Detecting Anomalies in Financial Data Using Machine Learning Algorithms

• Published in: Systems (Basel) Vol. 10; no. 5; p. 130
• Main Authors: Bakumenko, Alexander, Elragal, Ahmed
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022
• Subjects: Accounting; Algorithms; Anomalies; anomaly detection; Auditing; Auditors; Audits; Automation; Datasets; Deep learning; Enterprise resource planning; Evaluation; Financial statements; Forensic accounting; Fraud; general ledger; Information systems; Informationssystem; Machine learning; Random sampling; Safety and security measures; Sweden

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...