Ethical Dimensions of Automated Bankruptcy Risk Systems: A Framework for Fairness, Transparency, and Access

Abstract

This article examines the ethical foundations of cloud-native bankruptcy risk detection systems, exploring the tension between institutional efficiency and social responsibility in financial distress contexts. The article presents a comprehensive framework for designing automated systems that reduce wrongful collections while ensuring appropriate legal actions for distressed individuals. The framework addresses critical elements including fairness in risk classification algorithms, explainability of scoring logic, intervention thresholds, and enhanced access to justice through responsible automation. Drawing on interdisciplinary perspectives from computer science, finance, law, and ethics, the article identifies design principles that promote transparency and minimize disparate impacts across demographic groups. The analysis suggests that thoughtfully designed bankruptcy prediction systems can simultaneously protect institutional integrity while supporting vulnerable populations through their financial challenges. The article concludes by advocating for sustained dialogue between technical professionals, legal experts, and financial institutions to develop standards that balance innovation with ethical considerations in this consequential domain.