Statistical Analysis for Credit Scoring based on Logistic regression model | ||||
| التجارة والتمويل | ||||
| Volume 44, Issue 1, March 2024, Page 345-359 PDF (983.32 K) | ||||
| DOI: 10.21608/caf.2024.351751 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Mona Emad El-Din Mohamed1; Mervat El-Gohary2; Ahmed Amin El-Sheikh3 | ||||
| 1a Faculty of commerce- Al-Azhar University (Girls Campus), Egypt | ||||
| 2Professor of Statistics Faculty of commerce- Al-Azhar University (Girls Campus), Egypt. | ||||
| 3Professor of Applied Statistics and Econometrics- Faculty of Graduate Studies for Statistical Research Cairo University. Egypt | ||||
| Abstract | ||||
| A large number of classification techniques for credit scoring can be found in literature. Among These techniques statistical models which mainly comprise logistic regression techniques, linear discriminant analysis, k-nearest neighbor and classification tree. In the study, 614 random loan applications for clients made of a bank branch were examined. In this paper, Logistic Regression Analysis” was conducted to determine the problem and related factors and to predict the credibility according to these factors. In the model, customer age, education status, marital status, gender, profession, income, debt income ratio, credit card debt, other debts and multiplication product are taken as independent variables. As a result of the study, the bank branch will benefit from the statistical model in which it is created, to evaluate according to the customer characteristics in its portfolio, and to give more credit to branch customers. | ||||
| Keywords | ||||
| : Credit Scoring; logistic regression (LR); loan prediction | ||||
|
Statistics Article View: 23 PDF Download: 44 |
||||
