Using Analytics to Detect Fraud at Outsourced Operations.
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 OO’s. This presentation will cover a range of techniques from basic to advanced. Examples of techniques that will be discussed (in order of complexity):
F-Score and M-Score calculation
Discretionary Accrual /Expense Analysis
Social Network Analysis
Neural Network Modelling
We will 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.
Become familiar with managing fraud risks in outsourced operations Learn how basic and advanced data analytics help in managing fraud risk See examples of fraud risk modeling and how it can be used to prevent and detect financial fraud in early stages