Topic: Fraud & Risk Analytics
Intended Audience: Directors, Analytics Managers, Chief Analytics Officers, Data Scientists and practitioners
Length: 1 Hour
Mitigating fraud is a complex problem and requires a comprehensive and flexible fraud analytics solution. Organizations can be anywhere along the broad spectrum of fraud analytics capabilities, from basic rules and reporting to complex network graph analysis. This webinar will show data science teams how to move to the next stage in the process, ultimately building out all of the components of a holistic and robust fraud analytics platform. At each stage, we will discuss technical details as well as problems to watch out for based on lessons learned over the years.
Attendees will learn:
- How to use unsupervised learning to identify new cases of fraud.
- Common biases and their impact on supervised learning of new cases.
How to incorporate results from different techniques and processes to build an effective fraud platform.
Webinar hosts Mike Thurber (Lead Data Scientist) and Dr. Aric LaBarr (Director and Senior Scientist) draw on their years of analytical experience to map out the key requirements of a holistic fraud analytics platform. With a primary focus on healthcare and insurance, Mike works to deliver information products that drive business value. Dr. LaBarr is passionate about helping organizations use their data assets to solve problems in predictive modeling, advanced analytics, forecasting, and risk management.