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<p>Artificial intelligence and machine learning technologies have fundamentally transformed financial control systems, introducing unprecedented capabilities for fraud detection, compliance monitoring, and risk assessment. Despite rapid technological adoption, scholarly attention to the legal and accountability implications of AI-driven financial control remains limited. This article argues that responsible AI governance in financial control requires not better algorithms but institutional and legal frameworks ensuring accountability while preserving procedural justice. Through analysis of machine learning applications and examination of the EU’s General Data Protection Regulation, the Artificial Intelligence Act, and the Dutch SyRI judgment, the article develops a framework distinguishing public and private sector accountability requirements. The central finding suggests that algorithmic efficiency and legal accountability need not be mutually exclusive, but achieving both demands institutional innovation that most jurisdictions currently lack.</p>

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AI-driven financial control systems: machine learning models for fraud and compliance monitoring

  • 期刊论文

  • 发表日期：

    2026年3月2日

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  • Ikrom Ergashev

摘要

Artificial intelligence and machine learning technologies have fundamentally transformed financial control systems, introducing unprecedented capabilities for fraud detection, compliance monitoring, and risk assessment. Despite rapid technological adoption, scholarly attention to the legal and accountability implications of AI-driven financial control remains limited. This article argues that responsible AI governance in financial control requires not better algorithms but institutional and legal frameworks ensuring accountability while preserving procedural justice. Through analysis of machine learning applications and examination of the EU’s General Data Protection Regulation, the Artificial Intelligence Act, and the Dutch SyRI judgment, the article develops a framework distinguishing public and private sector accountability requirements. The central finding suggests that algorithmic efficiency and legal accountability need not be mutually exclusive, but achieving both demands institutional innovation that most jurisdictions currently lack.