From Ledger Lines to Algorithms: How Digital Tools Are Rewriting Fraud Detection in Auditing

Fraud has always been a cat-and-mouse game between those who manipulate financial records and those who try to catch them. For most of the twentieth century, the auditor’s toolkit consisted of sampling procedures, manual reconciliations, and professional skepticism applied to paper trails. Today, that world is vanishing. A systematic literature review published in Discover Sustainability by Wuryan Andayani of Universitas Brawijaya, Tutut Herawan of the University of Malaya, and colleagues maps how the auditing profession is being rebuilt around digital technologies, with artificial intelligence, big data analytics, blockchain, and computer-assisted audit tools emerging as the central instruments of a new fraud-detection era.

The research team set out to examine the role of accountants in identifying, mitigating, and detecting fraud in the field of digital technology, while also identifying patterns, innovations, gaps, and potential future developments in the literature. To do this rigorously, they employed a Systematic Literature Review method structured by the CIMO-Logic framework, an analytical device that organizes evidence around Context, Interventions, Mechanisms, and Outcomes. This framework allowed the authors to move beyond simply cataloguing studies and instead trace how specific technological interventions produce measurable changes in fraud-detection capability under particular organizational conditions.

The scope of the review covered a six-year period from 2020 to 2025, a window chosen to capture the most recent wave of digital transformation in accounting research. From searches of ScienceDirect and Google Scholar, the team selected 32 articles that met their criteria. That relatively small but focused corpus reflects both the maturity and the immaturity of the field: there is now a substantial body of peer-reviewed work on digital fraud detection, yet the literature remains fragmented across disciplines, methodologies, and technology domains, which is precisely the kind of landscape a systematic review is designed to clarify.

The headline finding is unambiguous. Technologies such as artificial intelligence, big data analytics, computer-assisted audit tools and techniques, known in the profession as CAATTs, blockchain, and other specialized digital tools play a significant role in enhancing accountants’ capabilities to detect and prevent fraud. Each of these technologies attacks a different weakness of the traditional audit. AI systems, particularly machine learning models, can classify transactions as anomalous or legitimate at scales no human team could match, learning the statistical fingerprints of fraud from historical cases. Big data analytics extends the auditor’s field of vision from small samples to entire transaction populations, eliminating the sampling risk that once allowed fraudulent entries to slip through unnoticed.

Computer-assisted audit tools and techniques occupy an interesting position in this story because they represent the bridge generation between manual auditing and fully intelligent systems. CAATTs allow auditors to interrogate complete datasets, run repeatable analytical tests, and flag exceptions automatically, which means that even organizations not yet ready for deep learning pipelines can achieve continuous, full-population testing. The reviewed literature shows that these tools are not merely conveniences; they change the mechanism of detection itself, shifting the auditor’s role from retrospective verification of selected records toward proactive, ongoing surveillance of financial flows.

Blockchain technology contributes a different kind of assurance. Because distributed ledger systems create immutable, timestamped, and cryptographically linked records, they reduce the opportunity for transaction tampering at the source rather than detecting it after the fact. In the framework of the review, blockchain functions as a preventive mechanism embedded in the accounting infrastructure, complementing the detective power of AI and analytics. Together, these technologies form a layered defense: blockchain hardens the data, big data analytics widens the view, and machine learning sharpens the identification of anomalies within that view.

Beyond the technology itself, the authors found that digitalization is a key factor transforming the role of accountants in fraud mitigation. The reviewed studies consistently indicate that as organizations digitize, two organizational consequences follow. First, there is growing pressure to improve the digital competencies of accounting professionals, since tools are only as effective as the people who deploy and interpret them. Second, digitalization encourages the strengthening of internal controls within organizations, because automated systems expose control weaknesses that manual processes could conceal. The auditor of the literature review’s future is therefore not a replaced professional but a redefined one, combining accounting judgment with data science literacy.

The theoretical implications of the review are considerable. By synthesizing the literature on the application of digital technologies in accounting for fraud detection and prevention, the study provides empirical evidence of how technology can transform accounting mechanisms, and it highlights the importance of integrating digital technology with organizational behavior. This integration point matters because fraud is ultimately a human phenomenon, enabled by opportunity, rationalization, and pressure, and technological controls interact with those human factors rather than operating in isolation. A model that flags anomalies is only useful if organizational incentives support investigation of the flags, and a blockchain ledger only helps if governance ensures the integrity of what enters it.

The practical implications are equally direct. The study emphasizes that accountants and auditors need to adopt digital technologies in order to improve the effectiveness of auditing and financial oversight, including through investments in security systems and big data infrastructure to prevent both internal and external fraud. For firms, this reframes technology spending as fraud-risk management rather than optional modernization. For regulators and standard setters, the findings suggest that audit standards built around sampling and periodic testing may need continued evolution toward continuous, technology-mediated assurance. For educators, the message is that curricula must produce graduates fluent in both double-entry bookkeeping and data analytics.

The review also leaves open questions that define the next research frontier. With only 32 articles across six years meeting the inclusion criteria, the evidence base remains thin in areas such as the real-world performance of AI fraud-detection models across industries, the costs and failure modes of blockchain-based assurance, and the behavioral responses of fraudsters to increasingly automated detection. The authors note that identifying these gaps and potential future developments was a core aim of the study, and their mapping of trends and methodologies gives researchers a clearer starting point. What is already clear, however, is that the transformation is not a forecast but an ongoing shift: the audit file of the 2020s is increasingly a live data pipeline, and the profession’s oldest skill, skepticism, is being augmented by algorithms that never sleep.

Subject of Research: Digital technologies transforming auditing and fraud detection

Article Title: The transformation of traditional auditing toward digital fraud detection a systematic literature review

Article References: Andayani, W., Herawan, T., Sari, S. N., & Prestianawati, S. A. (2026). The transformation of traditional auditing toward digital fraud detection a systematic literature review. Discover Sustainability. https://doi.org/10.1007/s43621-026-04666-9

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04666-9

Keywords: auditing, fraud detection, artificial intelligence, big data analytics, blockchain, CAATTs, digitalization, forensic accounting, systematic literature review, internal controls, accounting, financial technology