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TRUVACE RECORD VERSION
record: TRV-2026-2131
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-21T22:03:32.857469Z
status: published
lens: g_space
sector: business
headline: An Analytical Study on Artificial Intelligence as a Turning Point for the Transformation of the Auditing Industry
dek: With the digital economy growing at an unprecedented rate, corporate transaction data has become exponentially large, presenting several challenges to the auditing profession - including the exponential increase in the volume of data, more complex transaction patterns, and stricter regulatory requirements - making traditional sampling-based auditing techniques less effective. This paper examines how artificial intelligence (AI) can be a game changer in the evolution of the auditing industry. The research employs…
gain_title: AI is changing the scope, effectiveness, and decision-making references of the auditing profession, helping it cope with growing data volumes and complex transactions that weaken sampling-based methods.
problem_title: (none)
trace_subject: (none)
gain_reading: AI is changing the scope, effectiveness, and decision-making references of the auditing profession, helping it cope with growing data volumes and complex transactions that weaken sampling-based methods.
gain_evidence: AI is radically changing the scope, effectiveness, and decision-making references of the auditing profession
problem_reading: (none)
problem_evidence: (none)
quick_read: A paper published in 2026 reviews how artificial intelligence has been used in auditing since 2015. It starts from the pressure that rapidly growing corporate transaction data, more complex transaction patterns, and stricter regulation place on traditional sampling-based audit techniques.

The author concludes that AI is a key driver of change in the profession, though the change is gradual and the sector is still in transition. The work shows why audit firms and regulators may need to rethink governance and talent, but as a literature review and comparative analysis it offers conclusions about trends rather than measured results at specific firms.
limitation: The influence of AI is described as still unfolding slowly, with the sector in a transitional stage and unresolved issues around overreliance, transparency, accountability, and workforce change.
tag: Evidence-backed gain
key_points: The study uses a literature review and comparative analysis covering AI use in auditing since 2015. | Its analysis is organized into four dimensions: process transformation, motivations for adopting AI, practical benefits, and obstacles. | It describes the sector as moving from a mainly human-oriented approach toward a human-machine partnership. | Issues named for the transition include overdependence on AI, lack of transparency in algorithmic decisions, accountability, and workforce change.
rundown: The paper frames the problem as one of scale and complexity: corporate transaction data has become exponentially large, which the author says makes traditional sampling-based auditing techniques less effective.

The author organizes the review into four dimensions, covering how audit processes change, why firms adopt AI, what practical benefits result, and what obstacles arise during adoption.

From these findings the author offers recommendations on AI governance practices within firms, the creation of strong regulatory frameworks, and the development of competent auditing talent.
sources:
- peer_reviewed | Advances in Economics, Management and Political Sciences | https://doi.org/10.54254/2754-1169/2026.ld37791 | 2026-10-20
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