Business Knowledge Series course

Presented by Bart Baesens, Ph.D. Professor at KU Leuven (Belgium), and lecturer at the University of Southampton (UK); or Christophe Mues, Ph.D., Professor at the School of Management of the University of Southampton (UK); or Cristian Bravo, Ph.D., Associate Professor, Business Analytics, University of Southampton (UK); or Wouter Verbeke, Ph.D., Assistant Professor, Business Informatics, University of Brussels (Belgium); or Stefan Lessmann, Ph.D., Professor, School of Business and Economics, Humboldt University (Germany); or Tim Verdonck is Professor of Statistics and Data Science at the Department of Mathematics of University of Antwerp (Belgium)

Formats available	Duration
Classroom:	2.0 days
Live Web Classroom:	4 half-day session(s)	System Requirements
e-Learning:	21 hours/180 day license	System Requirements

This course addresses SAS Enterprise Miner software.

• the importance of fraud detection
• defining fraud
• anomalous behavior
• fraud cycle
• 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

• motivation
• types of variables
• sampling
• visual data exploration
• missing values
• outlier detection and treatment
• standardizing data
• transforming data
• coarse classification and grouping of attributes
• recoding categorical variables
• segmentation
• variable selection

• target definition
• linear regression
• logistic regression
• decision trees
• ensemble methods: bagging, boosting, random forests
• neural networks
• dealing with skewed class distributions
• evaluating fraud detection models

• unsupervised learning
• clustering approaches: hierarchical clustering, k-means clustering, self-organizing maps
• peer group analysis
• break point analysis

• social networks and applications
• is fraud a social phenomenon?
• social network components
• visualizing social networks
• social network metrics
• community mining
• social network based inference (network classifiers and collective inference)
• from unipartite toward bipartite graphs
• featurizing a bigraph
• fraud propagation
• case study

• quantitative monitoring: backtesting, benchmarking
• qualitative monitoring: data quality, model design, documentation, corporate governance