Publication details
AI-Enabled Financial Fraud and Deepfake Technologies: Emerging Threats, Detection Challenges, and Implications for Counter-Fraud Practice
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| Year of publication | 2026 |
| Type | Appeared in Conference without Proceedings |
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| Description | The rapid integration of artificial intelligence into digital services has reshaped financial fraud, introducing attack vectors that challenge established assumptions about detection, prevention, and accountability. In the banking sector, AI-powered frauds—such as deepfake impersonation, voice cloning, synthetic identities, and automated social engineering—enable highly convincing and scalable schemes exploiting both technological infrastructures and human trust. This paper identifies and classifies contemporary AI-enabled frauds relevant to financial services and counter-fraud practice. It analyses the technical mechanisms underlying these attacks and their deployment across key banking interactions, including remote customer identification, biometric authentication, and transaction authorization. Building on this classification, the paper outlines its intention to examine the impacts of AI-enabled frauds on detection capabilities, risk allocation, and the effectiveness of counter-fraud frameworks. Particular attention is paid to the growing asymmetry between attacker capabilities and the realistic ability of individuals to recognize or prevent such frauds, contributing to interdisciplinary discussions on adapting counter-fraud strategies to AI-driven cybercrime. |
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