This paper provides the theoretical review of the fraud risk characteristics, systematization of the existing fraud knowledge and the causes of fraud occurrence. Moreover, it determines the roles and the responsibilities of managers and auditors in the fraud risk assessments. In addition, risk factors relating to the misstatements arising from fraudulent financial reporting have been examined. ...

This paper explores the effectiveness of Data Mining (DM) classification techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS. In accomplishing the task of management fraud detection, auditors could be facilitated in their work by using data mining techniques. This study investigates the usefulness of Decis...

This study aims to detect the risk of fraud in financial statements and ongoing prevention measures if there are irregularities statements. The issue earnings management PT Garuda Indonesia (Persero) Tbk by significantly increasing profits has raised a lot suspicion greatly harmed many parties, especially investors who invested this company. analysis was carried out using triangle variable aski...

______________________________________________________ This study actually draws from and builds on an earlier paper (Kumar and Bhattacharya, 2002). Here we have basically added a neutrosophic dimension to the problem of determining the conditional probability that a financial fraud has been actually committed, given that no Type I error occurred while rejecting the null hypothesis H0: The obse...

Data mining tools become important in finance and accounting. Their classification and prediction abilities enable them to be used for the purposes of bankruptcy prediction, going concern status and financial distress prediction, management fraud detection, credit risk estimation, and corporate performance prediction. This study aims to provide a state-of-the-art review of the relative literatu...

In this paper we describe the results achieved using the JAM distributed data mining system for the real world problem of fraud detection in financial information systems. For this domain we provide clear evidence that state-of-the-art commercial This research is supported in part by grants from DARPA (F30602-96-1-0311) and NSF (IRI96-32225 and CDA-96-25374).