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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 study aims to develop a novel approach for predicting default risk in bancassurance, which plays a crucial role in the relationship between interest rates in banks and premium rates in insurance companies. The proposed method aims to improve default risk predictions and assist with client segmentation in the banking system.

This research introduces the group method of data handling (GMDH) technique and a diversified classifier ensemble based on GMDH (dce-GMDH) for predicting default risk. The data set comprises information from 30,000 credit card clients of a large bank in Taiwan, with the output variable being a dummy variable distinguishing between default risk (0) and non-default risk (1), whereas the input variables comprise 23 distinct features characterizing each customer.

The results of this study show promising outcomes, highlighting the usefulness of the proposed technique for bancassurance and client segmentation. Remarkably, the dce-GMDH model consistently outperforms the conventional GMDH model, demonstrating its superiority in predicting default risk based on various error criteria.

This study presents a unique approach to predicting default risk in bancassurance by using the GMDH and dce-GMDH neural network models. The proposed method offers a valuable contribution to the field by showcasing improved accuracy and enhanced applicability within the banking sector, offering valuable insights and potential avenues for further exploration.

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.

The purpose here is to show how the “shadow” economy has grown in scale and impetus in recent years, though even before modern times it has been present (e.g. the City of London, Shaxson, 2011) since at least the middle ages. The reasons for this have become complicated, but we can identify some “deep structures” that are common. Firstly, “globalisation” made it easier for multinationals to escape national regulatory regimes. Secondly, one of the ways neoliberal trading regulations allowed such actors to augment their assets was by means of what they initially called “transfer-pricing” but which now is officially known as “profit shifting” through tax havens. Thirdly, the growth in international trade in legal and illegal ways caused money laundering – even by otherwise respectable banks – to grow across borders. Conversely, from the supply-side, tax haven status was increasingly accessed by jurisdictions that sought to achieve economic growth by supplying tax haven services, both Delaware and Ireland as exemplars of a “developmental” fiscal policy.

This paper adopts a “pattern recognition” design, an approach that is abductive, meaning interpretive, as shown in the observation that explanation can be valid or reliable without direct observation. This is shown in the indirect observation that “rain fell because the terrace has puddles” or “ancient glaciers once carved this valley”.

Reviewing the European Union’s (EU) list of non-co-operating jurisdictions in support of the OECD’s review of base erosion and profit-shifting activity, Collin concluded the EU’s listing “moved the needle” somewhat but was only a modest success. This is because of its reluctance to sanction its own members or large economies like the USA. Data on foreign direct investment and offshore banking assets suggest listed jurisdictions did not suffer notably from being named and shamed. In all cases studied, this contribution found legally damaging, fraudulent, conflict of interest and corrupt practice activities everywhere.

The originality is found in three spheres. Firstly, the pattern recognition method was vindicated in yielding hard to research results. Secondly, the “assemblage-thirdspace” theory was found advantageous in demonstrating the uneven geography of tax haven clusters and their common history in turbocharging economic development. Finally, the empirics showed the ruses executed by cluster members in tax havens to circumvent the law from global management consultancies to micro-firms consisting of tax lawyers and other experts interacting in knowledge supply chains of dubious morality.

This study aims to gain insight into the motivations behind the decision to use high-cost payday loans by households who possess mainstream credit and to determine whether this behavior has changed over time.

Using data from Statistics Canada’s Surveys of Financial Security, probit models are used to examine the sociodemographic and financial indicators associated with payday loan use.

The analysis uncovers the sociodemographic and financial characteristics of payday loan-user households with access to lower-cost short-term loans. The findings indicate that the likelihood of payday loan use has risen over time. Additional analysis reveals that indicators of financial instability are positively associated with payday loan use among this group.

This research highlights the dichotomy of payday loan users and recommends policymakers tailor solutions to the specific needs of different types of payday loan users.

This research highlights the distinguishing sociodemographic and financial characteristics of payday loan user households and recommends policymakers tailor solutions to the specific needs of different types of payday loan users.

This is the first study, to our knowledge, to focus analysis on payday loan use of those with access to lower-cost short-term credit alternatives in Canada and to include measures of financial instability in the analysis. This research is timely given the current economic environment of high interest rates and high levels of household debt.

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.

The description of “blockchain banking”, the determination of “the sub-processes” of “blockchain banking” as a “business process”, and the assessment of “maturity level” in Parsian Bank.

Theoretical sources on “blockchain banking” were initially investigated. Then the “sub-processes” of “blockchain banking” as a “business process” were extracted by Parsian Bank's experts through the “Delphi method”. Next, the “sequence” of the “sub-processes” was determined by means of the “AHP”. Eventually, Parsian Bank's maturity levels for all the sub-processes as well as the overall maturity level were specified on the basis of the “CMMI” V1.3 in order for Business Process Management (BPM).

Blockchain banking’ combines traditional banking with cryptocurrencies, which can be provided by merging “hybrid e-wallet” with “bank account” and “bank card” – all together as “crypto bank account”. Plus, “hybrid e-wallet” is a form of mobile e-wallet on blockchain that supports both cryptocurrencies and traditional currencies in the same platform by which the purchase and sale of cryptocurrencies are possible. Besides, “Blockchain banking service” can also be offered within the framework of “open banking” aligned with “open innovation” through a FinTech (or a beta bank) in collaboration with a licensed bank via “open API”, which is called “blockchain banking based on FinTech”. At last, the eight sub-processes of “blockchain banking” were determined and Parsian Bank's “maturity level” was specified.

This is the very first practical guide to “blockchain banking service”.

The research documented in this paper aims to examine multiple factors related to borrowers' default in peer-to-peer (P2P) lending in the USA. This study is motivated by the hypothesis that both P2P loan characteristics and macroeconomic variables have influence on loan performance. The authors define a set of loan characteristics, borrower characteristics and macroeconomic variables that are significant in determining the probability of default and should be taken into consideration when assessing credit risk.

The research question in this study is to find the significant explanatory variables that are essential in determining the probability of default for LendingClub loans. The empirical study is based on a total number of 1,863,491 loan records issued through LendingClub from 2007 to 2020Q3 and a logistic regression model is developed to predict loan defaults.

The results, in line with prior research, show that a number of borrower and contractual loan characteristics predict loan defaults. The innovation of this study is the introduction of specific macroeconomic indicators. The study indicates that macroeconomic variables assessed alongside loan data can significantly improve the forecasting performance of default model. The general finding demonstrates that higher percentage change in House Price Index, Consumer Sentiment Index and S&P500 Index is associated with a lower probability of delinquency. The empirical results also exhibit significant positive effect of unemployment rate and GDP growth rate on P2P loan default rates.

The results have important implications for investors for whom it is of great importance to know the determinants of borrowers' creditworthiness and loan performance when estimating the investment in a certain P2P loan. In addition, the forecasting performance of the model could be applied by authorities in order to deal with the credit risk in P2P lending and to prevent the effects of increasing defaults on the economy.

This paper fulfills an identified need to shed light on the association between specific macroeconomic indicators and the default risk from P2P lending within an economy, while the majority of the existing literature investigate loan and borrower information to evaluate credit risk of P2P loans and predict the likelihood of default.

This study aims to establish that the relationship between the risky indebtedness behavior (RIB) of consumers and their attitude toward adopting buy-now-pay-later (BNPL) is not immediate but is mediated through impulse buying. Moreover, it explores how perceived risk moderates the association between the attitude to adopt BNPL and its adoption intention.

This study used the existing theoretical and empirical evidence to propose a model and validated it using the data collected from 339 young shoppers in India. Analysis of data is conducted using partial least squares structural equation modeling.

The study results show that consumers’ RIB is not directly related to their attitude toward BNPL. However, impulse buying fully mediates this relationship, influencing the attitude toward BNPL. Impulse buying and attitude serially mediate the relationship between RIB and BNPL adoption intention. Further, in the context of BNPL, perceived risk strengthens the attitude-intention gap.

This study advises policymakers and BNPL providers to carefully assess users’ creditworthiness to prevent those already in debt from entering into a detrimental loop.

This study provides novel perspectives on consumer’s RIB and BNPL within the Indian context. The study additionally identifies the mediating influence of impulse buying and the moderating effect of perceived risk on BNPL adoption intention.

The purpose of the study is to identify cybersecurity threats that hinder the adoption of digital banking and provide sustainable strategies to combat cybersecurity risks in the banking industry.

Systematic literature review guidelines were used to conduct a quantitative synthesis of empirical evidence regarding the impact of cybersecurity threats and risks on the adoption of digital banking.

A total of 84 studies were initially examined, and after applying the selection and eligibility criteria for this systematic review, 58 studies were included. These selected articles consistently identified identity theft, malware attacks, phishing and vishing as significant cybersecurity threats that hinder the adoption of digital banking.

With the country’s banking sector being new in this area, this study contributes to the scant literature on cyber security, which is mostly in need due to the myriad breaches that the industry has already suffered thus far.