2026-10-12T00:07:53Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34041549 2026-10-01T08:27:39Z category_39 category_45 category_931 category_106 portal_87 item_type_6 month_year_10_2026

AI-based Identity Fraud Detection: A Systematic Review ChuoJun Zhang (25155366) Asif Q. Gill (25155369) Bo Liu (127343) Memoona Anwar (25155372) Ecology Sociology Information Systems not elsewhere classified Science Policy personally identifiable information highlights key insights foundational knowledge base based idf methods based idf detection idf ). prevention methods detection approaches work provides systematic review rapid development pressing need practitioners advancing potential solutions paper presents open challenges increasingly exposed identity fraud growing use enabling fraudsters emerging trends digital services consolidates findings artificial intelligence <p>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.</p> 2026-10-01T08:27:39Z Text Journal contribution 10.6084/m9.figshare.34041549.v1 https://figshare.com/articles/journal_contribution/AI-based_Identity_Fraud_Detection_A_Systematic_Review/34041549 CC BY 4.0