INTEGRASI SISTEM INFORMASI AKUNTANSI DENGAN TEKNOLOGI BIG DATA DAN ARTIFICIAL INTELLIGENCE: TINJAUAN LITERATUR SISTEMATIS

Authors

DOI:

https://doi.org/10.66896/asset.1.01.2026.29

Keywords:

Accounting Information Systems, Big Data, Artificial Intelligence, Systematic Literature Review

Abstract

This study aims to map the trends, benefits, challenges, and integration models of Big Data and artificial intelligence (AI) in accounting information systems (AIS) during the period 2020 to 2025. The method employed is a systematic literature review (SLR) adopting the PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. The literature search was conducted on the Scopus database using Boolean keywords combining terms related to AIS, Big Data, and AI. From 582 articles identified, the selection process yielded 50 articles meeting all inclusion criteria for further analysis. The findings reveal that publication trends have grown consistently, with machine learning as the most dominantly applied AI technology (60%), followed by natural language processing (36%) and robotic process automation (28%). Key benefits of integration include improved operational efficiency, data accuracy, fraud detection, and faster decision-making. The most significant challenges involve data security and privacy, availability of skilled human resources, high implementation costs, and immature regulatory frameworks. This study also identifies the need for longitudinal research, exploration of AI ethics, and development of adoption frameworks for small and medium-sized accounting organizations.

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Published

2026-03-31

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Vol. 1 No. 01 (2026): Navigasi Pengelolaan Keuangan di Era Transformasi Digital dan Kepatuhan Korporasi.

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Artikel

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Copyright (c) 2026 Accounting Student Series on Emerging Trends

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How to Cite

Purnaman, S. M. N. (2026). INTEGRASI SISTEM INFORMASI AKUNTANSI DENGAN TEKNOLOGI BIG DATA DAN ARTIFICIAL INTELLIGENCE: TINJAUAN LITERATUR SISTEMATIS. Accounting Student Series on Emerging Trends, 1(01), 325-342. https://doi.org/10.66896/asset.1.01.2026.29