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Under the background of low consumer financial knowledge and accumulated credit card liabilities, this study investigates the relationship between financial knowledge and responsible credit card behavior using data from the 2019 China Household Finance Survey (CHFS). From the perspective of consumer economic well-being, this study defines accruing credit card debt to buy houses and cars when loans with lower interest rates are available as irresponsible credit card behavior.

This study uses probit regressions to examine the association between financial knowledge and responsible credit card behavior because the dependent variable is a dummy variable. To alleviate endogeneity problems, this study uses instrument variables and Heckman’s two-step estimation. Furthermore, to explore the potential mediators in this process, this study follows the stepwise regression method. Finally, this study introduces interaction terms to examine whether this association differs in different groups.

The results indicate that financial knowledge is conducive to increasing the probability of responsible credit card behavior. Mediating analyses reveal that the roles of financial knowledge occur by increasing the degree of concern for financial and economic information and the propensity to plan. Moderating analyses show that the effects of financial knowledge on responsible credit card behavior are stronger among risk-averse consumers and in regions with favorable digital access.

This study measures responsible credit card behavior from the perspective of the consumer’s well-being, which enriches practical implications for consumer finance. Furthermore, this study explores the potential mediators influencing the process of financial knowledge that affects responsible credit card behavior and identifies moderators to conduct heterogeneous analyses, which helps comprehensively understand the nexus between financial knowledge and credit card behavior. By achieving these contributions, this study helps to curb the adverse effects of irresponsible credit card behavior on consumers’ well-being and the economic system and helps policymakers promote financial knowledge to fully prevent irresponsible credit card behavior.

Every year, millions of consumers around the world become victims of credit card fraud. These individuals have to appeal to their credit card companies to reverse unauthorized charges. This study aims to profile the American consumers’ experience when complaints to their credit card companies about unauthorized charges fail to produce a resolution. Using a large database of consumer complaint filings with the Consumer Financial Protection Bureau (CFPB), the characteristics of these consumer complaints are identified, and the drivers of consumer financial hardship resulting from credit card fraud are determined.

A random sample of consumer complaints about their credit card companies’ perceived mishandling of cases, filed with the CFPB, is used to conduct content analysis. The resulting content analysis categories are used in a predictive model to determine the drivers of consumer hardship.

In nearly one-quarter of all complaint filings, the credit card company had blamed the complainant as the party responsible for the fraudulent charges or refused to open a fraud investigation altogether. Nearly 60% of complaint reports contain expressions of emotional distress and many mention financial hardship. Nearly half of all complainants consider the fraud department operations of their credit card company as lacking in service quality, many reporting inability to reach the department or to receive a returned call. Even after CFPB intermediation, only 15% of complainants receive some form of financial relief from their credit card company. The majority of the complainants report a lack of willingness by the credit card company to reverse unauathorized charges, leaving the complainant financially responsible for them.

This study focused on data collected from consumers. Future research can expand the scope of inquiry by surveying the staff and executives in the fraud investigation departments of credit card companies to determine the norms of fraud investigation used within the industry.

This study sheds light on the financial hardship and emotional pains that consumers victimized by credit card fraud experience in dealing with their credit card companies.

To the best of the authors’ knowledge, this is the first study to empirically examine American consumers’ complaints about the fraud investigation operations of their credit card companies. Using data captured through the complaint filing system of a federal bureau (CFPB), the findings have implications for policymakers, regulators and credit card companies.

This paper aims to analyze decisions about payment rates on credit card statements by using background factors and perceptions that indirectly influence beliefs, according to the theory of planned behavior.

Since legal and institutional frameworks and household financial surveys are heterogeneous among countries, household data on the Chilean economy is used as the starting point in this matter.

The probability that an individual chooses to pay amounts less than the total billing of their credit cards rises with essential variables related to perceived behavioral control. Being the head of the household, being younger, perceiving a high or excessive financial burden of debt and facing unfavorable and unexpected situations that divert the budget, among others, are relevant to repayment decisions.

The novelty of this article is that its psychological approach differs from the traditional focus of economic rationality regarding credit cards. The results are relevant for policymakers and financial regulators due to implications for household behavioral finance and means of payment.

Analizamos la decisión de la tasa de pago de los estados de cuenta de tarjetas de crédito a través del uso de factores de fondo y percepciones que indirectamente inciden en las creencias de acuerdo a la teoría del comportamiento planeado.

Debido a que los marcos legales e institucionales, así como también las encuestas financieras de hogares son heterogéneas entre países, se utilizan datos de los hogares de la economía chilena como un punto de partida en esta materia.

La probabilidad de que un individuo elija pagar un monto menor que el total de facturación de sus tarjetas de crédito es afectada por variables proxy asociadas al control conductual percibido. La condición de ser jefe de hogar, ser más joven, la percepción de una alta o excesiva carga financiera de la deuda, y enfrentar situaciones desfavorables e inesperadas que desvían del presupuesto, entre otras, son relevantes para las decisiones de pago.

La novedad de este artículo es que su enfoque difiere del enfoque tradicional de la racionalidad económica en relación a las tarjetas de crédito. Los resultados son relevantes para los hacedores de política y reguladores financieros debido a sus implicancias para las finanzas conductuales de los hogares y sus medios de pago.

After the effective date of International Financial Reporting Standard (IFRS) 15, the accounting treatment of credit card rewards programmes (CCRPs) is no longer explicitly prescribed. Uncertainty regarding what constitutes faithful representation, and the inconsistent accounting practices observed, has created a need for guidance on the appropriate accounting treatment of CCRP transactions. Accounting theory has the potential to provide the foundation for this guidance. As a result, the objective of this study was to develop a theoretical model for the accounting treatment of CCRP transactions using accounting theory.

This non-empirical qualitative conceptual study utilised document analysis, focussing specifically on accounting theory, to construct an accounting treatment model.

Applying the relevant accounting theory (International Accounting Standards Board's (IASB's) Conceptual Framework), a theoretical model for the accounting treatment of CCRP transactions was developed, which emphasises the importance of understanding the economic phenomenon (the CCRP transaction) and determining how management views the transaction (in isolation as marketing or as an integral part of the credit card transaction).

Addressing the problem of accounting for CCRP transactions with reference to accounting theory (which is the main element of scholarly activity in accounting) distinguishes this study from previous research on the topic. The CCRP accounting treatment theoretical model could assist CCRP management in faithfully accounting for a CCRP transaction and reduce uncertainty and inconsistency in practice. Moreover, this study identified the procedures to be employed when using accounting theory to determine the appropriate accounting treatment of business transactions. These procedures could be employed by accountants when faced with other transactions not covered by specific accounting standards.

This case teaches students the importance of maintaining a strong FICO score by illustrating the consequences of paying bills late or not at all. The protagonist is David Molina, a waiter at a struggling Italian restaurant located down the block from where he lives. Money is tight for Molina right now—his limited income means he lives paycheck to paycheck. However, Molina knows things will be looking up for him soon because he recently accepted a job as a bank teller across town—his first desk job.

Molina has been putting off paying two of his bills: a cable bill and his Bank of America credit card bill, both of which are late and have been issued, this time, in the form of threats to impact Molina's credit score if he doesn't pay them. He has just enough money to afford the minimum payments on each overdue bill. But then he receives a phone call from his friend, Jim Lindsey, reminding him about an invitation to go to Myrtle Beach for the upcoming weekend. Molina knows he cannot afford it, but a woman he's attracted to, Jessica, will be there too. Should Molina put off the bills yet again, and if so, how exactly will being late on them hurt his credit score?

With the development of inclusive financial business in China in recent years, this case describes the credit risk control of “mobile credit”, a smart online credit platform launched by Shanghai Mobanker Co. Ltd. (referred to as “Mobanker”, previously named as “Shanghai Mobanker Financial Information Service Co., Ltd.”) which provides technical services for inclusive finance industry.

This paper aims to measure the extent of digital financial inclusion (DFI) and examine the effect of socioeconomic characteristics on using government remittances and the adoption of digital financial services (DFS) during the COVID-19 pandemic.

The World Bank Global Financial Inclusion (Global Findex) database 2021 is used in this study, with a sample size of 3,000 Indian individuals. The study measured the demand-side analysis of DFI, namely, accessibility and usage of DFS with selected socioeconomic characteristics such as gender, age, income, education, being in the workforce and residential status of respondents. The dependent variable is binary in nature; therefore, the logistic regression model is used for the data analysis.

The results of the study reveal that individuals’ socioeconomic factors, such as female, all the age groups, tertiary education, third- and fourth-income quintile and workforce, are found to have a significant association with “accessibility,” an exogenous variable of DFS. Besides, respondents’ socioeconomic attributes, namely, female, tertiary education, income for all quintiles and workforce, are more likely to use DFSs in the COVID-19 pandemic. The study also finds the residential status of individuals is influencing the accessibility and usage of DFS.

The findings of the study provide valuable insights to the service providers and policymakers regarding the rapid expansion of DFS by digital infrastructure, simplifying the banking procedures and highlighting the importance of digital financial literacy to accomplish government goals through serving the unbanked population and also design strategies for achieving the objectives of Digital India: “Faceless, Paperless, and Cashless” of DFI across the country.

Notable studies used World Bank Findex survey data to explore the determinants of financial inclusion in general. This research is one among the few studies to explore the determinants of India’s DFI. Moreover, this study measured the effect of individual socioeconomic attributes on the adoption of DFSs during the COVID-19 pandemic, which has not been included in prior studies. Therefore, this study has added value to the existing literature on financial technology innovation and DFS for the sustainable development of emerging nations.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

This study explores the psychological determinants of buy-now-pay-later (BNPL) use in the UK and reviews the efficacy of existing payment constructs.

A total of 533 BNPL users engaged in story stem completion. Template analysis was used, supported by the identification of four BNPL sentiment groups to enable comparison.

Whilst positive attitudes towards BNPL dominate, other psychological determinants are apparent to a varied extent. Psychological distance and ownership of borrowed money are redolent, while transparency and transaction convenience are less appreciable. BNPL users understand temporality beyond its current conceptualizations. Some users construe BNPL as a “savings” product, and hence payment format conceptualizations may be erroneous. Those with a positive sentiment foreground BNPL’s consumption and budget management benefits. However, the potential for unintended consequences is manifest across all users.

The potentially unwanted consequences, or dark side, of BNPL use in the UK are highlighted. The specified constructs, whilst helpful, do not particularize the complex interconnected nature of the psychological determinants of BNPL use. Improved conceptualization offering richness and clarity is needed – temporality specifically requires consideration.

Users’ sophistication and misunderstanding are both evident, necessitating fuller conversations among various stakeholders, including, providers, policymakers, consumers and advocacy groups.

This research advances the scarce literature exploring consumers’ BNPL use determinants and challenges current conceptualizations surrounding payment format perceptions.

Buy-now, pay-later (BNPL) services can put consumers into a debt trap by encouraging consumers to buy things they cannot afford, leading to a culture of materialism and consumerism. Therefore, this research aims to investigate how materialism can influence BNPL use and impulsive and compulsive buying. Additionally, the authors examine if BNPL use and impulsive buying mediate between materialism and compulsive buying.

Data from 556 participants were collected through a structured questionnaire via an online survey. Structural equation modeling (SEM) using SMART PLS 4 was employed to analyze the relationship between variables and to test the proposed hypotheses.

Materialism impact BNPL use and increases the inclination for impulse buying, precipitating compulsive buying behavior. However, BNPL use does not directly influence compulsive buying. The mediating relationship was identified, where BNPL use, and impulsive buying mediate the relationship between materialism and compulsive buying.

BNPL use alone does not inevitably lead to compulsive buying. The only way BNPL use could lead to compulsive buying is through impulsive buying. Therefore, BNPL service providers need to foster responsible buying habits due to the rise in impulsive buying, which, if not controlled, could lead to a debt trap resulting from compulsive buying.

This study contributes to the limited BNPL literature because there is speculation, but the scarcity of empirical evidence to substantiate, how materialism influences BNPL use and increases the inclination for impulse buying, precipitating compulsive buying behavior.