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To improve the loan repayment performance (LRP) of microfinance banks (MFBs) in Pakistan, this study aims to look at the direct impact of multiple borrowing (MB) on LRP and client-business performance (CBP), as well as the direct impact of CBP on LRP. The moderating function of pandemic factors in the relationship between MB and CBP, as well as the mediating effect of CBP in the association between MB and LRP, was also investigated in this study.

A questionnaire was used to obtain data from 531 lower-level workers of microfinance institutions (MFIs) for the study. The respondents were chosen using stratified sampling, which divided the target population into four influential groups: lending officers in agriculture, lending officers in businesses, lending officers in gold loans and lending officers in salary loans. In this study, a two-stage structural equation modeling approach was used, including a measurement model (outer model) and a structural model (inner model). The validity and reliability of the questionnaire were investigated using the measurement model (outer model), whereas PLS-SEM bootstrapping was performed to test the hypothesis and find the relationship among different underpinning constructs by using the structural model (inner model).

The outcomes of this study demonstrate that MB has a direct impact on CBP, and that CBP has a direct impact on LRP. MB, on the contrary, had no direct and significant impact on LRP in this study. The idea that CBP mediates the relationship between MB and LRP, as well as the moderating effect of pandemic factors on the relationship between MB and CBP, is supported by this research.

Until now, the influence of MB on LRP via the mediating role of CBP and the moderating role of a pandemic factor in the setting of Pakistani MFBs has received little attention. During the COVID-19 pandemic, this research also aids MFBs in better understanding MB and its impact on LRP. Furthermore, based on the findings of this study, Pakistani MFIs can enhance their LRP by implementing new lending regulations, particularly with reference to MB and the COVID-19 pandemic.

In the backdrop of an increase in market-based banking activities, this paper aims to study operational efficiency of Indian banking sector during 2009–2010 through 2017–2018 considering Capital Gain and Gain from Forex Market (as desirable outputs) and Slippage (as undesirable byproducts) simultaneously, along with Advances – a desirable output considered in the traditional banking performance assessment literature. This enables to have an assessment of performance (as captured by the measured efficiency scores) of Indian Banks following an alternative viewpoint about the banking activities. The authors also explain such efficiency scores in terms of bank-specific factors, banking industry competition scenario and interest rate channel.

Using data envelopment analysis (DEA) method, the authors estimate six alternatives but interlinked operational efficiency scores (TES) of the Indian domestic commercial banks. In the second stage, they explain such TES in terms of bank-specific factors, banking industry competition scenario and interest rate channel.

The authors observe that the private sector banks as a group outperform those under public ownership. Moreover, although the private sector banks could maintain somewhat consistency in their operational efficiency performance over the sample period, public sector banks clearly show a declining tendency. The second stage econometric estimation results show that the priority sector lending has a negative effect on efficiency. Interestingly, the authors get varying results for the relationship between maturity and efficiency score depending on banks’ strategies on stressed assets management. Furthermore, the analyses result that banks are not so efficient in managing relatively larger-volume loans. It is also observed that banks’ efficiency positively depends on the Credit-to-Deposit (CD) ratio. It is found that the overall operational efficiency of the banks to manage their credit risk portfolio improves with a reduction in the lending rate (LR). However, the interaction of lending activities and capital market shows that with the increase in LR, corporate borrowers may switch to capital market to explore for desired funds, which may induce the banking sector to investment in capital markets and create a positive market sentiment.

Literature, although scanty, is there dealing stressed assets of a bank as some undesirable byproducts of its operational and business activities. However, such literature mostly done within the traditional framework of banking business activities and modern market-based business activities are almost absent in the literature. The authors have done it in the present study.

The blue and green firms are notable contributors to sustainable development. Similar to other businesses in circular economies, blue and green firms also face financing constraints. This paper aims to assess whether blue and green lending help in optimizing the interest rate spreads and the likelihood of default.

This analysis is based on an unbalanced panel of banks from 20 eurozone countries for eleven years between 2012 and 2022. The key indicators of banking include interest rate spread and a market-based probability of default. The paper assesses how these indicators are influenced by exposure to green and blue firms after controlling for several exogenous factors.

The results show a positive relationship between green and blue lending and spread, while there is a negative link with the probability of default. This confirms that the blue and green exposure positively supports the credit portfolio both in terms of profitability and risk management.

The banking system is among the key contributors to corporate finance and to enable continuous access to sustainable finance, the banking firms must be incentivized. While many studies analyze the impact of green lending, to the best of the authors’ knowledge, this study is among the very few that extend this analysis to blue economy firms.

Research shows that having student loan debt in retirement is associated negatively with life satisfaction, suggesting that student debt is a bane of retiree well-being. The rationale for this study is to determine the factors related to owing student debt in retirement, given the adverse effects on the well-being of retired households.

The study utilizes pooled cross-sectional data from the 2015 and 2018 U.S. National Financial Capability Study. The empirical analysis uses a sample of retired Americans aged 65 years and older (N = approximately 8,000) and estimates two-block logistic regression models to examine the effects of demographic, socioeconomic and behavioral factors on student loan indebtedness in retirement. A sensitivity analysis is performed for the subsample of retirees holding student debt for their children's education. Statistical interpretations use odds ratios.

The findings indicate that financial literacy, age, homeownership and high subjective financial knowledge are associated with a low likelihood of holding student loan debt in retirement. However, being Black, having postsecondary education, having difficulty covering expenses, having financially dependent children, having high-risk preferences and spending more than income increase the likelihood of holding student debt in retirement. The ensuing discussion will assist financial planners and educators identify practical ways to shape decisions regarding student loan debt in retirement.

The amount of student loan debt is unavailable in the dataset for analysis. One cannot infer causal relations from the study. The factors examined do not reflect the time the student loan was obtained.

The study focuses on the determinants of student loan indebtedness among retired Americans rather than young adults or older adults on the verge of retirement. The paper enhances the understanding of student loan holdings in the decumulation phase of the life cycle. Many US individuals have low retirement savings from which they draw a retirement income. The more the student debt burdens on retired Americans, the greater the likelihood of outliving their resources and experiencing poverty.

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 crux of this paper is to unveil efficient features and practical tools that can predict credit default.

Annual data of non-financial listed companies were taken from 2000 to 2020, along with 71 financial ratios. The dataset was bifurcated into three panels with three default assumptions. Logistic regression (LR) and k-nearest neighbor (KNN) binary classification algorithms were used to estimate credit default in this research.

The study’s findings revealed that features used in Model 3 (Case 3) were the efficient and best features comparatively. Results also showcased that KNN exposed higher accuracy than LR, which proves the supremacy of KNN on LR.

Using only two classifiers limits this research for a comprehensive comparison of results; this research was based on only financial data, which exhibits a sizeable room for including non-financial parameters in default estimation. Both limitations may be a direction for future research in this domain.

This study introduces efficient features and tools for credit default prediction using financial data, demonstrating KNN’s superior accuracy over LR and suggesting future research directions.

The objective of this paper is to study the impact of political connection and governance on credit rating and whether there is a substitution or complementary relationship between them.

In order to achieve the objective, a succession of eight ordered probit regressions has been carried out. Moderating variables between the political connection and governance characteristics were introduced. The whole population is taken as a sample, i.e., 27 Tunisian companies that are evaluated by FITSH NORTH AFRICA agencies over a period of 10 years (2009–2018).

The outcomes are mixed. They show that the political connection does not always influence credit rating; the size and board independence always improves credit rating; the duality between the functions affects credit rating; whereas the majorities’ proportion does not influence credit rating; and a substitution between the political connection and the governance characteristics is validated.

Like any other research, our results are factors of our measures and variable choice and depends heavily on the how these variables were conceived. Also, although our number of observations responds to the statistical result generalization requirements, our sample remains relatively narrow with 27 companies only.

In practice, the research will allow investors to have a better vision upon the future of their investments based on whether to develop their governance system or promote political networking. It will also prompt lenders to look beyond ratings and consider factors such as political connections to make a rational judgment on their future placements.

This study leads us to find various solutions: the establishment of credit agencies that take into consideration all the data of all the operators taken as a whole (bank, leasing company, and factoring). It encourages the reorganization of the Tunisian banking sector through mergers for example.

This study is a pioneer in the credit rating field in Tunisia, where the source of debt financing is the most used by all enterprises across all sectors. This study extends the literature of political connection effectiveness, independent directors, board size, in improving corporate performance and credit rating.

For close to two decades, the West African Monetary Zone (WAMZ) has been preparing to launch a second monetary union within the ECOWAS region. This study aims to determine the impact such a unionised monetary regime will have on financial stability as represented by the nonperforming loan ratios of Ghana in a counterfactual framework.

This study models nonperforming loan ratios as dependent on the monetary policy rate and the business cycle. The study then used historical data to estimate the parameters of the nonperforming loan ratio response function using an Autoregressive Distributed Lag (ARDL) approach. The estimated parameters are further used to estimate the impact of several counterfactual unionised monetary policy rates on the nonperforming loan ratios and its volatility of Ghana. As robustness check, the Least Absolute Shrinkage Selection Operator (LASSO) regression is also used to estimate the nonperforming loan ratios response function and to predict nonperforming loans under the counterfactual unionised monetary policy rates.

The results of the counterfactual study reveals that the apparent cost of monetary unification is much less than supposed with a monetary union likely to dampen volatility in non-performing loans in Ghana. As such, the WAMZ members should increase the pace towards monetary unification.

The paper contributes to the existing literature by explicitly modelling nonperforming loan ratios as dependent on monetary policy and the business cycle. The study also settles the debate on the financial stability cost of a monetary union due to the nonalignment of business cycles and economic structures.

The reason for concern is the rapid decline in loans to small enterprises which is critical to their performance, compared to large businesses following the periods of banking reformations in Nigeria. Thus, the purpose of this paper is to investigate the influence of risk perception on bank lending behaviour to small enterprises. It also investigates the impact of government intervention, consolidation and recapitalization on the relationship between risk perception and bank lending behaviour to small enterprise.

This study empirically analysed (ordinary least square) secondary data obtained from the Central Bank of Nigeria Statistical Bulletins, Annual Statement of Accounts covering the period 1992–2020.

The results show that the absence of government interventions and the presence of banking reformations have statistically negative significant effect on bank lending to small enterprises. The findings challenge the argument that generally assumes risk aversion of banks towards small enterprise lending because of small enterprise’s inability to prove their credit worthiness and consequently constraining access to finance to the sector. Instead, the results and analysis from this study found theoretical support for the variation of bank behaviour in lending to small enterprises depending on the status of wealth of the financial system.

A key lesson from this study for government concerned about promoting performance of the small enterprise sector is that regulating and enforcing lending requirements on access to debt financing of the sector is necessary if constraints in access debt finance is to be eliminated. Second, while strategies such as bank consolidation, recapitalization may help strengthen and make financially robust the banking system; it places the banks in a gain position where losses looms to them than gain.

This study challenges the argument that generally assumes risk aversion of banks towards small enterprise lending as a result of inability to prove their credit worthiness and consequently constraining access to finance to the sector. Instead, the results and analysis from this study reveal a variation in lending to small enterprises and suggests that the position of the bank in relation to a reference point influences how risk is perceived by the bank and thus impacts on their risk decision-making behaviour.

This study aims to examine the impact and contribution of priority and non-priority sectors, as well as their sub-sectors, on the gross non-performing assets of public, private and foreign sector banks.

The Reserve Bank of India's database on the Indian economy is used to retrieve data over 13 years (2008–2021). Public sector (12), private sector (22) and foreign sector (44) banks are represented in the sample. Two-way ANOVA, multiple regression and panel regression statistical techniques are used in SPSS and EViews to examine the data. Further, the results are also validated by using robustness testing by applying the fully modified ordinary least square (FMOLS) and dynamic least square (DOLS) regression.

The results showed that, for private and foreign banks, the non-priority sector makes up the majority of the total gross non-performing assets, although both the priority and non-priority sectors are substantial for public sector banks. The largest contributors to the total gross non-performing assets in public, private and foreign banks are industries, agriculture and micro and small businesses. The FMOLS displays robustness results that are qualitatively similar to the baseline result.

Based on the study's findings about the patterns of non-performing assets originating from these specific industries, banks might improve the way in which these advanced loans are managed.

There has not been much research done on the subject of sub-sector-specific non-performing assets and how they affect total gross non-performing assets across the three sector banks. The study's primary focus will be on the issue of non-performing assets in the priority’s and non-priority’s sub-sectors, namely, agricultural, micro and small businesses, food credit, industries, services, retail loans and other priority and non-priority sectors.