Question

1. What is an example of a fraud that occurred on a company that has an audit department?

What controls were circumvented? How was the fraud detected?  

3. What is the role of internal audit in fraud prevention and detection, beyond the Standards?. Should internal audit have a greater role in fraud detection?

4. What would be examples of how data analytics that can be used in fraud prevention and detection.

Answer 1

(1) An example of a fraud that ocurred in a  company that had an audit department is "Satyam Scandal(2009)".

This scandal was done by Founder/ Chairman Ramalinga Raju. This fraud was about corporate governance and fraudulent audit practices. This fraud was done in collusion with auditors and chartered accountants. In this case the accounts were misrepresented to show wrong figures to board, stock exchange , shareholders and investors. The accounts were inflated by $1.5 billion. Fake bills were raised. This fraud was detected when Mr. Raju sent an email to stock exchange and SEBI . where he admitted to inflating cash and bank balances of the company.

(3) Role of internal audit in fraud prevention and detection, beyond the standards:

• Minimizing the risk of fraud.
• Improve organizational efficiency and effectiveness.
• Social roles.

Internal audit should have a greater role in fraud detection as internal audit is to provide assurance that an organisation risk management , governance and internal control are effective

(4) examples of how data analytics :

• Financial Institutions mainly depends on data analytics to reduce fraud.machine learning and prediction give notifications of transactions that is different from the norms,it thus helps to detect to curb fraud before it becomes extensive and damages.
• Merchandise returns comprise billions of dollars every year for retailers, and a substantial percentage of them could be fraudulent. Retailers including Best Buy, Amazon and L.L. Bean have started using data analytics to uncover cases where a consumer might be wrongfully benefiting from an extremely liberal return policy.
• Data analytics could help in cases by examining the approval timelines for similar generic drugs and contrasting them with a medication awaiting approval. If the process seems unusually long, investigators might realize it's time to take a closer look at what's causing the slowdowns.