Thesis Abstract

This research aims to analyze the effectiveness of credit scoring models in predicting loan default. Credit scoring models play a crucial role in the lending industry by assessing the creditworthiness of borrowers and predicting the likelihood of loan default. However, the accuracy and reliability of these models have been a subject of debate. This study will evaluate different credit scoring models, such as logistic regression, decision trees, and neural networks, and compare their predictive performance in identifying loan default. It will also examine the factors that influence the accuracy of credit scoring models and propose recommendations for improving their effectiveness. The findings of this research will provide valuable insights for lenders in enhancing their credit risk assessment processes

Thesis Overview