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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.

Using a video revealing unhygienic practices in Chinese five-star hotels as the case study, this study aims to understand the impact of service failure online exposure on hotel revenue performance in terms of seriousness, magnitude and duration, as well as to identify the hotel-characteristics and hotel-responsiveness factors that influence revenue recovery.

This study uses the actual Revenue per Available Room data of ten hotels involved in the incident and five different market segments during 2016–2019. Event study method is used to investigate the effect of online exposure on hotel revenue performance.

This study confirms the significant negative effect of online exposure and that hotels take nearly nine months to fully recover. The results indicate that hotel size, hotel age and response strategy play an important role in reducing negative impacts. Moreover, this study reveals the dynamic spillover effects of online exposure on different hotel market segments. These effects change from a competitive to a contagious effect with a decrease in class ratings.

Low-class hotel managers should take effective actions to avoid possible negative spillovers from others’ service failure incidents. Hotel managers could consider the synergy of different strategies rather than a single response strategy to minimize losses.

This study theoretically broadens knowledge about the negative impact of online exposure on Chinese hotel revenue. Additionally, the findings examine the dynamic spillover effects on hotels in different segments. Furthermore, they extend the existing findings on the negative impact of online public opinion crises.

本研究以一段揭示中国五星级酒店不卫生行为的视频为案例, 旨在了解网上曝光的服务失败事件在严重程度、规模和持续时间方面对酒店收入绩效的影响, 并确定影响收入恢复的酒店特征和酒店回应因素。

本研究使用了2016–2019年期间10家涉及酒店和5个不同的细分市场的实际每间可用房收入（RevPARs）数据。采用事件研究法（ESM）来研究网上曝光对酒店收入绩效的影响。

本研究证实了网上曝光的显著负面效应, 酒店需要近9个月的时间才能完全恢复。结果表明, 酒店规模、酒店年龄和回应策略在减少负面影响方面发挥了重要作用。此外, 本研究还揭示了在线曝光对不同酒店细分市场的动态溢出效应。这些效应随着酒店星级的下降而从竞争效应变为传染效应。

低星级酒店管理者应采取有效行动, 避免其他酒店的服务失败事件可能带来的负面溢出效应。酒店管理者可以考虑不同策略的协同作用, 而不是单一的回应策略来减少损失。

本研究从理论上拓宽了关于网上曝光对中国酒店收入绩效的负面影响的知识。与此同时, 本研究的结果考察了不同细分市场的酒店的动态溢出效应。此外, 还扩展了现有的关于网络舆情危机的负面影响的研究结果。

Este estudio utiliza los datos reales de ingresos por habitación disponible (RevPAR) de 10 hoteles implicados en el incidente y cinco segmentos de mercado diferentes durante 2016-2019. Se utiliza el método de estudio de sucesos (ESM) para investigar el efecto de la exposición en línea en el rendimiento de los ingresos de los hoteles.

Utilizando como caso de estudio un vídeo que revela prácticas antihigiénicas en hoteles chinos de cinco estrellas, este estudio pretende comprender el impacto de la exposición online de fallos en el servicio sobre el rendimiento de los ingresos hoteleros en términos de gravedad, magnitud y duración, así como identificar las características y los factores de respuesta del hotel que influyen en la recuperación de los ingresos.

Este estudio confirma el importante efecto negativo de la exposición online, tardando los hoteles casi nueve meses en recuperarse totalmente. Los resultados indican que el tamaño del hotel, su antigüedad y la estrategia de respuesta desempeñan un papel importante en la reducción del impacto negativo. Además, este estudio revela los efectos indirectos dinámicos de la exposición online en diferentes segmentos del mercado hotelero. Estos efectos cambian de un efecto competitivo a un efecto contagioso con una disminución de las calificaciones de la categoría o clase hotelera.

Los revenue managers de los hoteles de categoría baja deberían tomar medidas eficaces para evitar posibles repercusiones negativas de los fallos en el servicio de otros hoteles. Los directores de hotel podrían considerar la sinergia de diferentes estrategias en lugar de una única estrategia de respuesta para minimizar las pérdidas.

Este estudio amplía teóricamente los conocimientos sobre el impacto negativo de la exposición online en los ingresos de los hoteles chinos. Además, los resultados examinan los efectos indirectos dinámicos en hoteles de diferentes segmentos. Además, amplían los resultados existentes sobre el impacto negativo de las crisis de opinión pública online.

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 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.

Financial inclusion is measured by the number of people who use the formal financial system and banks in particular. Limited access to formal banking services and the existence of unbanked households is a main policy concern. The authors evaluate how the use of prepaid (reloadable) debit cards by unbanked households affects financial inclusion and specifically the potential for these households to participate in the formal financial system and open a bank account.

The authors apply matching models to analyze survey data from the Federal Deposit Insurance Corporation National Survey of the Unbanked and Underbanked Households from 2009 to 2019 and evaluate how prepaid cards use affects plans to open a bank account.

Unbanked households who use prepaid cards are 5% less likely to open a bank account compared to the matched nonusers of prepaid cards. In addition, prepaid card users are 12% more likely to use nonbanks to transfer money/transact online and 18% more likely to have obtained loans from alternative financial services providers compared to the matched unbanked nonusers of prepaid debit cards.

No previous work has estimated the causal impact of use of prepaid cards on financial inclusion.

Bank creditworthiness refers to the evaluation of a bank’s ability to meet its financial obligations. It is an assessment of the bank’s financial health, stability and capacity to manage risks. This paper aims to investigate the credit rating patterns that are crucial for assessing creditworthiness of the Islamic banks, thereby evaluating the stability of their industry.

Three distinct machine learning algorithms are exploited and evaluated for the desired objective. This research initially uses the decision tree machine learning algorithm as a base learner conducting an in-depth comparison with the ensemble decision tree and Random Forest. Subsequently, the Apriori algorithm is deployed to uncover the most significant attributes impacting a bank’s credit rating. To appraise the previously elucidated models, a ten-fold cross-validation method is applied. This method involves segmenting the data sets into ten folds, with nine used for training and one for testing alternatively ten times changeable. This approach aims to mitigate any potential biases that could arise during the learning and training phases. Following this process, the accuracy is assessed and depicted in a confusion matrix as outlined in the methodology section.

The findings of this investigation reveal that the Random Forest machine learning algorithm superperforms others, achieving an impressive 90.5% accuracy in predicting credit ratings. Notably, our research sheds light on the significance of the loan-to-deposit ratio as a primary attribute affecting credit rating predictions. Moreover, this study uncovers additional pivotal banking features that intensely impact the measurements under study. This paper’s findings provide evidence that the loan-to-deposit ratio looks to be the purest bank attribute that affects credit rating prediction. In addition, deposit-to-assets ratio and profit sharing investment account ratio criteria are found to be effective in credit rating prediction and the ownership structure criterion came to be viewed as one of the essential bank attributes in credit rating prediction.

These findings contribute significant evidence to the understanding of attributes that strongly influence credit rating predictions within the banking sector. This study uniquely contributes by uncovering patterns that have not been previously documented in the literature, broadening our understanding in this field.

This paper aims to provide a review of literature directions regarding the potential impact of fintech operators on the financial services market globally. This paper reviews the literature to identify possible benefits or challenges that fintech firms can have for the traditional banking system.

This paper is based on a review of published research papers related to fintech and digital finance. The Scopus database, SSRN database and google scholar were used to find relevant research papers. The final sample included impactful papers about the effect of fintech activities on the banking and financial services industry.

The current paper indicated that while fintech firms would take some market share away from banks, it is not expected that fintech firms would substitute banks. However, banks are required to accelerate their adoption of innovations and advanced technology to compete with fintech firms. It is also proposed that strategic partnerships and cooperation could happen between banks and fintech companies in a way that benefits both sides.

The present paper adds to the understanding of the effect of the fintech firms’ growth on the banking industry in light of the emerging opportunities and threats for the financial sector. The paper also provides guidance for fruitful research on the impact of fintech activities on social and economic welfare in the future.

Introduction: When people need to take decisions, being economic decisions or otherwise, their decisions tend to rely on information the brain has already processed, and this includes the resources that the person has already invested. This is called sunk cost bias in the behavioural economics literature. On the other hand, mental practices could lead to the mental accounting bias, where people allocate a different value to a fixed amount of money, depending on circumstances.

Purpose: In this chapter, both biases mental accounting and sunk cost are investigated for the tourism industry in Turkey.

Methodology: The topic is researched through scenario-based questions and the Chi-square Automatic Interaction Detector (CHAID) method is applied.

Findings: As a result, it could be reported that people, regardless of gender, fall into sunk cost and mental accounting biases in decisions relating to their vacations. Mental accounting biases can be primarily explained using the scenario questions posed rather than gender, education, and income while sunk cost bias is explained by status, ‘being s university student’ and ‘income level’.

Practical implications: Rapid price changes in the tourism industry can disturb consumers who are mental accounting and sunk cost biased. So, they can change their holiday preferences or be dissatisfied with it and give negative feedback.

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.