Explainable AI (XAI) in Cloud-Native Financial Services: Building Trust and Transparency in Modernized Decision Engines

Abstract

The rapid migration of financial services to cloud infrastructure has fundamentally transformed the industry's relationship with artificial intelligence, creating unprecedented challenges for transparency and explainability. As sophisticated AI models increasingly drive critical financial decisions, their inherent complexity within distributed cloud environments introduces significant opacity risks that impact regulatory compliance, stakeholder trust, and business performance. Financial institutions face mounting pressure from evolving regulatory frameworks that demand clear explanations for automated decisions affecting consumers, while simultaneously navigating technical hurdles inherent to cloud-native deployments. The transparency imperative extends beyond compliance concerns to directly affect customer retention, brand trust, and operational efficiency. Explainable AI (XAI) emerges as a crucial capability for addressing these challenges, enabling financial organizations to provide meaningful insights into model behavior while maintaining performance. By implementing specialized explainability techniques adapted for cloud environments, institutions can satisfy regulatory requirements, enhance customer experience, streamline governance processes, and improve model performance. The convergence of cloud computing and financial AI necessitates a strategic focus on transparency to ensure responsible innovation that balances technological advancement with accountability and trust.