Deplyoing advance data analytics techniques with conversational analytics outputs for fraud detection

This paper outlines the application of various classification methods and analytical techniques to identify a potential fraud. The aim of this document is to showcase the usefulness of such classification and analytical techniques for fraud detection. Considering the fact that there are hundreds of statistical methods and procedures to perform such analysis. In this paper, I would like to present a hybrid fraud detection method by using the Bayesian Classification technique to identify the risk group; followed by Benford's Law (The Law of First Digit) to detect a fraudulent transaction done by the identified risk group. Though this analysis focuses on the healthcare dataset, however, this technique can be replicated in any industry setup. Also, by adding the Voice of the Customer data to these classification and statistical methods, makes this analysis even more powerful and robust with improved accuracy.