Mitigating Algorithmic Bias in Credit Scoring: A CNN-SMOTE Framework
Le Duy Quang
Hanoi - Amsterdam High School for the Gifted, Vietnam.
Nguyen Quang Dat *
VNU University of Science, Vietnam.
Ngo Dai Phong
Le Quy Don Technical University, Vietnam.
Doan Tien Ban
Le Quy Don Technical University, Vietnam.
*Author to whom correspondence should be addressed.
Abstract
Algorithmic bias in artificial intelligence (AI) systems has raised significant ethical concerns, particularly in critical applications such as credit scoring, where fairness and accuracy are paramount. This study proposes a novel framework that integrates Convolutional Neural Networks (CNN) with the Synthetic Minority Oversampling Technique (SMOTE) to address data imbalance and mitigate algorithmic bias. The approach leverages CNN's ability to capture complex nonlinear relationships within structured credit data while employing SMOTE to generate synthetic samples for underrepresented classes, ensuring a balanced training dataset.
By incorporating fairness-aware metrics and optimization strategies, the proposed framework not only improves predictive accuracy but also promotes equitable decision-making. Experimental evaluations on real-world credit scoring datasets demonstrate that this hybrid method outperforms traditional models, achieving higher classification performance while reducing disparities across demographic groups. This research highlights the potential of combining deep learning and oversampling techniques to build fairer and more transparent AI systems, paving the way for ethical advancements in financial decision-making.
Keywords: Algorithmic bias, fairness, random forest, Convolutional Neural Networks (CNN), Synthetic Minority Oversampling Technique (SMOTE)