Detecting Anomalies in Financial Data Using Machine Learning Algorithms
Bookkeeping data free of fraud and errors are a cornerstone of legitimate business operations. The highly complex and laborious work of financial auditors calls for finding new solutions and algorithms to ensure the correctness of financial statements. Both supervised and unsupervised machine learni...
Main Authors: | Alexander Bakumenko, Ahmed Elragal |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-08-01
|
Series: | Systems |
Subjects: | |
Online Access: | https://www.mdpi.com/2079-8954/10/5/130 |
Similar Items
-
Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection
by: Aida Kamišalić, et al.
Published: (2021-08-01) -
A Blockchain Architecture for Trusted Sub-Ledger Operations and Financial Audit Using Decentralized Microservices
by: Noussair Fikri, et al.
Published: (2022-01-01) -
Blockchain in accounting and auditing: unveiling challenges and unleashing opportunities for digital transformation in Egypt
by: Ahmed Anis
Published: (2023-08-01) -
A Framework for Verifiable and Auditable Collaborative Anomaly Detection
by: Gabriele Santin, et al.
Published: (2022-01-01) -
Anomaly Detection in Endemic Disease Surveillance Data Using Machine Learning Techniques
by: Peter U. Eze, et al.
Published: (2023-06-01)