Applied Artificial Intelligence· 2026Q1· Review
AI-based Identity Fraud Detection: A Systematic Review
- 5citations
- Q1SCImago
- 2026year
Short summary
A systematic review consolidates AI-based identity fraud detection methods into two principal categories, highlighting key insights, challenges, and trends in combating sophisticated deepfake threats.
AI-generated from the title and abstract; the full text is not read.
Key points
- Identity fraud (IDF) is a growing threat due to increased digital service use and sophisticated AI-enabled deepfake technologies.
- The review categorizes AI-based IDF detection methods into two principal approaches.
- A taxonomy of AI-based IDF methods is presented, consolidating findings from the literature.
- Key insights, open challenges, and emerging trends in AI-based IDF detection are highlighted.
AI-generated from the title and abstract; the full text is not read.
Abstract
With the rapid development of digital services, personally identifiable information (PII) is increasingly exposed to identity fraud (IDF). The growing use of artificial intelligence (AI)-enabled deepfake technologies has further amplified this threat, enabling fraudsters to produce highly sophisticated counterfeit documents, photos, and videos. There is a pressing need to systematically review IDF detection methods, their limitations, and potential solutions. This paper presents a systematic literature review examining AI-based IDF detection and prevention methods. The review identifies two principal categories of detection approaches, consolidates findings into a taxonomy of AI-based IDF methods, and highlights key insights, open challenges, and emerging trends. This work provides a foundational knowledge base for researchers and practitioners advancing this critical area.
The authors' abstract, as published at the source. Applied Artificial Intelligence, 2026 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Signal Processing
Signal ProcessingComputer Science