Human capital disclosure in Islamic banks: a multi-method analysis using machine learning

Purpose This study investigates the scope, intensity, and thematic structure of human capital (HC) disclosures in Islamic banks. It addresses the gap in understanding how HC narratives are constructed, benchmarked and communicated in faith-based financial institutions across diverse regulatory settings. Design/methodology/approach The study adopts a multi-method framework combining lexicon-based extraction, bidirectional encoder representations from transformers (BERT)-based sentence classification, criteria importance through intercriteria correlation (CRITIC) weighting, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) benchmarking and BERTopic modeling. The analysis is based on 638 annual reports from 86 Islamic banks across 21 countries (2015–2023). Findings Results reveal substantial heterogeneity in disclosure intensity and thematic focus across institutions and jurisdictions. Compensation and governance-related themes dominate reporting, while diversity, equity and inclusion and employee well-being remain underdisclosed. The COVID-19 pandemic triggered a sharp increase in health and safety reporting. Country-level rankings highlight Indonesia, Malaysia and Bangladesh as consistent leaders. Originality/value An artificial intelligence/machine learning-enabled, multi-method framework is developed to measure and interpret HC disclosure in Islamic banking by integrating transformer-based sentence classification with CRITIC-weighted benchmarking, TOPSIS ranking and topic modeling. The study extends automated disclosure analytics to a Shariah-compliant setting and offers a scalable approach for cross-jurisdictional comparability and governance insight.