A Comparative Analysis of Credit Scoring Models and Generative AI Techniques

This paper compares traditional credit scoring methods, deep learning models, and large language models (LLMs), using synthetic data to protect privacy and ensure consistency. Credit scoring has traditionally used methods like logistic regression and new AI models which may improve prediction accuracy. In this paper it was tested and evaluated these baseline methods (logistic regression), deep learning (Gradient Boosting Machine and Neural Networks), and LLM-based models for feature extraction and prediction looking at performance in areas like accuracy, precision, and recall. The results show that deep learning and LLM-based models perform better with complex data, while traditional models still work well with lower computational demands. This paper provides valuable insights into balancing accuracy, interpretability, and computational efficiency when developing credit scoring models.