Fraud Detection¶
Fraud detection encompasses techniques, tools, and approaches to identify, prevent, and mitigate fraudulent activities across digital and financial systems. The emergence of generative AI has introduced new vectors for fraud, including identity-based scams, deepfake impersonation, and synthetic financial documents.
Scope¶
Fraud detection addresses:
- Traditional fraud vectors: identity theft, financial fraud, unauthorized access, phishing
- AI-enabled fraud: deepfake-based impersonation, synthetic document generation, voice cloning for authorization fraud, personalized scam campaigns
- Detection methods: behavioral analysis, biometric verification, content analysis, network pattern detection
- Verification and authentication: multimodal authentication, liveness detection, document authentication
Generative AI has lowered the cost and complexity of executing convincing fraud schemes. Media reports document cases of GenAI-enabled deepfakes used to impersonate trusted contacts (colleagues, family members) to extract money or information, as well as synthetic personas crafted for investment scams and phishing campaigns.
Key papers¶
- Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data — 18% of documented GenAI misuse involved fraud; prominent tactics include impersonation (especially audio/video deepfakes), falsification of identity and evidence, and personalized phishing scams
Related topics¶
- Generative AI Misuse (AI-enabled fraud tactics)
- Deepfakes (audiovisual deepfakes used in identity fraud)
- Synthetic Media Detection (detecting forged evidence and identity documents)
- Misinformation and fake news detection (detecting fraudulent claims and falsified narratives)