An Explainable AI Framework for Identity Document Authentication in AML/KYC Verification

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.