Ai-Driven Signal and Image Analytics for Economic Crime Detection: A Techno-Legal Framework for Regulatory Compliance

Prof. (Dr.) Prem Chandra; Dr. Shalini Bhartiya; Dr Samriti Mann; Gaurav; Dr. Arti Rani; Vishesh Bhardwaj; Dr. Govind Prasad Goyal

doi:10.29284/d6e6c834

Authors

Prof. (Dr.) Prem Chandra Professor, Faculty of Law, Swami Vivekanand Subharti University, Meerut, UP (India)-250005
Dr. Shalini Bhartiya Assistant. Professor, Vivekananda Institute of Professional Studies
Dr Samriti Mann Assistant Professor, SRM University Delhi NCR
Gaurav Research Scholar, National Law University and Judicial Academy, Assam
Dr. Arti Rani Assistant Professor , School of Management Studies, CGC University, Janjheri, Mohali -140307(PB)
Vishesh Bhardwaj Assistant Professor, SRM University, Haryana
Dr. Govind Prasad Goyal Dean Students’ Welfare, IMS Law College, Noida

DOI:

https://doi.org/10.29284/d6e6c834

Keywords:

artificial intelligence, economic crime detection, signal analytics, image analytics, regulatory compliance, anti-money laundering, algorithmic accountability, financial surveillance, machine learning, techno-legal framework

Abstract

The accelerating sophistication of economic crime — encompassing money laundering, securities fraud, market manipulation, tax evasion, financial statement falsification, and cyber-enabled financial offenses — has fundamentally outpaced the capacity of traditional rule-based compliance and investigation frameworks. Artificial intelligence (AI), and specifically its applications in signal analytics and image analytics, has emerged as a paradigm-transforming toolkit for the detection, investigation, and prosecution of economic crime. This paper develops a comprehensive techno-legal framework for AI-driven signal and image analytics as instruments of economic crime detection, situated within the evolving landscape of regulatory compliance obligations. Drawing on developments in machine learning, deep neural networks, graph analytics, satellite image intelligence, and forensic document analysis, the paper examines how AI-powered analytical systems process financial transaction signals, market data streams, remote sensing imagery, and digital document images to identify patterns indicative of criminal economic conduct. Critically, the paper interrogates the legal and regulatory dimensions of deploying such systems: evidentiary admissibility, data privacy and protection obligations, explainability and algorithmic accountability requirements, international regulatory harmonization, and the ethical implications of AI-driven surveillance of financial behavior. The paper proposes a techno-legal framework that balances the imperative of effective economic crime detection against the fundamental rights and due process guarantees that constrain state and regulatory power in democratic legal systems. It argues that the effective governance of AI-driven financial crime analytics requires a co-evolutionary approach in which technological development and legal-regulatory reform proceed as mutually informing processes rather than in isolated silos.

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