Ethical and Privacy Considerations in Automated Fraud Detection Systems

Abstract

This article examines the balance between technological innovation and ethical considerations in automated fraud detection systems within banking and financial services. As institutions increasingly deploy AI-driven solutions to identify fraudulent activities, significant questions arise regarding data privacy, algorithmic transparency, and potential discrimination. The article addresses technical challenges in legacy systems, including secure deletion complexities, data lineage tracking, and classification inconsistencies that hinder governance. It explores explainability approaches such as SHAP, LIME, and counterfactual explanations that illuminate complex model decisions for various stakeholders. The discussion extends to privacy-enhancing technologies—differential privacy, homomorphic encryption, secure multi-party computation, and federated learning—as mechanisms to reconcile security with privacy. By evaluating regulatory frameworks, governance structures, and ethical design principles, the article advocates for a balanced approach incorporating transparent system design and appropriate oversight, building trustworthy systems that protect consumers while respecting fundamental privacy rights.