Fraud Detection Using Artificial Intelligence and Big Data Analytics in Accounting: A Systematic Literature Review
TL;DR
The evidence suggests that AI and BDA are most effective when deployed as decision-support mechanisms that complement professional skepticism and audit judgment rather than replace them.
Abstract
This study reviews the development and application of artificial intelligence (AI) and big data analytics (BDA) for fraud detection in accounting and auditing. The review adopts a systematic literature review approach guided by PRISMA principles and synthesizes 20 peer-reviewed and scholarly sources covering data mining, machine learning, natural language processing, deep learning, audit analytics, and big data. The literature indicates that AI and BDA extend fraud detection from periodic, sample-based procedures toward continuous, risk-oriented analysis of large volumes of structured and unstructured data. Machine learning methods, including logistic regression, support vector machines, decision trees, ensemble methods, neural networks, and deep learning, are increasingly used to classify suspicious observations and identify nonlinear fraud patterns. BDA strengthens these models by integrating financial ratios, transaction records, audit evidence, textual disclosures, management commentary, and external information. The review also identifies persistent challenges involving class imbalance, data quality, explainability, privacy, model bias, cybersecurity, and auditor competencies. Overall, the evidence suggests that AI and BDA are most effective when deployed as decision-support mechanisms that complement professional skepticism and audit judgment rather than replace them. Future research should emphasize multimodal data integration, explainable AI, real-time analytics, robust validation across jurisdictions, and governance frameworks for responsible AI-enabled accounting and auditing.