Term of Award
Summer 2025
Degree Name
Master of Science in Mathematics (M.S.)
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Department of Mathematical Sciences
Committee Chair
Ionut Emil Iacob
Committee Member 1
Divine Wanduku
Committee Member 2
Zheni Utic
Abstract
Credit risk prediction remains both a challenging and high-interest problem due to the inherently unbalanced nature of financial datasets and the continuous drive for higher pre- dictive precision. In this work, I build upon previous advancements in credit risk modeling and introduce an ensemble-based Artificial Neural Network (ANN) architecture designed to enhance classification performance. By leveraging a selective ensemble of decision net- works, this approach not only improves prediction accuracy but also mitigates the chal- lenges posed by imbalanced data distributions. While the primary focus is on credit risk prediction, my analysis demonstrates that the proposed model can be effectively applied for both dimensionality reduction and classification of unbalanced datasets more broadly. The results reinforce the potential of ensemble deep learning strategies in financial risk assessment, offering a scalable and precise solution for real-world credit risk evaluation.
Recommended Citation
Dey, Vincent, "Majority Decision Using Top-Performing Neural Networks Models for Improved Credit Risk Prediction" (2025). Electronic Theses and Dissertations. 2979.
https://digitalcommons.georgiasouthern.edu/etd/2979
Research Data and Supplementary Material
Yes
