Efficient AI-driven allegation screening: A case study of Thailand’s National Anti-Corruption Commission

Issara Sereewatthanawut, Patipan Sriphon, Pattrawut Khunwipusit, Babatunde Oluwaseun Ajayi, Ademola Enitan Ilesanmi, Jutarat Suwaree and Wonlop Writthym Buachoom

PLOS ONE, 2026, vol. 21, issue 1, 1-20

Abstract: Efficient screening of corruption allegations is crucial for promoting accountability and transparency in public administration. However, many institutions still rely on manual processes that are prone to inefficiency and inconsistency. As AI gains traction across sectors, this study develops and evaluates an artificial intelligence (AI)-powered prototype designed to support the preliminary screening of corruption complaints at Thailand’s National Anti-Corruption Commission (NACC). The proposed system integrates Optical Character Recognition (OCR), Natural Language Processing (NLP), and machine learning techniques to automate document handling and improve workflows. A mixed-methods research approach was adopted, combining institutional process analysis with a comprehensive technical performance assessment. The OCR module achieved an F1-score of 81.8%, with precision and recall of 84.2% and 79.6%, respectively. For printed text, the system attained 72% word-level accuracy and 78% at the character level. Additionally, the integrated framework demonstrated a classification accuracy of 57.5% and significantly improved operational efficiency, reducing average complaint processing time by 78.6% compared to traditional manual methods. The findings highlight AI’s transformative potential in enhancing anti-corruption efforts through increased speed, accuracy, and consistency. They underscore the importance of responsible and context-sensitive AI adoption in public sector governance. This study contributes to the growing discourse on digital governance by providing empirical evidence and practical insights for policymakers and practitioners aiming to implement scalable, transparent, and ethically grounded AI solutions within institutional accountability frameworks.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0338633

DOI: 10.1371/journal.pone.0338633

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