Risk disclosure complexity and report readability: an AI-based analysis
Zabihollah Rezaee, Javad RajabalizadehPurpose
The purpose of this study is to examine how the extent, structure and semantic organization of risk disclosures affect the readability of 10-K reports in US financial institutions. The study aims to move beyond traditional keyword-based disclosure measures by introducing artificial intelligence (AI) based linguistic and semantic metrics to better understand how risk transparency interacts with textual clarity.
Design/methodology/approach
The study combines traditional and AI-driven textual analysis methods to measure both risk disclosure and readability. Risk disclosure is captured using dictionary-based keyword measures as well as AI-based topic modeling and semantic embedding techniques that quantify thematic dispersion, concentration, dominance and novelty. Readability is measured using the Bog Index together with AI-based linguistic indicators of syntactic complexity and semantic atypicality. The empirical analysis is conducted on 4,649 firm-year observations of US banks and insurance companies over the period 2005–2023.
Findings
The results show that greater risk disclosure is consistently associated with lower readability across both traditional and AI-based measures. Higher risk-term intensity, broader thematic dispersion and greater semantic novelty increase linguistic and structural complexity, making reports harder to read. In contrast, concentrated and semantically cohesive risk narratives improve readability. AI-based topic and embedding measures produce stronger and more consistent effects than keyword counts alone. Additional analyses indicate that positive disclosure tone, stronger board effectiveness and higher auditor effectiveness mitigate the negative readability effects of extensive risk reporting. Crisis conditions, such as the COVID-19 period, amplify disclosure dispersion and further reduce readability.
Originality/value
To the best of the authors’ knowledge, this study is among the first to jointly integrate dictionary-based, topic-model, syntactic and semantic embedding approaches to analyze the relationship between risk disclosure and report readability. It demonstrates that AI-based linguistic measures add substantial explanatory power beyond traditional readability and keyword metrics and offers a comprehensive framework for evaluating disclosure clarity in financial reporting.