Audit Committee Oversight Functions and Financial Accountability Strengthening

Abstract

This research introduces a novel, computational framework for analyzing and
strengthening the oversight functions of corporate audit committees, a domain traditionally dominated by qualitative, compliance-based assessments. Departing from
conventional governance studies, we formulate the audit committee’s role as a complex adaptive system and apply principles from computational organizational theory, network analysis, and information theory to model its oversight efficacy. We
posit that financial accountability is not merely a product of regulatory adherence
but emerges from specific structural and informational dynamics within the committee’s interactions with management, internal audit, and external auditors. Our
methodology constructs a multi-agent simulation model where agents represent
committee members and key organizational actors, programmed with behavioral
rules derived from governance literature and empirical data on committee failures.
The model incorporates variables for expertise diversity, inquiry intensity, information asymmetry, and social network cohesion to simulate decision-making processes
around financial reporting and fraud risk assessment. A key innovation is the application of a ’supervision entropy’ metric to quantify the predictability and robustness
of oversight interactions. We test this framework using a unique dataset comprising
detailed minutes from 45 audit committee meetings of anonymized firms, which we
process using natural language processing techniques to extract interaction patterns
and topic focus. Our results identify a non-linear relationship between committee
meeting frequency and oversight effectiveness, with a threshold point beyond which
additional meetings yield diminishing returns. More significantly, we find that
committees with optimal ’informational network centrality’—acting as integrators
rather than passive recipients of reports—exhibit a 40