Learn about Data Analytics and Fraud Detection in a Free GGU Seminar

Sia Zadeh

GGU Prof. Sia Zadeh, PhD, and Navin Sinha, CEO of Double Check Consulting, are conducting a seminar that will examine the analytical tools and methods that the industry can use to discern fraudulent insurance claims from legitimate claims. Analytics for Fraud Detection: Car Injury Fraud and AML Predictive Analytics takes place on Monday, December 11, 2017 from 5:00 PM to 6:40 PM at GGU. You must register to attend in-person (Room 5224) or online.

Standard medical diagnostic processes and insurance codes can be used to file fraudulent claims in the property and casualty insurance industry. False claims cause significant distortion of the system and process, leading to revenue loss, increased cost of legitimate claims, and undue delay in the process. Normal claim processing systems often do not have the capability to tell a false claim from a legitimate claim. This requires human intervention and adds significant cost to the process.

Analysis of Big Data can often lead to the detection of unusual trends, raising flags and alerting claim administrators to potential fraud. Data analytics skills – namely application of statistical analysis to large data sets using tools and languages such as Python and R – are required for analyzing data and identifying these trends. For example, Centers for Medicare & Medicaid Services (CMS), successfully uses data analytics to identify and prevent billions of dollars in fraudulent claims every year – as do all insurance companies.


Analytics for Fraud Detection: Car Injury Fraud and AML Predictive Analytics

Monday, December 11, 2017
5:00 PM to 6:40 PM
Room 5224 and online*
REGISTER NOW >>


*In-person and online attendance for students: If you are registered for on-campus courses, you should participate in an on-campus seminar unless the venue is sold out. Ageno graduate students have priority for in-person registrations, but space is limited.

Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.