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This paper aims to investigate whether loan officers' risk taking in credit decisions are associated with their personal financial risk preference and personality traits or solely with bank-contextual and loan-relevant factors.

An online survey administered in six large Swedish banks to 163 loan officers responsible for assessing credit risk and approval of loan applications. The loan officers rated their likelihood of approving fictitious loan applications from business companies.

The loan officers' credit risk taking is associated with bank-contextual factors, directly with perceived organizational credit risk norms and indirectly with self-confidence in assessing credit risks through attitude to credit risk taking. A direct association is also found with personal financial risk preference but not with personality traits.

Increased awareness of that loan officers' personal financial risk preference is associated with their credit risk taking in loan decisions but that the banks' risk policy has a stronger association. Banks' managements and boards should therefore assure that their credit risk policy is implemented, followed and being aligned with their performance incentives.

Increased awareness of that loan officers' credit risk taking is associated with personal financial risk preference but more strongly with the banks' risk policy that motivate banks' managements and boards to assure that their credit risk policy is implemented, followed and being aligned with their performance incentives.

The first study which directly compare the associations of loan officers' risk taking in credit approvals with personal risk preference and personality traits versus bank-contextual factors and loan-relevant information.

This paper aims to analyse trends and determinants of NPAs in India's banks. It has empirically examined the bank-specific determinants of NPAs.

An FE panel estimation of a sample of 44 banks was carried out for the post-crisis time period, from 2010 to 2020 to identify the bank-specific determinants of NPAs. The sample of 44 banks includes 20 PSBs, 19 private banks and 5 foreign banks. Separate FE estimation was also carried out to identify the drivers of NPAs in PSBs.

The determinant of NPAs during the post-crisis period suggests that faulty earning management and deterioration in loan quality have resulted in high NPAs in India's banks. The result is similar for PSBs as well.

The findings of the study suggest that the banks, especially the Public Sector Banks (PSBs) need to revisit their earning management strategies to maximise income and improve their loan quality in order to reduce the incidence of loan failure.

The paper contributes by empirically analysing the determinants of NPAs during the recent decade, between 2010 and 2020. Separate estimations have been carried out to understand whether the drivers of NPAs differ in the case of PSBs.

Since introducing the UK start-up loan (SUL) Scheme in 2012, 82,809 new start-ups have been supported with loans totalling £759m. Even during the Covid-19 crisis, new business start-ups supported by SUL did not abate. The authors ask whether the entrepreneurs starting businesses during the Covid-19 crisis were different from those becoming entrepreneurs before the pandemic. This paper aims to discuss the aforementioned question.

The authors model the differences between pre-Covid-19 business start-ups and Covid-19 start-ups. The administrative data obtained from the UK Government Department for Business, Energy and Industrial Strategy (BEIS) represent information about individual loan records for 82,798 individuals and total lending of £759m between 2012 and 2021. The probit regression model with dependent variable coded one if the start occurred after February 2020 and zero between 2012 and February 2020, was estimated.

The study’s findings show that both groups of entrepreneurs differ in many facets. The new Covid-19 entrepreneurs are older, more likely to have a graduate-level education and are significantly more likely to make this transition from full-time waged employment or inactivity. Furthermore, they are more likely to set up in manufacturing industries at the business level than their pre-Covid-19 counterparts who favoured service sectors. Finally, their initial lending to support the start-up is much higher.

This study provides value for the policymakers responsible for the administration of the SUL scheme, and it also contributes to the body of knowledge on the effects of the global Covid-19 pandemic.

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.

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

The primary objective of this paper is to identify and compare the collateral imposition practices among Islamic banks in Indonesia to serve micro, small and medium-sized enterprise (MSME) clients and explore the experiences and perceptions of MSME entrepreneurs pertaining to collateralisation in MSME financing.

This study was carried out by implementing a case study research strategy. The data was gathered primarily through the interview by utilising purposive uncontrolled quota sampling. The interview was conducted using semi-structured interview questions by targeting the two sides of Islamic financial inclusion: the Islamic banking industry (supply-side) and the MSME segment (demand-side).

This paper implies that the collateral provision is indeed an obligatory requirement for MSME to access regular financing in an Islamic bank, preferably the immovable type that consists of land and property. Subsequently, although the Islamic banks offer non-collateralised financing, their disbursement is still relatively scant and limited. Furthermore, despite the collateral issues, most MSME entrepreneurs positively perceive the bank’s collateralisation practice, indicating their awareness and understanding of the collateral purpose and function to access the financing facility.

This paper merely observed six Islamic bank institutions and 22 MSME units in urban and rural areas in Indonesia using a case study approach. Therefore, the empirical findings and case discussions were limited to those around the corresponding Islamic banks and MSME participants.

By referring to the several disclosed issues associated with the collateral imposition practices, this paper presents several recommendations that might be considered by the policymakers and the Islamic banking industry to enhance the realisation of MSME Islamic financial inclusion from the collateral implementation aspect, and thereby, facilitating more inclusive growth for the MSME industry.

This paper is unique since the paper attempts to analyse and compare the collateral imposition practices and its perception from the two distinct sides of Islamic financial inclusion that were represented by Islamic banks and MSMEs in Indonesia by including different types of Islamic banks and different segments of MSME in their diverse business sector within the urban and rural locations.

The purpose of this paper is to highlight the opportunity for the energy policy in Brazil to tackle the very high cost-effectiveness potencial of solar energy to the power system. Three mechanisms to achieve ambitious reductions in the greenhouse gas emissions from the power sector by 2030 and 2040 are assessed wherein treated as solar targets under ambitious reductions in the greenhouse gas emissions from the power sector. Then, three mechanisms to achieve these selected solar targets are suggested.

This paper reviews current and future incentive mechanisms to promote solar energy. An integrated energy system optimization model shows the most cost-efficient deployment level. Incentive mechanisms can promote renewable sources, aiming to tackle climate change and ensuring energy security, while taking advantage of endogenous energy resources potential. Based on a literature review, as well as on the specific characteristics of the Brazilian power system, under restrictions for the expansion of hydroelectricity and ambitious limitation in the emissions of greenhouse gases from the power sector.

The potential unexploited of solar energy is huge but it needs the appropriate incentive mechanism to be deployed. These mechanisms would be more effective if they have a specific technological and temporal focus. The solar energy deployment in large scale is important to the mitigation of climate change.

The value of the research is twofold: estimations of the cost-effective potential of solar technologies, generated from an integrated optimization energy model, fully calibrated for the Brazilian power system, while tacking the increasing electricity demand, the expected reduction of greenhouse gas emissions and the need to increase the access to clean and affordable energy, up to 2040; proposals of three mechanisms to deploy centralized PV, distributed PV and solar thermal power, taking the best experiences in several countries and the recent Brazilian cases.

This paper reviews the literature on zombie firms to provide a holistic view by delineating their formation, impact, widespread nature, prevention and policy implications.

This paper uses a systematic literature review methodology, in which 76 papers published in journals ranked on the Australian Business Deans Council (ABDC) 2022 list were reviewed. The study period was from 2000 to 2022.

Among the main findings, the widespread problems of zombie firms were evident. The authors found that consistent support, either in the form of government grants or a weak financial framework, was responsible for their formation. The suboptimal performance of factors of production, depressed job creation, low innovation and overall negative impact on economic activity are the consequences of zombification. This can be controlled by ensuring better bankruptcy codes, focused on government assistance, technology use and better due diligence by banks.

This review serves as a reference point for future researchers as a cohesive and holistic study presenting a full picture of the problem, so that the proposed solutions are robust and tenable.

This review is among the initial attempts to comprehensively study published work on zombie firms in terms of analyzing their region-specific nature, with an emphasis on definition, causes, impact and prevention.

This paper aims to present a literature review of the most recent optimisation methods applied to Credit Scoring Models (CSMs).

The research methodology employed technical procedures based on bibliographic and exploratory analyses. A traditional investigation was carried out using the Scopus, ScienceDirect and Web of Science databases. The papers selection and classification took place in three steps considering only studies in English language and published in electronic journals (from 2008 to 2022). The investigation led up to the selection of 46 publications (10 presenting literature reviews and 36 proposing CSMs).

The findings showed that CSMs are usually formulated using Financial Analysis, Machine Learning, Statistical Techniques, Operational Research and Data Mining Algorithms. The main databases used by the researchers were banks and the University of California, Irvine. The analyses identified 48 methods used by CSMs, the main ones being: Logistic Regression (13%), Naive Bayes (10%) and Artificial Neural Networks (7%). The authors conclude that advances in credit score studies will require new hybrid approaches capable of integrating Big Data and Deep Learning algorithms into CSMs. These algorithms should have practical issues considered consider practical issues for improving the level of adaptation and performance demanded for the CSMs.

The results of this study might provide considerable practical implications for the application of CSMs. As it was aimed to demonstrate the application of optimisation methods, it is highly considerable that legal and ethical issues should be better adapted to CSMs. It is also suggested improvement of studies focused on micro and small companies for sales in instalment plans and commercial credit through the improvement or new CSMs.

The economic reality surrounding credit granting has made risk management a complex decision-making issue increasingly supported by CSMs. Therefore, this paper satisfies an important gap in the literature to present an analysis of recent advances in optimisation methods applied to CSMs. The main contribution of this paper consists of presenting the evolution of the state of the art and future trends in studies aimed at proposing better CSMs.

This paper makes a novel attempt to estimate the potential impact of credit risk on foreign direct investment (FDI hereafter), thereby focusing on a completely unexplored area in the existing empirical literature.

To provide a comprehensive understanding of the relationship between credit risk and FDI inflows, the study incorporates all the eight-member economies of the South Asian Association of Regional Cooperation (SAARC hereafter) and analyzes a panel data set, over the period 2011 to 2019, extracted from the World Development Indicators, using the suitable econometric techniques for the efficient estimations of the specified models.

The results indicate a negative and statistically significant relationship between the credit risk of the banking sectors and FDI inflows. Similarly, market size and inflation rate appear to be the two other main factors behind the increasing FDI inflows in the SAARC member economies. Interestingly, the size of the market became irrelevant in attracting FDI inflows when the Indian economy is excluded from the sample due to its higher economic weight. On the other hand, FDI inflows are not dependent on the level of trade openness, with most of the specifications showing either an insignificant or negative coefficient of the variable.

The obtained results are unique and robust to alternative methodologies, and hence, the SAARC economies could consider them as the critical inputs in formulating the appropriate policies on FDI inflows.

The findings are unique and original. The authors have established a relationship between credit risk and FDI for the first time in the SAARC context.