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To enhance the loan repayment performance of microfinance institutions (MFIs) in Pakistan, this study aims to analyze the direct impact of social capital and loan credit terms on loan repayment performance and microenterprises’ business performance while considering the mediating role of microenterprises’ business performance on the relationship between social capital, loan credit terms and loan repayment performance.

The analysis was conducted based on the data gathered via a questionnaire distributed to 316 microenterprises owners. The respondents were selected using the stratified sampling technique by dividing the target population into three influential groups of manufacturing, trading and services microenterprises. The reliability and validity of the constructs were established using (1) factor loading, (2) Cronbach’s alpha, (3) composite reliability, (4) average variance extracted, (5) the variance inflation factor, (6) the Fornell–Larcker criterion and (7) the heterotrait–monotrait ratio. The structural equation modeling technique was then applied, and the hypotheses were tested based on the structure model generated through bootstrapping by using partial least squares structural equation modeling.

The results confirm the direct impact of social capital and loan credit terms on microenterprises’ business performance and loan repayment performance. It also supports the mediating role of microenterprises’ business performance toward the relationship between social capital, loan credit terms and loan repayment performance while considering the direct impact of microenterprises’ business performance on loan repayment performance.

To date, the direct impact of social capital and loan credit terms on microenterprises’ business performance and loan repayment performance has been hardly investigated in the context of Pakistan. This study also examines the mediating role of microenterprises’ business performance toward social capital, loan credit terms and loan repayment performance. The findings will enable both MFIs and microenterprises to improve their business performance and loan repayment performance through enhanced social ties and the development of more flexible credit products that protect the borrowers’ interests and the interest of lenders.

Over the last ten years (2010–2019), the amount of nonperforming loans (NPLs) has been more than tripled in the banking industry of Bangladesh. Thus, this paper explores the behavioral dimensions, which contribute to the NPLs.

By analyzing social, cultural, psychological, political, economic, internal control mechanism and law enforcement contexts of Bangladesh, this study identifies nepotism (NE), moral hazard (MH ), inadequate collateral (IC), poor credit assessment (CA), lack of proper monitoring (LPM), repayment flexibility (RF), business risk (BR) and lending interest rate (LIR) as the catalysts of raising NPLs. Next, a structured questionnaire survey has been performed in Bangladesh among bank officials who closely work in credit risk management, credit supervision, corporate finance and loan recovery department. Finally, partial least squares (PLS) path modeling, a variance-based technique of structural equation modeling, is used in this study as a statistical tool to analyze the data.

This study finds that moral hazard problem, lack of proper monitoring, inadequate collateral and nepotism have significant positive impact on the raising of NPLs. Unfortunately, this study does not find any statistical significance of poor credit assessment, business risk and repayment flexibility on the NPLs in Bangladesh. Finally, this study reveals that lending interest rate has significant positive impact on the NPLs. Hence, this study concludes that domestic lending interest rate is not lower enough, and so this double-digit interest rate affects negatively to loan repayment.

This study concludes that moral hazard problem of borrower, lack of board independence, lack of proper monitoring, form and extent of collateral, management lobbying, indecorous personal guarantee by management, dependent-independent directors and nepotism are extensively contributing for occurring NPLs in Bangladesh. These noninstitutionalized stimulators should adequately be scrutinized by regulatory bodies, policy makers and banks. Besides, LIR needs to be decreased in a convenient level for mitigating NPLs.

This study is the empirical evidence of behavioral dimensions related with the growth of NPLs in Bangladesh by taking direct response from knowledgeable bankers.

This article describes financial professionals' perceptions of their clients' financial behaviors and the explanatory factors underlying these behaviors.

In this qualitative research, the authors seek to understand financial professionals' experiences in relation to how their clients manage their own finances. The authors conduct and analyze 26 semi-structured interviews with financial professionals from several industries within the financial sector in Canada.

The professionals in this study noted that despite their clients' financial knowledge, several other factors can explain these individuals' financial behaviors. They include psychological factors (such as financial bias, the need for instant gratification, and the lack of awareness regarding the long-term effects of certain types of financial behaviors), financial habits (such as lifestyle, financial planning and lack of discipline) and the financial system's flexibility with respect to debt financing and repayment. These perceptions are categorized according to whether they are related to debt financing or repayment, savings or investments.

By using a qualitative methodology that relies on the perceptions of financial professionals, this study aims to better understand the financial behaviors of individuals and households, and these behaviors' underlying factors. This study's findings could be useful to various stakeholders interested, in one way or another, in financial literacy, such as organizations aiming to strengthen and promote financial literacy, educators, researchers, regulatory bodies of financial institutions and financial advisers.

The purpose of this study is to show that closure-based classification and regression models provide both high accuracy and interpretability.

Pattern structures allow one to approach the knowledge extraction problem in case of partially ordered descriptions. They provide a way to apply techniques based on closed descriptions to non-binary data. To provide scalability of the approach, the author introduced a lazy (query-based) classification algorithm.

The experiments support the hypothesis that closure-based classification and regression allow one to both achieve higher accuracy in scoring models as compared to results obtained with classical banking models and retain interpretability of model results, whereas black-box methods grant better accuracy for the cost of losing interpretability.

This is an original research showing the advantage of closure-based classification and regression models in the banking sphere.

The purpose of this paper is to evaluate the impact of short-term loan (STL) vs long-term loan (LTL) on wheat productivity of small farms in Sindh, Pakistan.

The econometric estimation is based on cross-sectional data collected in 2016 from 18 villages in three districts, i.e. Shikarpur, Sukkur and Shaheed Benazirabad, Sindh, Pakistan. The sample data set consist of 180 wheat farmers. The collected data were analyzed through different econometric techniques like Cobb–Douglas production function and Instrumental variables (two-stage least squares) approach.

This study reconfirmed that agricultural credit has a positive and highly significant effect on wheat productivity, while the short-term loan has a stronger effect on wheat productivity than the long-term loan. The reasons behind the phenomenon may be the significantly higher usage of agricultural inputs like seeds of improved variety and fertilizers which can be transformed into the wheat yield in the same year. However, the LTL users have significantly higher investments in land preparation, irrigation and plant protection, which may lead to higher wheat production in the coming years.

In the present study, only those wheat farmers were considered who obtained agricultural loans from formal financial institutions like Zarai Taraqiati Bank Limited and Khushhali Bank. However, in the rural areas of Sindh, Pakistan, a considerable proportion of small-scale farmers take credit from informal financial channels. Therefore future researchers should consider the informal credits as well.

This is the first paper to examine the effects of agricultural credit on wheat productivity of small farms in Sindh, Pakistan. This paper will be an important addition to the emerging literature regarding effects of credit studies.

This study aims to examine and compare the credit risk management (CRM) scenario of Islamic banks (IBs) and conventional banks (CBs) in Pakistan, keeping in view the phenomenal growth of Islamic banking and its future implications.

A sample of five CBs and four IBs was chosen out of the whole banking industry for the study. Secondary data obtained from the banks’ annual financial reports for 13 years, starting from 2004 to 2016, were analyzed. Multiple regression, correlation and descriptive analysis were used in the examination of the data.

The results show that loan quality (LQ) has a positive and significant impact on CRM for both IBs and CBs. Asset quality (AQ), on the other hand, has a negative impact on CRM in the case of IBs, but has a significantly positive relation with CRM in the case of CBs. The impact of 16 ratios measuring LQ and AQ have also been individually checked on CRM, by making use of a regression model using a dummy variable of financial crises for robust comparison among CBs and IBs. The model proved significant, and CRM performance of IBs was observed to be better than that of CBs. Moreover, the mean average value of financial ratios used as a measuring tool for these variables shows that the CRM performance of IBs operating in Pakistan was better than that of CBs over the period of the study.

The research findings are expected to facilitate bankers, investors, academics and policy makers to build a better understanding of CRM practices as adopted by CBs and IBs. The findings would be useful in formulating policy measures for the progress of the banking industry in Pakistan.

This research is unique in terms of its approach toward analyzing and comparing CRM performance of CBs and IBs. Such work has not been carried out before in the Pakistani banking industry.

The paper aims to disentangle the physiological credit risk from the credit risk coming from the inefficient screening and monitoring management process. The analysis is conducted on a sample of 338 Italian banks–56 joint-stock banks (SpA), 23 cooperative banks (Popolari) and 259 mutual banks (BCCs)–over the time period 2006–2017.

The authors use the maximum likelihood method to estimate the efficient frontier, as a set of best management credit practices, which minimises the credit risk defined on the basis of the level of loans granted, the technical structure of the loan portfolio (such as credit lines, mortgages, consumer loans and other technical loan categories) and the interest rate charges.

The empirical results show that the increase in non-performing loans (NPLs) is related both to the severe and protracted recession in Italy, which significantly reduced borrowers' capacity to service their debt, and to other factors, such as banks' lending monitoring policies with limited capacity to work-out defaulted loans.

The authors propose a new approach to the study of the performance of the credit process. With the stochastic frontier, the physiological credit risk, assumed by the bank according to its lending activity and management choices, is separated from the credit risk resulting from an inefficient management of the screening and monitoring process. In addition, the authors analyse the determinants of the excess of NPLs. This aspect is considered particularly original because the scientific contributions which consider the causes of NPLs have largely focused on the level of NPLs not considering the physiological part, linked to the structure of the bank's loan portfolio and its operational strategy and therefore not compressible and in any case not attributable to mismanagement or moral hazard.

Using bank-level data over the 1994–2015 period, the authors aim to investigate the role of bank-specific factors on credit risk in Italy by considering two different groups of banks, namely, cooperative and non-cooperative (commercial and popular), in different local markets.

Relying on highly territorially disaggregated data at labour market areas’ level, the authors estimate the impact of the role of bank-specific factors on credit risk in Italy from the estimation of a fixed-effect estimator. Non-performing loans to total loans has been used as a proxy of credit risk; the bank-specific factors are as follows: growth of loans, reflecting credit policy; log of total assets, controlling for banks’ size; loans to total assets, reflecting the volume of credit market; equity to total assets, capturing the solvency of banks and reflecting their capital strength; return on assets, reflecting the profitability of banks; deposits to loans, reflecting the intermediation cost; cost of total assets, reflecting the banks’ efficiency or volume of intermediation cost.

The empirical findings suggest that regulatory credit policy, capitalisation, volume of credit and volume of intermediation costs are the main bank-specific factors affecting non-performing loans. Nevertheless, the present analysis suggests that the behaviour of cooperative banks’ behaviour seems to be in line with that of commercial rather than popular banks, casting doubts about the feasibility of their credit policies. It turns out that recent reforms involving popular and cooperative banks represent the first step toward the enhancement of the stability and efficiency of the Italian banking system. While the present study’s benchmark results are not particularly affected by the degree of competition in the banking sector and by banks’ size, it shows that both cooperative and non-cooperative banks have undertaken more prudent credit policies after the advent of the financial crisis and the introduction of the Basel regulation.

The relationship between bank-specific factors and credit risk has been analysed using a rich sample of cooperative, commercial and popular banks in Italy over the 1994–2015 period. The authors rely on labour market areas being sub-regional geographical areas where the bulk of the labour force lives and works. The contribution is motivated by the financial distress experienced after the 2008 financial crisis, which has significantly hit the Italian banking system and cooperative banks in particular.

The purpose of this paper is to explore the effect of monetary policy on bank risk in the banking system in some MENA countries. It explores how some economic and credit indicators affect the level of risk in the banking sector. It combines many factors that could affect banks’ risk appetite such as macroeconomic conditions, banks’ credit size and lending growth. The authors use nonperforming loans as a proxy for banking sector risks. At first, the authors have analyzed the linear relationship between monetary policy and credit risk. As mentioned above, nonlinearity is expected in the underlying relationship, and, thus, they have investigated the nonlinear relationship to deeply analyse the relationship using the dynamic panel threshold model, as stimulated by Kremer et al. (2013). Threshold models have gained a great importance in economics and finance for modelling nonlinear behaviour. Threshold models are useful in showing the turning points in the behaviour of financial and economic indicators. This technique has been applied in this study to study the effect of monetary policy on credit risk.

This paper is divided into the following sections: Section 2 which previews the recent literature; Section 3 which includes some stylized facts about the relationship between credit risk and monetary policy; Section 4 which deals with the model and methodology; Section 5 which handles the data sources and discusses the results, and finally Section 6 which is the conclusion. The paper adopts dynamic panel threshold model of Kremer et al. (2013).

The results show that the relationship between monetary policy and credit risk is positive and significant to a certain threshold, 6.3. If the lending interest rate is higher than 6.3, this increases the credit risk in the banking sector, because increasing the lending interest rate imposes huge burdens on the borrowers, and, therefore, the bad loans and nonperforming loans become more likely. Thus, the MENA countries need to decrease the lending interest rate to be less than 6.3 to reduce the effect of monetary policy on credit risk. Further, these results are qualitatively robust regarding the inclusion of additional control variables, using alternative threshold variables and further endogeneity checks of the credit risk, such as Risk premium and the squared term of the lending interest rate. The results of taking the risk premium and the squared term of the lending interest rate as a threshold served the analysis and confirmed the positive relationship between monetary policy and credit risk above a certain threshold. As for the risk premium, the relationship below the threshold was negative and significant. Other related research points might be a good avenue for the future research such as applying this approach to micro data of banks from different MENA countries. Also, more sophisticated approaches like time-varying panel approach to assess the relationship over the time can be applied.

The importance of this paper lies in the fact that it does not only study the effect of time, but it also focuses on the panel data about some economic and credit indicators in the MENA region for the first time. This is because central banks in the MENA region have common characteristics and congruous level of economic growth. Therefore, to study how the monetary policy affects those countries’ credit risks in their lending policies, this requires careful analysis of how the central banks in this region might behave to control default risks.

Loan default risk or credit risk evaluation is important to financial institutions which provide loans to businesses and individuals. Loans carry the risk of being defaulted. To understand the risk levels of credit users (corporations and individuals), credit providers (bankers) normally collect vast amounts of information on borrowers. Statistical predictive analytic techniques can be used to analyse or to determine the risk levels involved in loans. This paper aims to address the question of default prediction of short-term loans for a Tunisian commercial bank.

The authors have used a database of 924 files of credits granted to industrial Tunisian companies by a commercial bank in the years 2003, 2004, 2005 and 2006. The naive Bayesian classifier algorithm was used, and the results show that the good classification rate is of the order of 63.85 per cent. The default probability is explained by the variables measuring working capital, leverage, solvency, profitability and cash flow indicators.

The results of the validation test show that the good classification rate is of the order of 58.66 per cent; nevertheless, the error types I and II remain relatively high at 42.42 and 40.47 per cent, respectively. A receiver operating characteristic curve is plotted to evaluate the performance of the model. The result shows that the area under the curve criterion is of the order of 69 per cent.

The paper highlights the fact that the Tunisian central bank obliged all commercial banks to conduct a survey study to collect qualitative data for better credit notation of the borrowers.

El riesgo de incumplimiento de préstamos o la evaluación del riesgo de crédito es importante para las instituciones financieras que otorgan préstamos a empresas e individuos. Existe el riesgo de que el pago de préstamos no se cumpla. Para entender los niveles de riesgo de los usuarios de crédito (corporaciones e individuos), los proveedores de crédito (banqueros) normalmente recogen gran cantidad de información sobre los prestatarios. Las técnicas analíticas predictivas estadísticas pueden utilizarse para analizar o determinar los niveles de riesgo involucrados en los préstamos. En este artículo abordamos la cuestión de la predicción por defecto de los préstamos a corto plazo para un banco comercial tunecino.

Utilizamos una base de datos de 924 archivos de créditos concedidos a empresas industriales tunecinas por un banco comercial en 2003, 2004, 2005 y 2006. El algoritmo bayesiano de clasificadores se llevó a cabo y los resultados muestran que la tasa de clasificación buena es del orden del 63.85%. La probabilidad de incumplimiento se explica por las variables que miden el capital de trabajo, el apalancamiento, la solvencia, la rentabilidad y los indicadores de flujo de efectivo.

Los resultados de la prueba de validación muestran que la buena tasa de clasificación es del orden de 58.66% ; sin embargo, los errores tipo I y II permanecen relativamente altos, siendo de 42.42% y 40.47%, respectivamente. Se traza una curva ROC para evaluar el rendimiento del modelo. El resultado muestra que el criterio de área bajo curva (AUC, por sus siglas en inglés) es del orden del 69%.

El documento destaca el hecho de que el Banco Central tunecino obligó a todas las entidades del sector llevar a cabo un estudio de encuesta para recopilar datos cualitativos para un mejor registro de crédito de los prestatarios.

Curva ROC, Evaluación de riesgos, Riesgo de incumplimiento, Sector bancario, Algoritmo clasificador bayesiano.

Artículo de investigación