Embracing AI in Auditing: An Examination of Auditor Readiness Through the TRAM Framework

Abstract

The impact of technological developments has now led to a transformation in the preparation of financial statements.By applying machine learning, auditors can easily increase anomalies detection.The purpose of this study is to determine the effect of optimism, innovativeness, perceived usefulness, and perceived ease of use on auditors' intention to use machine learning.Data were collected using an online questionnaire and analyzed using the Structural Equation Modeling-Partial Least Square (SEM-PLS).The sample in this study used a nonprobability sampling and has a sample size of 100 respondents from auditors who work in a Public Accounting Firm in DKI Jakarta and Tangerang areas.The results of testing this study using SmartPLS 4 are optimism has a significant effect on perceived usefulness and perceived ease of use, while innovativeness only has a significant effect on perceived ease of use.In addition, perceived ease of use has a significant effect on perceived usefulness.This study implies that auditors' perception of the usefulness can influence the intention to use machine learning.However, perceived ease of use does not affect the intention to use machine learning.Therefore, we suggest that audit firms could establish training programs to enhance digital skills for auditor.

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