Tri-Layer Anomaly/ Fraud Detection Framework for Audit and Business Analytics
This whitepaper introduces the "Tri-Layer Anomaly/Fraud Detection Framework," a practical, multi-layered approach designed by Charm Myae Aung to enhance anomaly and fraud detection in audit and business analytics. Implemented in KNIME Analytics Platform, the framework combines statistical methods, machine learning, and time series analysis to detect irregularities across structured business datasets. Its strength lies in its adaptability to various audit scenarios, improving the precision and efficiency of anomaly and fraud detection.
Institutions
- SMART Reading (US)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-05-29
- DOI
- https://doi.org/10.5281/zenodo.15540277
- Primary Topic
- Imbalanced Data Classification Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00