<![CDATA[The rapid digital transformation of Indonesia's banking sector has generated a massive and complex volume of financial transactions. The value of digital banking transactions reached IDR 52,545 trillion in 2022 (Bank Indonesia, 2023); however, this growth has been accompanied by increasingly sophisticated fraud risks that are difficult to detect using conventional Accounting Information Systems (AIS). Traditional systems that remain reactive relying on manual audits, rule-based systems, and data sampling methods are often only able to detect fraud after losses have already occurred, creating an urgent need for innovation through the integration of Big Data Analytics into AIS to support faster, more accurate, automated, and preventive fraud detection. This study aims to analyze how the integration of Big Data Analytics into Accounting Information Systems can improve the effectiveness of fraud detection in Indonesia's banking sector, with a specific focus on real-time detection mechanisms, system automation, transaction pattern analysis, and machine learning-based anomaly detection. The method employed is a Systematic Literature Review (SLR), following a structured identification-screening-eligibility-inclusion protocol across Scopus, Web of Science, ScienceDirect, SpringerLink, Emerald Insight, and Google Scholar, resulting in 42 articles published between 2019 and 2025 that met the inclusion criteria. The novelty of this study lies in synthesizing the technical fraud-detection literature which has largely focused on algorithmic performance with the organizational and systems perspective of Accounting Information Systems, producing an integrated fivelayer architecture and a real-time detection workflow that are contextualized specifically for Indonesia's banking sector rather than adapted from developed-country settings. The findings indicate that Big Data Analytics integration enables AIS to perform full population analysis across all financial transactions, in contrast to conventional systems that rely on sampling, and that machine learning, anomaly detection, predictive analytics, and graph analytics consistently show higher accuracy and lower false-positive rates across the reviewed literature, while supporting an early warning mechanism that can prevent fraud before losses escalate. This study concludes that integrating Big Data Analytics into Accounting Information Systems is an adaptive, preventive, and strategically necessary step for Indonesia's banking sector during digital transformation. Successful implementation, however, remains contingent on technology infrastructure, competent human resources, sound data governance, and stronger regulatory support from OJK and Bank Indonesia. This study's main contribution is a practically grounded, incrementally implementable roadmap that connects theoretical constructs (Fraud Triangle Theory, Agency Theory, TAM) with concrete architectural and operational recommendations for Indonesian banks, including small and regional banks. Keywords: Big Data Analytics, Accounting Information System, Fraud Detection, Real-Time Analytics, Machine Learning, Anomaly Detection, Indonesian Banking.]]>
