Fraud Detection Using Descriptive, Predictive, and Social Network Analytics Training

About

A typical organization loses an estimated 5 percent of its yearly revenue to fraud. This course shows how learning fraud patterns from historical data can be used to fight fraud. The course discusses the use of supervised learning (using a labeled data set), unsupervised learning (using an unlabeled data set), and social network learning (using a networked data set). The techniques can be applied across a wide variety of fraud applications, such as insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, and counterfeiting. The course provides a mix of both theoretical and technical insights, as well as practical implementation details.

Learn How To

• Preprocess data for fraud detection (sampling, missing values, outliers, categorization, and so on).
• Build fraud detection models using supervised analytics (logistic regression, decision trees, neural networks, ensemble models, and so on).
• Build fraud detection models using unsupervised analytics (hierarchical clustering, non-hierarchical clustering, k-means, self-organizing maps, and so on).
• Build fraud detection models using social network analytics (homophily, featurization, egonets, PageRank, bigraphs, and so on).

Who Should Attend

Fraud analysts, data miners, and data scientists; consultants working in fraud detection; validators auditing fraud models; and researchers in financial services companies, banks, insurance companies, government institutions, health-care institutions, and consulting firms.

Prerequisites

Before attending this course, you should have a basic knowledge of statistics, including descriptive statistics, confidence intervals, and hypothesis testing.