Fairness in Financial AI: Evaluating Bias Mitigation Strategies for Credit Scoring Models

Authors

  • KANALA ANKITHA, CHENNAMSETTI VENKATESH, YADAVALLI RANGARAJU, GARIKINADURGAPRASAD, MR. M. KARTHIK Author

DOI:

https://doi.org/10.48047/tgrec170

Keywords:

Fair Credit Scoring, Algorithmic Decision Making, Bias Mitigation, Machine Learning, German Credit Dataset, Fairness in AI, Reweighing, Adversarial Debiasing, Meta Classifier, Disparate Impact, Ethical AI, Financial Decision Systems

Abstract

Credit scoring plays a pivotal role in financial decision-making, yet it often inherits and amplifies societal biases due to historical data disparities. As machine learning becomes increasingly embedded in these systems, the need for fairness-aware models has never been more urgent. This paper presents a comprehensive empirical study

Downloads

Download data is not yet available.

Downloads

Published

20.03.2025

Issue

Vol. 15 No. 3 (2025): volume 15 issue 3(2025)

Section

Articles

License

You are free to:

  1. Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
  2. Adapt — remix, transform, and build upon the material for any purpose, even commercially.
  3. The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

  1. Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  2. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Notices:

You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .

No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.

How to Cite

Fairness in Financial AI: Evaluating Bias Mitigation Strategies for Credit Scoring Models . (2025). International Journal of Information and Electronics Engineering, 15(3), 63-68. https://doi.org/10.48047/tgrec170