Increasingly corporations conduct business through the use of third parties, service providers and subcontractors. While the operations can be outsourced, the liability cannot. Organizations are responsible for taking reasonable steps to detect
and prevent fraud within outsourced operations (OO). Fraud risk must be assessed without having detailed access to the inner workings and internal controls of OO. Traditional fraud risk assessment methodology cannot be utilized in most cases.
Fraud examiners can use modern data analytics techniques to assess the risk of fraud at OOs.
This session will cover a range of techniques from basic to advanced, including ratio analysis, F-Score and M-Score calculation, discretionary accrual/expense analysis, social network analysis, sentiment analysis, text analytics, and neural network
modelling. It will also briefly discuss the methodology of each technique and demonstrate how they are used to assess the likelihood of fraudulent activity at an organization’s outsourced operations.
You Will Learn How To:
- Effectively manage fraud risks in outsourced operations
- Implement basic and advanced data analytics to help in managing fraud risk
- Apply fraud risk modeling to prevent and detect financial fraud in its early stages
Recommended Prerequisites: None
Field of Study: Auditing