Credit scoring and decision making in Egyptian public sector banks

Purpose – The main aims of this paper are: first, to investigate how decisions are currently made within the Egyptian public sector environment; and, second, to determine whether the decision making can be significantly improved through the use of credit scoring models. A subsidiary aim is to analyze the impact of different proportions of sub-samples of accepted credit applicants on both efficient decision making and the optimal choice of credit scoring techniques. Design/methodology/approach – Following an investigative phase to identify relevant variables in the sector, the research proceeds to an evaluative phase, in which an analysis is undertaken of real data sets (comprising 1,262 applicants), provided by the commercial public sector banks in Egypt. Two types of neural nets are used, and correspondingly two types of conventional techniques are applied. The use of two evaluative measures/criteria: average correct classification (ACC) rate and estimated misclassification cost (EMC) under different misclassification cost (MC) ratios are investigated. Findings – The currently used approach is based on personal judgement. Statistical scoring techniques are shown to provide more efficient classification results than the currently used judgemental techniques. Furthermore, neural net models give better ACC rates, but the optimal choice of techniques depends on the MC ratio. The probabilistic neural net (PNN) is preferred for a lower cost ratio, whilst the multiple discriminant analysis (MDA) is the preferred choice for a higher ratio. Thus, there is a role for MDA as well as neural nets. There is evidence of statistically significant differences between advanced scoring models and conventional models. Research limitations/implications – Future research could investigate the use of further evaluative measures, such as the area under the ROC curve and GINI coefficient techniques and more statistical techniques, such as genetic and fuzzy programming. The plan is to enlarge the data set. Practical implications – There is a huge financial benefit from applying these scoring models to Egyptian public sector banks, for at present only judgemental techniques are being applied in credit evaluation processes. Hence, these techniques can be introduced to support the bank credit decision makers. Originality/value – Thie paper reveals a set of key variables culturally relevant to the Egyptian environment, and provides an evaluation of personal loans in the Egyptian public sector banking environment, in which (to the best of the author's knowledge) no other authors have studied the use of sophisticated statistical credit scoring techniques

Inventory management.

A critical aspect of blood transfusion is the timely provision of high quality blood products. This task remains a significant challenge for many blood services and blood systems reflecting the difficulty of balancing the recruitment of sufficient donors, the optimal utilization of the donor's gift, the increasing safety related restrictions on blood donation, a growing menu of specialized blood products and an ever-growing imperative to increase the efficiency of blood product provision from a cost perspective. As our industry now faces questions about our standard practices including whether or not the age of blood has a negative impact on recipients, it is timely to take a look at our collective inventory management practices. This International Forum represents an effort to get a snap shot of inventory management practices around the world, and to understand the range of different products provided for patients. In addition to sharing current inventory management practices, this Forum is intended to foster an exchange of ideas around where we see our field moving with respect to various issues including specialty products, new technologies, and reducing recipient risk from blood transfusion products

Inventory management

A critical aspect of blood transfusion is the timely provision of high quality blood products. This task remains a significant challenge for many blood services and blood systems reflecting the difficulty of balancing the recruitment of sufficient donors, the optimal utilization of the donor's gift, the increasing safety related restrictions on blood donation, a growing menu of specialized blood products and an ever-growing imperative to increase the efficiency of blood product provision from a cost perspective. As our industry now faces questions about our standard practices including whether or not the age of blood has a negative impact on recipients, it is timely to take a look at our collective inventory management practices. This International Forum represents an effort to get a snap shot of inventory management practices around the world, and to understand the range of different products provided for patients. In addition to sharing current inventory management practices, this Forum is intended to foster an exchange of ideas around where we see our field moving with respect to various issues including specialty products, new technologies, and reducing recipient risk from blood transfusion product

Determinants of Default in P2P Lending

This paper studies P2P lending and the factors explaining loan default. This is an important issue because in P2P lending individual investors bear the credit risk, instead of financial institutions, which are experts in dealing with this risk. P2P lenders suffer a severe problem of information asymmetry, because they are at a disadvantage facing the borrower. For this reason, P2P lending sites provide potential lenders with information about borrowers and their loan purpose. They also assign a grade to each loan. The empirical study is based on loans' data collected from Lending Club (N = 24,449) from 2008 to 2014 that are first analyzed by using univariate means tests and survival analysis. Factors explaining default are loan purpose, annual income, current housing situation, credit history and indebtedness. Secondly, a logistic regression model is developed to predict defaults. The grade assigned by the P2P lending site is the most predictive factor of default, but the accuracy of the model is improved by adding other information, especially the borrower's debt level