Mandatory Al Red Teaming in Cloud-Based Fintech Platforms: Implications for Regulatory Compliance and Fraud Prevention

Abstract

This study examines the role of mandatory artificial intelligence red teaming in strengthening the security and regulatory compliance of cloud-based fintech platforms. A quantitative research design was employed using four open-access datasets: the IEEE Credit Card Fraud dataset, the IBM Credit Card Fraud dataset, the BankSim dataset, and the MITRE ATT&CK Enterprise dataset. The analysis integrated logistic regression vulnerability modelling, adversarial perturbation impact analysis, structural equation modelling, and a quantitative cybersecurity risk scoring model. The results indicate that key variables such as V14 significantly increase fraud probability (β = 2.031; odds ratio = 7.62), while adversarial manipulation reduced fraud detection accuracy from 0.986 to 0.863 before improving to 0.948 after red teaming mitigation. Governance oversight (β = 0.56, p < 0.001) also significantly strengthened fraud resilience. Based on these findings, the study develops and empirically operationalizes an AI Red Teaming Governance Framework consisting of four integrated components: vulnerability identification, adversarial red teaming, governance oversight, and regulatory compliance integration. The framework provides a structured approach for implementing and monitoring AI security testing within cloud-based fintech platforms. The study recommends mandatory AI red teaming regulations, continuous adversarial testing, standardized governance frameworks, and prioritization of high-risk attack vectors in fintech cybersecurity strategies.