Fraud Detection Using Descriptive, Predictive, and Social Network Analytics (E-learning)

📅 Self-Paced E-learning course
🌍 English

Overview

Business Knowledge Series course

Presented by Bart Baesens, Ph.D. Professor at the School of Management of 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, Assistant 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)

This E-learning course will show how learning fraud patterns from historical data can be used to fight fraud. To be discussed is the use of descriptive analytics (using an unlabeled data set), predictive analytics (using a labeled 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, counterfeit, etc. The course will provide a mix of both theoretical and technical insights, as well as practical implementation details. The instructor will also extensively report on his recent research insights about the topic. Various real-life case studies and examples will be used for further clarification.  The E-learning course consists of more than 20 hours of movies, each approximately 5 minutes on average.  Quizzes are included to facilitate the understanding of the material. Upon registration, you will get an access code which gives you unlimited access to all course material (movies, quizzes, scripts, ...) during 6 months year. The E-learning course focusses on the concepts and modeling methodologies and not on the SAS software!  To access the course material, you only need a laptop, iPad, iPhone with a web browser. No SAS software is needed.

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).

Course Outline

Introduction

• 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

Data Preprocessing

• 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

Supervised Methods for Fraud Detection

• 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 Methods for Fraud Detection

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

Social Networks for Fraud Detection

• 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

Fraud Analytics: Putting It All to Work

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