In this chapter, an intelligent fraud and anomaly detection system using statistical, supervised, and unsupervised machine learning techniques is presented. The following sections will show how a non-traditional approach can be leveraged by internal auditors to gain additional insight into the data. The chapter is organized like a recipe – goal of the recipe, ingredients, instructions, and variation and serving. The accompanying code is available at the GitHub repository specified in Chapter 20.
© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2022
M. SekarMachine Learning for Auditorshttps://doi.org/10.1007/978-1-4842-8051-5_2121. Fraud and Anomaly Detection
Maris Sekar1
(1)
Calgary, AB, Canada
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