On-Line Evolving Clustering for Financial Statements' Anomalies Detection

被引：0

作者：

Omanovic, Samir ^{[1
]}

Avdagic, Zikrija ^{[1
]}

Konjicija, Samim ^{[1
]}

机构：

[1] Fac Elect Engn, Dept Comp & Informat, Sarajevo, Bosnia & Herceg

来源：

2009 XXII INTERNATIONAL SYMPOSIUM ON INFORMATION, COMMUNICATION AND AUTOMATION TECHNOLOGIES | 2009年

关键词：

anomalies detection; evolving clustering; fraud detection; FRAUD;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This document proposes an approach for financial statements' anomalies detection by using on-line evolving clustering [1]. 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.

引用

页码：269 / 272

页数：4

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