Responsible AI in Action: Clustering Models for Ethical Categorization of High-Risk Users in Payment Ecosystems
Keywords:
Ethical AI, Financial Crime Mitigation, Clustering Models, High-Risk Users, Payment EcosystemsAbstract
The integration of ethical artificial intelligence (AI) in financial crime mitigation necessitates balancing algorithmic efficacy with transparency, fairness, and reg- ulatory compliance. This paper explores clustering models—including K-means, spectral clustering, and similarity learning—to categorize high-risk users in pay- ment ecosystems while addressing ethical challenges. By analyzing transaction patterns and behavioral data, these models reduce false positives by 30–50% compared to traditional rule-based systems, as demonstrated in industry case studies. Key ethical considerations include bias mitigation through fairness-aware machine learning and privacy preservation via federated learning frameworks. A Stripe case study highlights the effectiveness of XGBoost-based similarity cluster- ing, achieving a 67% reduction in fraudulent accounts by linking shared attributes like IP addresses and card details. The proposed approach emphasizes explain- able AI (XAI) techniques, such as SHAP values, to document decision-making processes for regulatory audits. Hybrid models combining spectral clustering with semi-supervised SVM (TSC-SVM) further enhance investigator validation of AI-generated alerts. These advancements underscore the importance of mul- tidisciplinary collaboration to align technical solutions with evolving anti-money laundering (AML) regulations and ethical AI standards [1].
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