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AI for Proactive Compliance Monitoring in Banking Operations

Publié
Serveur de preprints
Preprints.org
DOI
10.20944/preprints202507.1438.v1

The financial sector faces an era where compliance is not merely a regulatory checkbox but a critical operational necessity. Traditional compliance frameworks, reliant on reactive audits and manual oversight, often fall short in the fast-paced digital banking ecosystem. This paper explores how Artificial Intelligence (AI) is transforming compliance monitoring from a passive, after-the-fact process into a proactive, real-time safeguard. AI-driven systems leverage machine learning, natural language processing, and predictive analytics to detect anomalies, anticipate regulatory breaches, and automate reporting with unmatched accuracy and speed. Unlike conventional methods that uncover problems only after they occur, AI enables institutions to stay ahead of risks, ensuring compliance continuity even amid evolving regulations and mounting cyber threats. This paradigm shift not only reduces compliance costs but also strengthens trust, operational resilience, and customer confidence. The discussion underscores the strategic imperative for banks to integrate AI proactively, not as an optional tool, but as the backbone of modern compliance architecture.

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