Research Ideas for Artificial Intelligence in Auditing: The Formalization of Audit and Workforce Supplementation

TLDR

Artificial intelligence has progressed beyond early setbacks, and while many industries now reap its benefits, auditing has historically lagged but is poised for automation due to its labor intensity and decision‑making complexity. The paper outlines promising AI research directions for auditing and poses methodological questions to guide the AI‑driven transformation of audit into future assurance.

Abstract

ABSTRACT After decades of frustration with long “AI Winters,” various business industries are witnessing the arrival of AI's “Spring,” with its massive and compelling benefits. Auditing will also evolve with the application of AI. Recently, there has been a progressive evolution of technology aimed at creating “artificially intelligent” devices. Although this evolution has been permeated with false starts and exaggerated claims, there is some convergence on the fact that substantive progress has been obtained in the last few years with the adoption of deep learning in conjunction with much faster machines and dimensionally larger storage spaces (and samples). The area of auditing has lagged business adoption in the past (Oldhouser 2016), but is prime for partial automation due to its labor intensiveness and range of decision structures. Several accounting firms have disclosed substantive investments in the AI fields. This paper proposes various areas of AI-related research to examine where this emerging technology is most promising. Moreover, this paper raises a series of methodological and evolutionary research questions aiming to study the AI-driven transformation of today's world of audit into the assurance of the future.