Thesis (Ph.D.) - Cairo University - Faculty of Commerce - Department of Business Administration

Purpose{u2013} This thesis introduces Data Envelopment Analysis (DEA) approach to generate credit scoring (CS). Compared with other models like multiple discriminant analysis and regression analysis to predict firms{u2019} bankruptcy or being in default, which need prior information. This proposed model will help decision makers in banks and other nonbanking financial institution to differentiate between bad and good customers and predict business failure, which will help in taking appropriate credit decision through creating an assessment that can be used to determine a credit scoring via DEA, which will help managers support their lending process. Design/Methodology/Approach{u2013}This methodology merely needs ex-post in-formation to calculate borrower{u2019}s credit score. Based on the empirical evidence, this methodology has been applied on a sample on 219 small and medium enterprises firms (SMEs) versus 219 large Corporate firms (LCs) operating in the Egyptian Market, which includes the credit portfolio of one of the largest public Egyptian banks.This approach could synthesize a firm{u2019}s overall financial performance through converting a number of financial ratios into a single credibility score.This thesis pioneered on credit scoring generated from DEA as the dependent variable, when it comes to independent Variables, financial ratios measured through five common di-mensions, as recommended by the literature review, short term solvency (STV), Long term Solvency (LTV), profitability ratio (PR), operation ratio (OR), and development capacity ratio (DCR), the selected set of five main dimensions is classified as inputs and outputs ratios for DEA. The minimized inputs are long-term solvency while the maximized are short-term solvency, profitability, operation, and de-velopment capacity ratios

Issued also as CD