Enhancing Computer Assisted Audit Techniques (CAATs) with Artificial Neural Network (ANNs)

Abstract

This study explores the integration of Artificial Neural Networks (ANNs) with Computer Assisted Audit Techniques (CAATs) to enhance the efficiency, accuracy, and depth of auditing processes. Despite the established efficacy of CAATs in handling large data sets, their effectiveness diminishes with the increasing complexity and volume of financial transactions. ANNs offer a solution by leveraging their capacity to learn from data and identify complex patterns, thus significantly improving fraud detection and data analysis capabilities within audits.

The study examines how ANNs can be integrated with traditional auditing tools to address current limitations by conducting empirical tests and simulations across varied data sets. The findings indicate that ANNs not only augment the capabilities of CAATs in detecting discrepancies and fraudulent activities but also enable real-time data processing, which is crucial for timely decision-making in audits.  Furthermore, the study discusses the challenges and limitations associated with deploying ANNs in auditing, including the need for significant computational resources and the risks posed by the opaque nature of ANNs decision-making processes. Recommendations for mitigating these risks include enhancing transparency and developing continuous learning and adaptation mechanisms within ANNs to keep pace with evolving financial environments.  The integration of ANNs with CAATs marks a significant advancement in the auditing profession. It heralds a shift towards more technologically advanced audit methodologies, such as those incorporating IoT, which are well-suited to accommodate the complexities of modern financial systems. This ultimately leads to financial reporting that is both more reliable and transparent.