A Comparative Analysis of Credit Scoring Models and Generative AI Techniques

Bozagiu Andreea-Mădălina (), Mihai Georgian-Dănuț (), Neacşu Andrei Costin () and Neacşu George Alexandru ()
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Bozagiu Andreea-Mădălina: Bucharest University of Economic Studies, Bucharest, Romania
Mihai Georgian-Dănuț: Bucharest University of Economic Studies, Bucharest, Romania
Neacşu Andrei Costin: Bucharest University of Economic Studies, Bucharest, Romania
Neacşu George Alexandru: Bucharest University of Economic Studies, Bucharest, Romania

Proceedings of the International Conference on Business Excellence, 2025, vol. 19, issue 1, 1235-1247

Abstract: 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.

Keywords: Modelling Credit Risk; Credit Scoring; Machine Learning; Generative AI (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:poicbe:v:19:y:2025:i:1:p:1235-1247:n:1016

DOI: 10.2478/picbe-2025-0098

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