Sunil Kappal

Work place: GH5 and 7/775 Second Floor Paschim Vihar,India, New Delhi-87



Research Interests: Data Structures and Algorithms, Computational Learning Theory


Sunil Kappal works as an Advanced Analytics Consultant. He has more than 21 years of experience in Data Analytics, Business Intelligence, Statistical Modeling, Predictive Models and implementation of Six Sigma Methodologies in various setups. Sunil has also earned a certificate of Distinction in the field of Healthcare Innovation and Entrepreneurship from Duke University of North Carolina, Project Management Specialization from University of California Irvine Extension, Business Analytics Specialization from Wharton School of Business. He has delivered multiple lectures at the University of Texas Dallas on the usage of various advanced analytical techniques and machine learning practices. Sunil was also recently invited as a guest speaker at the Symbiosis Institute of Operations Management to talk on Big Data and Machine Learning.

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

By Sunil Kappal

DOI:, Pub. Date: 8 Jan. 2019

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.

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