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Vol. 5 No. 1 (2024)
2024 Edition

Articles

Algorithmic Bias Identification and Mitigation Strategies in Machine Learning-Based Credit Risk Assessment for Small and Medium Enterprises

Published 2024-09-30

  • Wenbo Liu
    • ,
  • Kun Qian
    • ,
  • Shuwen Zhou

Wenbo Liu
Software Engineering, Northeastern University, MA, USA

Kun Qian
Business Intelligence, Engineering School of Information and Digital Technologies, Villejuif, France

Shuwen Zhou
Computer Science, The University of New South Wales, Sydney, Australia

Abstract

This research investigates algorithmic bias issues within machine learning-based credit risk assessment systems specifically targeting small and medium enterprises (SMEs). The study addresses the critical challenge of unfair lending practices that disproportionately affect SMEs due to biased algorithmic decision-making processes. Through comprehensive analysis of bias manifestations and systematic evaluation of mitigation strategies, this work proposes a framework for identifying and reducing discriminatory patterns in automated credit scoring systems. The research methodology combines statistical bias detection techniques with advanced fairness optimization algorithms, including reweighting approaches and multi-objective optimization frameworks. Experimental results demonstrate significant improvements in fairness metrics while maintaining competitive predictive accuracy. The proposed bias mitigation strategies show effectiveness in reducing disparate impact across different SME categories, with particular success in addressing geographic and sector-based discrimination. This study contributes to the development of more equitable financial technology solutions that enhance SME access to credit while maintaining robust risk assessment capabilities. The findings provide practical guidance for financial institutions and regulatory bodies seeking to implement fair lending practices in automated decision-making systems.

Keywords

Algorithmic Bias, Credit Risk Assessment, Small Medium Enterprises, Machine Learning Fairness

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