Human-AI collaboration in internal auditing: the moderating role of financial reporting quality

This study examines how artificial intelligence (AI) capabilities influence the quality of internal auditing (QIA) within a human-AI collaborative framework. It also investigates whether the quality of financial reports (QFR) moderates this relationship.

The study applies a quantitative, cross-sectional survey design. Data were collected from 150 employees working in accounting and audit-related roles in Jordanian industrial companies listed on the Amman Stock Exchange. The proposed model was tested using partial least squares structural equation modelling (PLS-SEM) in SmartPLS 4. AI capabilities were examined through four dimensions: expert systems (ES), algorithms (A), artificial neural networks (ANN), and intelligent agents (IA).

All four AI capability dimensions have positive and statistically significant effects on QIA. Algorithms exert the strongest effect, followed by intelligent agents, expert systems, and artificial neural networks. The interaction between AI capabilities and QFR is also positive and statistically significant. This indicates that AI capabilities contribute more strongly to QIA when financial reports are accurate, complete, timely, and reliable.

The study conceptualises internal auditing as a socio-technical process in which AI capabilities complement rather than replace auditors’ professional judgement. It extends the literature by identifying QFR as a contextual condition that shapes the effectiveness of AI-enabled internal auditing.

Organisations should combine investment in AI tools with improvements in financial reporting systems, data governance, auditors’ digital competencies, and human-inthe-loop procedures. Reliable financial information and professional oversight are necessary to realise the benefits of AI-enabled internal auditing.