Sponsored by: ISBIS
Topic: general/theory
Register for this online course
Instructor
Affiliated with McCoy College of Business, Texas State University, USA
Dr. Tahir Ekin
Tahir Ekin is Steven R. “Steve” Gregg Excellence Endowed Professor of Quantitative Methods in McCoy College of Business, Texas State University. His book Statistics and Health Care Fraud: How to Save Billions was published as part of ASA/CRC Series on Statistical Reasoning in Science and Society. His work has been published in a variety of journals, including International Statistical Review, Applied Statistics and American Statistician. He has given trainings on fraud analytics in workshops sponsored by European Health Care Fraud and Corruption Network, ISI and INFORMS. Dr. Ekin is an elected member of ISI.
Course description
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.
Target Audience
Researchers in financial service companies, banks, insurance companies, government institutions, health care institutions, and consulting firms as well as fraud data analysts/scientists; consultants working in fraud detection. This course is also expected to be of interest to early career statisticians that can gain insights about how different data mining/statistical methods are applied in this emerging crucial subject domain using R.
Syllabus
Consist of 4 sessions:
- Introduction to fraud and data: Statistical fraud assessment and R, Importance of fraud detection, Definition and types of fraud with examples, Fraud data and pre-processing methods
- Descriptive Fraud Analytics: Visualization, Descriptive Statistical Methods, Outlier detection, Clustering, Topic models
- Predictive Fraud Analytics: Classification, Regression; Model Evaluation
- Case Study and Overview: Fraud analytics applications using R, Overview: Future directions and challenges with statistical fraud detection
One option to do these is to have the sessions for 40 minutes, with breaks in between of 5, 10 and 5 minutes respectively, for a total of 3 hours.
Required software
It is highly recommended that the attendees have open source free software R and RStudio installed.