Auditors manually evaluate numerous documents and contracts, review implementations of regulatory changes, and investigate dubious transactions. Owing to the large volume of data and the high cost of these efforts, auditors usually achieve these goals by relying on a sample-based approach or performing the audit based on a defined checklist.

There is a need to utilize machine learning to reduce the workload of auditors and use machine learning to extend the scope of audits beyond sampling. This includes using AI to review the entirety of available data and automatically notifying auditors of high-risk items. Automating such simple tasks allows auditors to focus on work requiring human intelligence and creativity.