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This paper aims to determine causal relationships between the level of productive credit, real deposits and money demand – all of them in real terms – and Gross National Product between 2006 and 2020.

The vector autoregressive technique (VAR) was used, where data from real macroeconomic aggregates published by the Central Bank of Ecuador (BCE) are correlated, such as productive credit, gross domestic product (GDP) per capita, deposits and money demand.

The results indicate that there is no causal relationship, in the Granger sense, between GDP and financial activity, but there is between the growth rate of real money demand per capita and the growth rate of total real deposits per capita.

The study shows that bank credit mainly finances the operations of current assets and/or liabilities. In addition, economic agents use the banking system mainly to carry out transactional and precautionary activities.

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 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 aims to determine the effect of bank ownership on the credit risk of Indonesian Islamic local banks (ILBs).

This study uses the system generalized method of moments (GMM) estimation technique with a sample of 155 Islamic local banks in Indonesia from 2012 to 2019.

The results show that commissioner board (D.COW) ownership has a negative effect on credit risk. This indicates that an increase in the number of shares of Islamic local banks owned by the commissioner board reduces credit risk. On the other hand, government ownership (D.GOW), the Sharia supervisory board (D.SOW) and the director board (D.DOW) do not affect credit risk.

The government, Sharia supervisory board and director board need opportunities to easily own more Islamic local bank shares. Therefore, the provisions regarding the share ownership rights of the government, Sharia supervisory board and director board need to be improved to increase their role in reducing credit risk.

Previous researchers have not studied the effect of government ownership, the commissioner board, the Sharia supervisory board and the ownership of directors on credit risk at the ILB in Indonesia.

This paper aims to review the literature extensively by analysing recent work and providing a guide for models, data sets and research findings.

This paper reviews the literature extensively by analysing recent work and providing a guide for models, data sets and research findings within the context of capital market imperfections. The authors further break down the literature into closer-in-nature categories for reader’s convenience and comprehension. Finally, the authors address gaps in the existing literature and propose government policies that can tone down the potential effect of credit rationing on employment.

This paper provides a map of the literature so as to help future researchers in the relevant literature and give a short insight of what has been explored so far.

This paper is original and is the result of a thorough review of an extensive literature.

The credit of agricultural product quality and safety reflects the ability of the main actors involved in the supply chain to provide reliable agricultural products to consumers. To fundamentally solve the problem of agricultural product quality and safety, it is worth studying how to make the credit awareness and integrity self-discipline of the supply chain agriculture-related subjects strengthened and the role and value of credit supervision given full play. Starting from the application of blockchain in the agricultural product supply chain, this paper aims to investigate the main factors affecting the credit regulation of agricultural product quality.

Using the DEMATEL-ISM (decision-making trial and evaluation laboratory–interpretative structural modeling) method, we analyze the credit influencing factors of agricultural quality and safety empowered by blockchain technology, find the causal relationship between the crucial influencing factors and deeply explore the hierarchical transmission relationship between the influencing factors. Then, the path analysis in structural equation modeling is utilized to verify and measure the significance and effect value of the transmission relationship among the crucial influencing factors of credit regulation.

The results show that the quality and safety credit regulation of agricultural products is influenced by a combination of direct and deep influencing factors. Long-term stable cooperative relationship, Quality and safety credit evaluation, Supply chain risk control ability, Quality and safety testing, Constraints of the smart contract are the main influence path of blockchain embedded in agricultural product supply chain quality and safety credit supervision.

Credit supervision is an important means to improve the ability and level of social governance and standardize the market order. From the perspective of blockchain embedded in the agricultural supply chain, the regulatory body is transformed from the product body to the supply chain body. Take the credit supervision of supply chain subjects as the basis of agricultural product quality supervision. With the help of blockchain technology to improve the effectiveness of agricultural product quality and safety credit supervision, credit supervision is used to constrain and incentivize the behavior of agricultural subjects.

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 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.

The goal of this study was to determine the impact of access to credit facilities on financial performance among farmers of cooperative societies. The study also tested the predictive power of financial literacy.

The descriptive survey research design was used for the study while the sample size was 240 farmers of cooperative societies from South-East Nigeria. The farmers were categorised into those with access to credit facilities and those without access to credit facilities. A structured questionnaire was used to collect data for the study. Data were analysed using multiple analyses of variance (MANOVA) and multiple regression analysis.

Farmers with access to credit facilities reported higher financial performance such as return on investment, working capital, net profit, profit margin and sales. However, those without access to credit facilities reported lower mean scores on financial performance. Also, financial literacy, like financial knowledge, attitude and awareness, significantly predicts the impact of access to credit facilities on financial performance. It was also found that the duration of repayment of credit facilities, like medium and long term, contributes more to improving financial performance.

This study has shown that even though access to credit facilities impacts financial performance, financial literacy is an important consideration. Also, the duration of repayment is a crucial factor.

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.