Artificial intelligence applications and the reduction of accounting errors in bank financial statements: Evidence from the Iraqi banking sector

Abstract

This study examines the extent to which artificial intelligence (AI) applications contribute to reducing accounting errors in bank financial statements, with a focus on the obstacles facing their adoption in accounting practice. Using a descriptive-analytical approach, a structured questionnaire was distributed to 100 preparers and auditors of financial statements in the Iraqi banking sector, yielding 91 valid responses for analysis. The findings indicate that AI applications can detect discrepancies between the methods used to prepare banking financial statements and standard or cross-country practices, and can identify the party responsible for an accounting error, regardless of its size. However, the results also show that AI applications lack the discretionary judgement that human auditors apply in assessing the materiality of an error and its broader implications for banking operations. The study recommends that preparers and auditors of bank financial statements leverage AI's error-detection capabilities, irrespective of error size, to improve the accuracy and reliability of financial statements in the banking sector. The findings contribute to the growing literature on AI-enabled auditing and offer practical implications for bank management, financial statement preparers, and regulators in emerging banking markets.