Citation

Huey, Eldeena Yinn Lim and Connie, Tee (2026) An Explainable AI Framework for Identity Document Authentication in AML/KYC Verification. Future Internet, 18 (9). p. 485. ISSN 1999-5903

Text futureinternet-18-00485.pdf - Published Version Restricted to Repository staff only Download (28MB)

Abstract

This study investigates the development of an AI-driven document authentication framework for Anti-Money Laundering (AML) and Know Your Customer (KYC) verification environments. Conventional manual inspection and rule-based verification techniques often fail to detect sophisticated forged identity documents containing subtle visual or semantic manipulations. To address this limitation, the proposed framework combines handcrafted forensic feature extraction, OCR-driven semantic analysis, rule-based semantic field extraction and Random Forest classification to identify inconsistencies within identity documents captured under realistic mobile imaging conditions. Experimental evaluation was conducted using selected MIDV-2020 identity document subsets consisting of Albanian identity cards, Latvian passports, and Slovakian identity cards. The proposed framework achieved a recall rate of 92.31% and an overall accuracy of 84.85% on the held-out test set, while maintaining interpretable forensic feature analysis suitable for regulated AML/KYC environments. The results demonstrate that lightweight and explainable machine learning approaches can provide effective forged-document detection without requiring computationally intensive deep learning architectures.

Item Type:	Article
Uncontrolled Keywords:	anti-money laundering, document forgery detection, computer vision, machine learning, OCR-driven semantic validation, forensic analysis
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28-70 Management. Industrial Management > HD30.2 Electronic data processing. Information technology. Including artificial intelligence and knowledge management
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	30 Oct 2026 03:47
Last Modified:	30 Oct 2026 03:47
URII:	http://shdl.mmu.edu.my/id/eprint/17037

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