Peer-reviewedCC BYInternational Review of Economics & Finance
The role of big data analytics in financial fraud detection: A systematic literature review
- 1Doctoral Program, Faculty of Economics and Business, Universitas Gadjah Mada, and Lecturer at the Department of Accounting, School of Economics and Business, Telkom University, Indonesia
- 2Professor at the Department of Accounting, Faculty of Economics and Business, Universitas Gadjah Mada, Indonesia
Abstract
• Big Data Analytics improves financial fraud detection accuracy • Auditors, analysts, investors, and regulators are primary users • Predictive analytics dominates fraud detection practices • Data integration reduces information asymmetry • Fraud theories enhance machine learning performance
Related
- Deep Learning-Based Object Detection for Fashion, Uniform, and PPE Items: A Systematic Literature Review
- Governing Agentic AI in Africa: A Systematic Literature Review of Ethical Challenges, Framework Gaps, and Ubuntu-Informed Governance Solutions
- Hierarchical Cognitive Structuring and Game Mechanics Alignment in Educational Game Systems: A Systematic Literature Review
- Graph-Enhanced Knowledge Tracing for Explainable Learner Modeling: A Systematic Literature Review
- Mapping E-Readiness Dimensions for Digital Village Development: A Systematic Literature and Bibliometric Review
BibTeX
@article{triyanto2026role,
title = {{The role of big data analytics in financial fraud detection: A systematic literature review}},
author = {Dedik Nur Triyanto and Syaiful Ali},
journal = {International Review of Economics \& Finance},
year = {2026},
month = may,
doi = {10.1016/j.iref.2026.105453},
url = {https://doi.org/10.1016/j.iref.2026.105453},
note = {CC BY},
}
RIS
TY - JOUR
TI - The role of big data analytics in financial fraud detection: A systematic literature review
AU - Dedik Nur Triyanto
AU - Syaiful Ali
PY - 2026
DA - 2026/05/26
JO - International Review of Economics & Finance
DO - 10.1016/j.iref.2026.105453
UR - https://doi.org/10.1016/j.iref.2026.105453
AB - • Big Data Analytics improves financial fraud detection accuracy • Auditors, analysts, investors, and regulators are primary users • Predictive analytics dominates fraud detection practices • Data integration reduces information asymmetry • Fraud theories enhance machine learning performance
SN - 1059-0560
SN - 1873-8036
ER -