Year: 2025
Type: article
Abstract: Credit risk modelling is essential for predicting a borrower’s likelihood of default and ensuring financial stability. Traditional models like Logistic Regression often struggle with complex, non-linear financial data. This paper presents a Machine Learning–based approach using Deep Neural Networks (DNNs) to enhance prediction accuracy and adaptability. The proposed model is trained on publicly available credit datasets after preprocessing and feature engineering. Experimental results show an accuracy of ~85% and an AUC of ~90%, outperforming classical models by a significant margin. The study demonstrates that deep learning can effectively capture hidden patterns in borrower behaviour, offering a robust, scalable, and data-driven framework for modern credit risk assessment.
Authors: Vaidehi Patil, Shubham Bari, Kairav Rajgariah, Sumit Singh
Institution: Thakur College of Engineering and Technology
Language: English
Subfield: Accounting
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W4416248205