Internal audit is a bulwark ensuring the integrity of financial statements, a linchpin for stakeholder trust and informed corporate decision-making. With the proliferation of complex financial transactions, audit teams face mounting challenges in deciphering voluminous transactional data to safeguard financial reporting quality. Machine learning has the potential to identify signifiers of financial reporting quality. Within the Design Science Methodology framework, we apply the Random Forest Classifier technique to metrics such as the error rate to enhance financial reporting. We find that the Random Forest Classifier identifies that certain parameters are critical to error detection, which enhance account receivable accuracy, lower receivable account control risk. This research advances the argument that technologically-enhanced internal audit procedures can play a pivotal role in ensuring that financial reporting mirrors the economic reality of the company.