In: Accounting
Fraud Detection Methods: Describe the various fraud detection methods used by auditors and forensic accountants to uncover fraudulent activity.
Meaning Of Fraud :
In simple terms fraud means intentional misrepresentation of facts i.e. hiding the reality with an intention to deceive or cheat for personal gains. Fraud is not equivalent to mistakes or errors because in mistakes the intention is not to deceive or misrepresent the fact.Therefore intention is the critical factor to determine whether the committed cause ia mistake or fraud.
Examples of fraud:
1. Recording of fictitious expenses and using the same for personal gains.
2. Drawing financial statement falsely i.e. in Satyam case in India
3. Personal use of financial resources.
Why fraud is usually committed :
Fraud is commited because of the following reasons :
1. Pressure Factors :
These are the factors which usually arises on account of personal desire or greediness without giving any thoughts whether commiting to the same is right or not.
Instances of Pressure Factors:
2. Oppurtunity Factors :
These are the factors arises of the internal weaknesses of the in the internal controls of the organisation . For instance if there is no proper audit work is carried by the enterprise then it creates an oppurtunity for the employee to do fraud.
Instances of Oppurtunity Factors :
Fraud detection methods used by the auditors and forensic accountants are as follows :
Forensic experts and the auditors uses inquiry as an investigative tool for fraud detection. Under the inquiry system a direct questioning made to individual in which the people who are more reluctant to provide information will do so.
2. Examination of unauthorised journal entries and other adjustments :
Auditors usually places importance to unauthorised journal entries usually called as top side adjustments. Auditors usually review unusual journal entries to detect journal entries to detect frauds.
3. Indepth internal review of the internal controls of the organisation
4. Examining the accounting estimates for bias