Explainable AI in Regulatory Compliance: Balancing Transparency and Performance in AI Driven Treasury Management
Ardhendu Sekhar Nanda
*
First Citizens Bank, Morristown, NJ, USA.
*Author to whom correspondence should be addressed.
Abstract
The integration of Artificial Intelligence (AI) in financial operations has transformed treasury management by enhancing efficiency, risk assessment, and compliance processes. However, the increasing use of AI in regulatory compliance introduces a critical challenge: balancing transparency and performance. Explainable AI (XAI) has emerged as a solution to enhance interpretability, accountability, and trust in AI-driven decision-making. This article explores the role of XAI in regulatory compliance for treasury operations, addressing key challenges and trade-offs between explainability and performance. It evaluates existing frameworks, regulatory expectations, and industry best practices for implementing XAI in financial institutions. Additionally, this study highlights the impact of explainability on AI model efficiency and proposes strategies to optimize performance without compromising compliance. The findings provide valuable insights for financial professionals, regulators, and AI developers seeking to navigate the evolving landscape of AI-driven treasury management.
Keywords: Explainable AI, regulatory compliance, treasury operations, transparency, financial risk, AI performance, accountability, financial regulations, algorithmic decision-making