A typical organization loses an estimated 5 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. During the course, the instructor reports extensively on his recent research insights about the topic. Various real-life case studies and examples are presented for further clarification.
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 Can Benefit
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, healthcare institutions, and consulting firms
Before attending this course, you should have a basic knowledge of statistics, including descriptive statistics, confidence intervals, and hypothesis testing.
the importance of fraud detection
types of fraud
examples of insurance fraud and credit card fraud
key characteristics of successful fraud analytics models
fraud detection challenges
approaches to fraud detection
types of variables
visual data exploration
outlier detection and treatment
coarse classification and grouping of attributes
recoding categorical variables
Supervised Methods for Fraud Detection
ensemble methods: bagging, boosting, random forests
https://www.exitcertified.com/training/sas/sas-solutions/fraud-security-intelligence/fraud-detection-using-supervised-social-network-39050-detail.htmlBFRSUSNFraud Detection Using Descriptive, Predictive, and Social Network Analyticshttps://assets.exitcertified.com/assets/CourseImages/844fc4b411/AdobeStock_237183950__FitMaxWzEwMDAsMTAwMF0.jpg1700.00USDInStock/Training/SAS/SAS Solutions/Fraud and Security Intelligence A typical organization loses an estimated 5 of its yearly revenue to fraud. This course shows how learning fraud patterns from...1700.00SASClassroom2015-06-15T15:34:27+00:00USD