Fraud has been around since the early days of commerce, continuously evolving and adapting to changing times. The fraudulent cases are seen in a wide range of domains such as finance, credit card, telecommunications, insurance and health care. Examples include but not limited to the post COVID-19 instances in financial stimulus, unemployment eligibility and health care procurement. For instance, in health care, overpayments are estimated to correspond up to ten percent of total expenditures. This short course presents the use of analytical methods for fraud assessment. Fraud data and its types will be introduced with some examples and pre-processing techniques. Next, the course will cover the use of visualization and unsupervised methods (outlier detection, clustering, topic models) to describe data and reveal hidden relationships. Whereas supervised methods such as classification and regression can be used with labeled data sets for prediction purposes. These methods will be discussed using examples from finance and health care industries. The course will conclude with an overview of applications using R. After completing the course, the attendees will have learnt various types of fraud, and the use of data and statistical methods for fraud detection.