Advancements in computer-aided audit technologies: Impact on loan asset quality in banking

Abstract

The banking sector continually adapts to technological advancements, and computer-aided audit technologies (CAATs) play a pivotal role in enhancing operational efficiency and risk management. This study explores the impact of CAATs on loan asset quality in banking, investigating how these technologies contribute to the identification, assessment, and mitigation of risks associated with loan portfolios. The research delves into the integration of advanced data analytics, artificial intelligence, and machine learning within audit processes, examining their effectiveness in evaluating loan asset quality. Through a comprehensive review of case studies and industry-specific implementations, the study aims to elucidate the practical implications and outcomes of deploying CAATs in banking audits. Key focus areas include the ability of CAATs to detect early warning signs of potential credit risks, assess the accuracy of loan data, and streamline audit workflows. The study also evaluates the role of these technologies in enhancing regulatory compliance, reducing audit cycle times, and improving the overall reliability of financial reporting related to loan portfolios. By understanding the impact of CAATs on loan asset quality, this research contributes valuable insights to banking professionals, auditors, and policymakers seeking to harness the benefits of technological advancements in ensuring the integrity and stability of loan portfolios within the financial landscape.