Purpose This paper is an empirical study of the effect of the characteristics of the Sharia supervisory board (SSB) on the financial performance of Islamic banks. Design/methodology/approach Using 42 Middle East and North Africa (MENA) Islamic banks outside the Gulf Cooperation Council (GCC) and non-Islamic countries during the 2011/2018 period, a random-effects generalized lease square method for the regression analyzes is applied. Findings The obtained results show that the characteristics of the SSB affect the financial performance of Islamic banks. The results also affirm that a large-sized board of directors and the number of SSB meetings improve banking performance while the cross-mandate seems to destroy it. On the other hand, the SSB members’ competence and reputation and the proportion of women sitting in SSB have no impact on the financial performance of Islamic banks. Research limitations/implications This paper gives a comprehensive literature survey on the effect of the characteristics of the SSB on the financial performance of Islamic banks. Practical implications This study offers insights into the practitioner and Islamic banking regulators interested in enhancing the legitimacy of corporate governance in Islamic financial institutions. Originality/value This paper is among the few studies that investigate the effect of the characteristics of SSB on the financial performance of Islamic banks in particular in Islamic banks in the MENA region outside the GCC and in non-Islamic countries.
Repayment of loans in microfinance is a very important subject for study. Microfinance institutions lend to poor and low-income borrowers and thus the terms of lending should be as easy as possible for more poor people to have access. On the other hand, poor and low-income borrowers tend to have no collateral and thus would constitute a considerable risk to lenders once they default. Lenders, therefore, have to devise a special system whereby to ensure that loan defaults are as low as possible in order to avoid charging a higher interest rate which will defeat the very purpose of microfinance-lending. In this paper, using a binary logistic regression model, we undertake to examine the factors that affect default among borrowers. It has been discovered that borrowers' socio-demographic characteristics, past participation in microcredit loans and past credit history have significant impacts, as special features, on their default rates.
Microfinance institutions' (MFIs') peculiar lending methodology is characterized by an unchallenged decisionmaking predominance from the part of loan officers. Indeed, the latter are in charge of providing a great deal of diagnostic information regarding the entrepreneur's psychological traits likely to help them run a business. This paper constitutes an initial attempt towards exploring the role of borrowers' psychological traits in predicting future default occurrences. It builds on a nonparametric credit scoring model, based on a decision tree, including borrowers' quantitative behavioural traits as input for the final scoring model. On applying data collected from a Tunisian microfinance bank, the major depicted result lies in the fact that borrowers' psychological traits constitute a major information source in predicting their creditworthiness. Actually, the variables deployed have helped reduce the proportion of bad loans classified as good loans by 3.125%, which leads to a decrease in MFIs' losses by 4.8%. In addition, the results indicate that the scoring model based on a classification and regression tree (CART) outperforms the classic techniques. Actually, implementing this CART model might well help MFIs reduce misclassification costs by 6.8% and 13.5% in comparison with the discriminant analysis and logistic regression models respectively. Our conceived model, we consider, would be of great practical implication for microfinance and may provide a means for securing competitive advantage over other MFIs that fail to implement such a methodology.1 These quantitative criteria are gathered from the loan application form. Usually, the loan officers collect and verify the personal identification of a borrower (identity card), their educational level and their professional experience. 2 Sociologists define social capital as the social networks, norms, and cooperation and trust created by human interactions in a community (e.g. see Putnam (1995)). 3 Basically, quantitative information is objective; qualitative is subjective. b AUC(1) and AUC(2) are statistically significant at the threshold of 1%.
The peculiarity in lending methodology in Microfinance institution characterized by unchallenged dominance of the loan officers in the decision-making prompted us to investigate the predictive accuracy of their subjective judgement. In addition, we investigate if the accuracy of this information depends on the strength of lenders-borrowers relationship.The objective of this paper is to understand the loan officer behaviour in default prediction task. Using credit file data from Tunisian Microfinance bank, we have found evidence that subjective judgment of loan officers has statistically significant predictive capability of default risk. In addition, our results suggest that novice loan officers have incorrect prediction of future default events because they are prone to different behavioral biases. Novice loan officers may overweight any information that is consistent with their existing beliefs and overestimate the precision of their own private information. However, through learning experienced loan officers have less behavior bias and more accurate feeling allowing them to make an accurate default prediction. To make correct decision in relationship lending approach, credit committee can rely on subjective judgment of experienced loan officers mainly when the latter judged the creditworthiness of frequent borrowers. As soft information is not easily and accurately transferable, Microfinance institutions have more interest to retain their experienced loan officers than their borrowers.
Purpose – The present paper’s aim lies in providing an empirical analysis of whether the loan officers’ psychological traits display an explanation of their subjective prediction accuracy. Design/methodology/approach – A qualitative and qualitative analysis has also been applied. Findings – The reached results reveal that, with respect to microfinance institutions, the loan officers’ accurate subjective judgment crucially relies on the principle of learning-through-experience so as to construct a special type of relevant skills and competences. Learning is both an intellectual and an emotional process, whereby loan officers acquire certain specific experience likely to enhance their cognitive skills and shape their emotional intelligence, which would, in turn, sharpen their forecasting accuracy. In fact, the higher emotional intelligence is, the easier it makes it for loan officer to adjust or reduce their judgmental errors and make a more effective application of the pertinent heuristics. Conversely, however, the lack or absence of emotions and feelings of novice loan officer is likely to hinder and inhibit the cognitive as well as the learning processes. Originality/value – The paper considers the role of individual psychological traits on the decisions of experienced and inexperienced individuals when deciding on the default risk in the context of loan decisions. Learning is both an intellectual and an emotional process, whereby loan officers acquire certain specific experience likely to enhance their cognitive skills and shape their emotional intelligence, which would, in turn, sharpen their forecasting accuracy.
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