced by factors such as demographic structure, economic development, and cultural practices. Conclusions. A customized approach that takes into account the unique socio-economic characteristics of each region is critical to bridging the digital divide in financial services. This study contributes to understanding the complex relationship between digitalization and financial inclusion in a region with diverse socioeconomic conditions, provides insight for policymakers and financial institutions on how to design strategies for inclusive and sustainable financial development.

Perceived Impacts of the Expanded Child Tax Credit Cessation Based on Family Income Level.

The 2021 Expanded Child Tax Credit (ECTC) lifted millions of children out of poverty and drastically improved well-being. These impacts were particularly salient for families with lower income among those who received the full ECTC benefit. This study gathered lived experiences on the ECTC cessation and explored differential impacts across income levels to inform discussions around policy restoration. Semi-structured interviews were conducted with parents who had a child (2-12 years) who received the full ECTC. Interviews occurred in May 2022 after the ECTC ended. Changes in financial security and health were assessed. Families were classified as having lower vs. higher income (LI; n=19 vs. HI; n=19) corresponding to household income below vs. above 200% of the Federal Poverty Line. Inductive analysis and constant-comparison methods generated themes on similarities/differences between groups. Results indicated families with LI experienced severe financial constraints and greater negative emotions, after the ECTC ended. Many reduced spending, budgeted, accepted financial support from family/friends, and delayed credit card payments. More families with HI reported the ECTC provided a financial buffer placingthem in a more secure position to meet current needs. Both groups reported negative impacts from inflation coinciding with the ECTC ending and minimal changes in their income tax return. Families overwhelmingly reported a desire for the ECTC to continue, despite experiencing different degrees of impact due to these financial changes. Families with LI faced greater hardships after the ECTC ended. Differences across income highlight the need for ECTC restoration, particularly for families in severely under-resourced circumstances.

Assessment of the Electronic Smart Card Payment System and the Security and Safety of Public Transport Users in Lagos

Assessment of the Electronic Smart Card Payment System and the Security and Safety of Public Transport Users in Lagos