Live Training – Anomaly Detection & Introduction to Fraud Modelling

Event Serial: WCA508358

Views: 61

08th Feb 2023 To 08th Feb 2023

Live Training – Anomaly Detection & Introduction to Fraud Modelling

Website:https://hubs.li/Q01wJdJy0

Contact Person - Iryna Pidkovych

Event inquiry /Organizer email address - iryna@odsc.com

Organized by: Open Data Science Conference

Topic Covered: Artificial Intelligence

  • Event Start Date - 08th Feb 2023
  • Deadline for abstracts/proposals: 01st Feb 2023
  • Event End Date - 08th Feb 2023

Venue: Virtual , USA

About Event:

Course Overview The Association of Fraud Examiners ACFE consistently estimates that organizations lose approximately 5% of their revenues due to fraud. Based on world GDP estimates, this would be anywhere from $3-4 trillion annually. Fraud is one of the most interesting problems to try and solve because the people in your data are not trying to be found. Data science techniques are now at the forefront of this industry to help fight the battle against criminals. This course outlines the typical fraud framework at an organization and where data science can play a role. It will also lay out how to build an analytically advanced fraud system at an organization. Moving beyond just simple rules and anomaly detection, these supervised and unsupervised approaches to fraud modeling will help an organization combat the every present problem of fraud. These fraud modeling approaches can also be used in other industries to help organizations find unique customers or problems that might exist in their current systems. Learning Objectives - Develop good features recency, frequency, and monetary value as well as categorical transformations for detecting and preventing fraud - Identify anomalies using statistical techniques like z-scores, robust z-scores, Mahalanobis distances, k-nearest neighbors k-NN, and local outlier factor LOF - Identify anomalies using machines learning approaches like isolation forests and classifier adjusted density estimation CADE - Visualize these anomalies identified by the above approaches Speaker: Aric Labarr, PhD Register here https:hubs.liQ01wJdJy0



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