Fraud Detection in R

Learn to detect fraud with analytics in R.

4 Hours16 Videos49 Exercises
6,403 Learners

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Course Description

The Association of Certified Fraud Examiners estimates that fraud costs organizations worldwide \$3.7 trillion a year and that a typical company loses five percent of annual revenue due to fraud. Fraud attempts are expected to even increase further in future, making fraud detection highly necessary in most industries. This course will show how learning fraud patterns from historical data can be used to fight fraud. Some techniques from robust statistics and digit analysis are presented to detect unusual observations that are likely associated with fraud. Two main challenges when building a supervised tool for fraud detection are the imbalance or skewness of the data and the various costs for different types of misclassification. We present techniques to solve these issues and focus on artificial and real datasets from a wide variety of fraud applications.
1. 1

Introduction & Motivation

Free

This chapter will first give a formal definition of fraud. You will then learn how to detect anomalies in the type of payment methods used or the time these payments are made to flag suspicious transactions.

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Introduction & Motivation
50 xp
Imbalanced class distribution
100 xp
Cost of not detecting fraud
100 xp
Time features
50 xp
Circular histogram
100 xp
Suspicious timestamps
100 xp
Frequency features
50 xp
Frequency feature for one account
100 xp
Frequency feature for multiple accounts
100 xp
Recency features
50 xp
Recency feature
100 xp
Comparing frequency & recency
100 xp
2. 2

Social network analytics

In the second chapter, you will learn how to use networks to fight fraud. You will visualize networks and use a sociology concept called homophily to detect fraudulent transactions and catch fraudsters.

3. 3

Imbalanced class distributions

Fortunately, fraud occurrences are rare. However, this means that you're working with imbalanced data, which if left as is will bias your detection models. In this chapter, you will tackle imbalance using over and under-sampling methods.

4. 4

Digit analysis and robust statistics

In this final chapter, you will learn about a surprising mathematical law used to detect suspicious occurrences. You will then use robust statistics to make your models even more bulletproof.

Datasets

Chapter 1 datasetsChapter 2 datasetsChapter 3 datasetsChapter 4 datasets

Collaborators

Bart Baesens

Professor in Analytics and Data Science at KU Leuven

Bart Baesens is professor in Analytics and Data Science at the Faculty of Economics and Business of KU Leuven, and a lecturer at the University of Southampton (UK). He has done extensive research on big data & analytics, credit risk analytics and fraud analytics. He regularly tutors, advises and provides consulting support to international firms with respect to their big data, analytics and fraud & credit risk management strategy.
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Sebastiaan Höppner

PhD researcher in Data Science at KU Leuven

Sebastiaan Höppner is a PhD researcher at the Section of Statistics and Data Science of the Departement of Mathematics at KU Leuven (Belgium). His research is mainly focused on developing new statistical tools and machine learning models that are capable of detecting credit transfer fraud.
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Tim Verdonck

Professor at KU Leuven

Tim Verdonck is a professor in Statistics and Data Science at the Department of Mathematics of KU Leuven (Belgium). He is also a visiting professor at the School of Economics, Management and Statistics at the University of Bologna (Italy), where he gives a course in the Master in Quantitative Finance. He is chairholder of the BNP Paribas Fortis Chair in Fraud Analytics, which investigates the use of predictive analytics in the context of payment fraud. Tim Verdonck is also chairholder of the Allianz Chair Prescriptive Business Analytics in Insurance. His research interests are in the development and application of robust statistical methods for financial, actuarial and economic data sets.
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