Performance Evaluation Criteria of Credit Scoring Models for Commercial Lenders
| dc.contributor.author | Bakker Daniel K#1, Odundo F*2, Nyakinda J*3, Paul Samuel F#4 | |
| dc.date.accessioned | 2024-09-05T11:27:42Z | |
| dc.date.available | 2024-09-05T11:27:42Z | |
| dc.date.issued | 2019-07 | |
| dc.description.abstract | Credit scoring has been regarded as a main tool of different companies or banks during the last few decades and has been widely investigated in different areas, such as finance and accounting. Different scoring techniques are being used in areas of classification and prediction, where statistical techniques have conventionally been used. We used ACC rate, which we believed was an important criterion, especially for new applications of credit scoring, because it highlighted the accuracy of the predictions. Confirmation of these values was done by the AUROC. Different Models were examined and the result showed that using Logistic Regression approach, 19.4% of the applicants were predicted false and 80.6% of them correct, which is relatively high compared to the other models, with the highest sensitivity and the lowest Type II error. That is to say, if we were credit officers, we would conclude that the model at hand, predicts 8 out of 10, the true status of each loan candidate. | |
| dc.identifier.issn | 2231-5373 | |
| dc.identifier.uri | https://ir.ueab.ac.ke/handle/123456789/19 | |
| dc.language.iso | en | |
| dc.publisher | International Journal of Mathematics Trends and Technology (IJMTT) | |
| dc.title | Performance Evaluation Criteria of Credit Scoring Models for Commercial Lenders | |
| dc.type | Article |