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AI-Assisted Audit Performance and Ethical Risk Governance in Financial Fraud Identification

Oct 2026 · International Journal of Technoethics · 32 references
Corporate Governance and Financial Management

Abstract

This study constructs a dual-cycle framework integrating financial fraud identification and ethical governance for artificial intelligence-assisted audit. It uses financial indicators, text features, and corporate governance variables to build multi-dimensional detection models. Traditional models and machine learning models including tree ensemble, explainable, and review-adjusted models are compared. Empirical results show that machine learning models outperform traditional models in fraud identification accuracy and stability. The explainable module and manual review can effectively reduce ethical risks such as opacity, bias amplification, over-reliance, and responsibility ambiguity. This work balances technical performance and ethical compliance, reveals ethical challenges of intelligent audit applications, and provides a governance path for the standardized application of artificial intelligence in auditing.

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