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Research Article Open access CC BY 4.0

Mitigating Artificial Intelligence Bias in Financial Systems: A Comparative Analysis of Debiasing Techniques

Oluwatofunnmi O. Oguntibeju

Asian Journal of Research in Computer Science · pp. 165–178 · Published 19 Dec 2024

10.9734/ajrcos/2024/v17i12536

Abstract

Balancing fairness and predictive accuracy remains a key challenge in AI system development. This study investigates the origins of AI bias, how it happens in business processes, and the challenges it poses to ethical and transparent decision-making. Drawing on existing literature, the research explores the various types of biases—including cognitive, algorithmic, and representation biases—and their impact on AI systems in the BFSI sector. Furthermore, the study critically evaluates current debiasing techniques, such as pre-processing, fairness-aware models, and post-processing, highlighting their limitations in balancing fairness with predictive accuracy. This study aims to advance the development of more equitable AI systems in the BFSI sector by proposing the FAIR-BIAS Framework. This framework provides a structured approach to detecting, mitigating, and monitoring biases in AI models. Key recommendations include implementing equalized odds as a fairness metric to ensure balanced outcomes across demographic groups, applying adversarial debiasing techniques during model training to minimize discriminatory effects, and conducting regular data audits to ensure long-term fairness. The findings offer direct benefits for BFSI stakeholders. Businesses can enhance the reliability and ethical integrity of AI models by adopting fairness-aware risk assessments, which promote compliance and customer trust. Regulators can enforce accountability by mandating transparency measures, such as model explainability, and conducting periodic audits using fairness metrics like equalized odds. Policymakers can use the insights to create inclusive legislation which requires fairness testing and transparency in AI applications. Future research could explore the long-term effectiveness of debiasing techniques across different industries, such as healthcare or public policy, by conducting longitudinal studies to assess how evolving datasets and models influence fairness outcomes. It is critical for BFSI organizations to adopt these frameworks and techniques to foster a more inclusive and ethical future in financial services.”

Bias in financial services AI bias algorithmic fairness debiasing techniques ethical AI AI transparency

References (5)

  1. 1 Algorithmic bias in machine learning-based marketing models [DOI]
  2. 2 Algorithmic equity in the hiring of underrepresented IT job candidates [DOI]
  3. 3 Enhancing audit accuracy: The role of AI in detecting financial anomalies and fraud [DOI]
  4. 4 The impact of overconfidence and herding bias on stock investment decisions mediated by risk perception [DOI]
  5. 5 Understanding Unfairness in Fraud Detection through Model and Data Bias Interactions [DOI]

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