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Fraud Analytics

In this course, participants learn the essentials of fraud analytics.

About This Course

The ACFE, or Association of Certified Fraud Examiners, estimates that a typical organization loses 5% of its revenues to fraud each year. In this course, participants learn how to use analytics in the fight against fraud. We start by setting the stage and review the importance of fraud, its definition, some examples, challenges and approaches. For the data preprocessing part, we refer to our Machine Learning Essentials course. Feature engineering is discussed next. This is a very important step to boost the performance of your analytical fraud models. We then give an overview of various methods to deal with imbalanced data sets, a very typical problem in fraud detection since usually only a few transactions are fraudulent. We briefly elaborate on supervised learning for fraud detection (see our Machine Learning Essentials course for more details). Unsupervised learning for fraud detection or anomaly detection is covered next. The course concludes by extensively discussing social networks for fraud detection.

The course provides a sound mix of both theoretical and technical insights, as well as practical implementation details. These are illustrated by several real-life case studies and examples. The course also features code examples in R. Throughout the course, the instructors also extensively report upon their research and industry experience.

The course features more than 3 hours of video lectures, more than 50 multiple choice questions, and references to background literature. A certificate signed by the instructors is provided upon successful completion.

See Homophily in Social Networks for Fraud Detection to get a free teaser of the course contents.

We can also come and teach this course on-site in classroom format. If interested, please mail us at:


The enrollment fee for this course is EUR 250 (VAT excl.) per participant. Payments are securely handled by PayPal. If you are a company in the European Union, then we can apply VAT reverse charge. For this, please mail your VAT number to Part of our course revenue is used towards funding organizations involvement in protecting and cleaning our oceans. See our about page to learn more about our mission statement.

After enrollment, participants will get 1 year unlimited access to all course material (videos, R/Python scripts, quizzes and certificate).


Before subscribing to this course, you should have a basic understanding of descriptive statistics (e.g., mean, median, standard deviation, histograms, scatter plots, etc.) and inference (e.g., confidence intervals, hypothesis testing). You should also have followed and completed our Machine Learning Essentials course. Previous R knowledge is helpful but not necessary.

Course Outline

  • Introduction
    • Instructor team
    • Our Fraud Analytics Publications
    • Course Outline
    • Software
    • R tutorial
    • Disclaimer
  • Fraud Analytics: Setting the Stage
    • Fraud in Numbers
    • Defining Fraud
    • Fraud Triangle
    • Fraud Management Cycle
    • Examples
    • Credit Card Fraud
    • Fraud Analytics KPIs
    • Fraud Analytics Challenges
    • Fraud Analytics Approaches
    • Expert-Based Approach
    • Fraud Analytics Process Model
    • Quiz
  • Data Preprocessing
  • Feature Engineering
    • Feature engineering
    • Time feature
    • Time feature in R
    • Recency feature
    • Monetary feature
    • Featurization
    • Quiz
  • Dealing with Imbalanced Data Sets
    • Imbalanced data sets
    • Random undersampling
    • Random oversampling
    • Example
    • SMOTE
    • Adjusting posterior probability estimates
    • Dealing with Imbalanced Data Sets in R
    • Quiz
  • Supervised Learning for Fraud Detection
  • Unsupervised Learning for Fraud Detection
    • Unsupervised Learning for Fraud Detection
    • Benford’s law
    • Benford’s Law in R
    • Graphical Outlier Detection Techniques
    • Grubbs test
    • Univariate Anomaly Detection using Robust Statistics
    • Multivariate Anomaly Detection using Robust Statistics
    • Multivariate Anomaly detection using Robust Statistics in R
    • Clustering
    • Breakpoint analysis
    • Peer group analysis
    • Association rules
    • Isolation forest
    • Isolation Forest in R
    • One-class SVMs
    • Autoencoders
    • Autoencoders in R
    • Quiz
  • Social Networks for Fraud Detection
    • Social Network Applications
    • Social Networks for Fraud Detection
    • Social Networks
    • Representing Social Networks
    • Network Centrality Measures
    • Community Mining
    • Graph Partitioning Approaches
    • Girvan Newman algorithm
    • Bottom Up Community Mining
    • Modularity Q
    • Homophily
    • Social Network Based Predictive Analytics
    • Relational Neighbor classifier
    • Probabilistic Relational Neighbor Classifier
    • Relational Logistic Regression
    • Social Network Featurization
    • Collective Inference
    • Gibbs Sampling
    • Iterative Classification
    • PageRank
    • From Unipartite towards Bipartite Networks
    • Featurizing a Bigraph
    • Propagation in bipartite graphs
    • Multipartite graphs
    • Gotcha!
    • BiRank
    • Representation Learning
    • Quiz

    Course Staff

    Prof. dr. Bart Baesens

    Prof. dr. Bart Baesens

    Bart was born in Bruges (West Flanders, Belgium) on February 27th, 1975. He speaks West-Flemish (which he is very proud of!), Dutch, French, a bit of German, some English and can order a beer in Chinese. He is married to Katrien Denys and has 3 kids (Ann-Sophie, Victor and Hannelore), and 2 cats (Felix and Simba). Besides enjoying time with his family, he is also a diehard Club Brugge soccer fan. Bart is a foodie and amateur cook. He loves drinking a good glass of wine (his favorites are white Viognier or red Cabernet Sauvignon) either in his wine cellar or when overlooking the authentic red English phone booth in his garden. His favourite pub is “In den Rozenkrans” in Kessel-Lo (close to Leuven) where you will often find him having a Gueuze Girardin 1882 or Tripel Karmeliet with a spaghetti of the house. Bart loves traveling and his favorite cities are: San Francisco, Sydney and Barcelona. He is fascinated by World War I and reads many books on the topic. He is not a big fan of being called professor Baesens (or even worse, professor Baessens), shopping (especially for clothes or shoes), pastis (or other anise-flavored drinks), vacuum cleaning (he can’t bare the sound), students chewing gum during their oral exam of Credit Risk Modeling (or had garlic for breakfast), long meetings (> 30 minutes), phone calls (asynchronous e-mail communication is a lot more efficient!), admin (e.g., forms and surveys) or French fries (Belgian fries are a lot better!). He is often praised for his sense of humor, although he is usually more modest about this. Bart is also a professor of Big Data and Analytics at KU Leuven (Belgium) and a lecturer at the University of Southampton (United Kingdom). He has done extensive research on Big Data & Analytics, Credit Risk Modeling, Fraud Detection and Marketing Analytics. He has written more than 250 scientific papers, some of which have been published in well-known international journals (e.g., MIS Quarterly, Machine Learning, Management Science, MIT Sloan Management Review and IEEE Transactions on Knowledge and Data Engineering) and presented at top international conferences (e.g., ICIS, KDD, CAISE). He has received various best paper and best speaker awards. Bart is the author of 8 books: Credit Risk Management: Basic Concepts (Oxford University Press, 2009), Analytics in a Big Data World (Wiley, 2014), Beginning Java Programming (Wiley, 2015), Fraud Analytics using Descriptive, Predictive and Social Network Techniques (Wiley, 2015), Credit Risk Analytics (Wiley, 2016), Profit Driven Business Analytics (Wiley, 2017), Web Scraping for Data Science with Python (Apress, 2018), and Principles of Database Management (Cambridge University Press, 2018). He sold more than 25.000 copies of these books worldwide, some of which have been translated in Chinese, Russian and Korean. His research is summarized at For an overview of the courses he is teaching, see He also regularly tutors, advises and provides consulting support to international firms regarding their big data, analytics and credit risk management strategy.

    Prof. dr. Tim Verdonck

    Prof. dr. Tim Verdonck

    Tim was born in Merksem (Antwerp, Belgium) on February 19, 1983. He lives together with his girlfriend Nuria Baeten, his daughter Oona, his dog Ragna and two cats Nello and Patrasche (the names of the cats come from the novel A Dog of Flanders, which takes place in Hoboken and Antwerp, see He lives in Wilrijk (Antwerp, Belgium) and enjoys relaxing in his garden with his family. He loves travelling and his favorite cities are Barcelona and Vancouver. On holidays, he likes to dive (his favorite place is Sipadan in Malaysia), snowboard and wakeboard. His other favorite sports are tennis and football.

    Tim Verdonck is also a professor of Statistics and Data Science at the Department of Mathematics of University of Antwerp (Belgium). He is affiliated to KU Leuven and has been an invited professor at the University of Bologna, teaching advanced non-life insurance in the Master of Quantitative Finance. He is chairholder of the BNP Paribas Fortis Chair on Fraud Analytics, the Allianz Chair on Prescriptive Business Analytics in Insurance and the BASF Chair on Robust Predictive Analytics. Tim has a degree in Mathematics and a PhD in Science: Mathematics, obtained at the University of Antwerp. During his PhD he successfully took the Master in Insurance and the Master in Financial and Actuarial Engineering, both at KU Leuven. His research interests are in the development and application of robust statistical methods for financial, actuarial and economic data sets. He is associate editor of Statistics: A Journal of Theoretical and Applied Statistics (Taylor & Francis) and Computational Statistics & Data Analysis (Elsevier). Tim is co-organizer of the Data Science Meetups in Leuven and managing partner at Boltzmann (, a team of experts in machine learning that transform data into actionable insights.