This video demonstrates a range of techniques used by forensic accounts and fraud examiners to uncover fraudulent journal entries and illegal activities. As data professionals, most of us will never unravel a Bernie Madoff scheme, but we can apply these same techniques in our own environments to learn more about our data. This video will uses the R programming language to apply these fraud detection techniques and help you to gain a better understanding of your data.

You will learn a variety of techniques from this video by which to examine your data and draw inferences that can help you to detect fraud and malfeasance. You'll begin with the use of basic analytical techniques such as including regression analysis. From there, you will learn how to use cohort analysis to find outliers between groups, leading you on a data-driven approach to forensic investigation. Finally, you will review numeric techniques around data set validity, including rules around the distributions of the first and last digits in data sets.

What You Will Learn
Summarize and review a new data set
Perform regression analysis using linear regression
Discover distributions of data, overall and between cohorts
Compare cohort behavior to discover outliers
Use distributions of first and last digits to test data set validity

Who This Video Is For

Data platform specialists and data scientists who are interested in identifying anomalies which may indicate fraud or the opportunity for deeper business insight. Viewers may have some experience with Python or R and some knowledge of statistics, but neither is necessary to get value from the video.