HIGHLIGHTS
- who: Alexander Bakumenko and Ahmed Elragal from the Department of Computer Science, Electrical and Space Engineering, University of Technology have published the article: Detecting Anomalies in Financial Data Using Machine Learning Algorithms, in the Journal: Systems 2022, 10, 130. of /2022/
- what: The aim of this work is to apply various supervised and unsupervised machine_learning techniques to detect anomaly journal entries in the collected general ledger data for more efficient audit sampling and further examination. Output from unsupervised models is based on the test data-related properties understanding and findings, and the main goal is . . .
If you want to have access to all the content you need to log in!
Thanks :)
If you don't have an account, you can create one here.