Deplyoing Advance Data Analytics Techniques with Conversational Analytics Outputs for Fraud Detection

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Sunil Kappal 1,*

1. GH5 and 7/775 Second Floor Paschim Vihar,India, New Delhi-87

* Corresponding author.


Received: 11 Oct. 2017 / Revised: 23 May 2018 / Accepted: 19 Jul. 2018 / Published: 8 Jan. 2019

Index Terms

Data Mining, Benford’s Law, Bayesian Classification Method, Conversational Analytics, Interaction Analytics


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.

Cite This Paper

Sunil Kappal,"Deplyoing Advance Data Analytics Techniques with Conversational Analytics Outputs for Fraud Detection", International Journal of Mathematical Sciences and Computing (IJMSC), Vol.5, No.1, pp.42-52, 2019. DOI: 10.5815/ijmsc.2019.01.04






[5] ( H. Zhang (2004)

[6]'s_Law_to_  Assist_in_Detecting_Fraud_in_Accounting_Data

[7] (Fletcher Lu and J  Efrim Boritz)

[8] (Sai Kiran, Jyoti Guru, Rishabh Kumar, Naveen Kumar, Deepak Kataria, Maheshwar Sharma

[9] (Carolyne Milgo)