On-line evolving clustering for financial statements' anomalies detection

摘要

This document proposes an approach for financial statements' anomalies detection by using on-line evolving clustering. Official records of the financial activities of a business are called financial statements and they are recorded in journals and general ledger in a supervised process. Anomalies in financial statements are caused by human mistakes during forming of financial statements, or as a result of changes in the software that produced un-expected errors, or as possible financial fraud.