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Fraud Detection in R

Learn to detect fraud with analytics in R.

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4 Hours16 Videos49 Exercises6,271 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.

    Play Chapter Now
    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.

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    Social network analytics
    50 xp
    Analyzing a network
    100 xp
    Overlapping edges
    100 xp
    Fraud and social network analysis
    50 xp
    Looking for homophily in a network
    100 xp
    Visualizing node attributes
    100 xp
    Social network based inference
    50 xp
    Relational vs non-relational models
    50 xp
    Relational neighbor classifier
    100 xp
    Social network metrics
    50 xp
    Degree, closeness & betweenness
    100 xp
    Adding network features
    100 xp
  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.

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    Dealing with imbalanced datasets
    50 xp
    How to deal with class imbalance?
    50 xp
    Visualizing patterns in the data
    100 xp
    Random over-sampling
    100 xp
    Random under-sampling
    50 xp
    Shrinking the majority group
    100 xp
    Combining ROS & RUS
    100 xp
    Synthetic Over-sampling
    50 xp
    Have you met SMOTE?
    50 xp
    SMOTE
    100 xp
    From dataset to detection model
    50 xp
    Build your own detection model
    100 xp
    True cost of fraud detection
    100 xp
  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.

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    Digit analysis using Benford's law
    50 xp
    Benford's Law for first digit
    100 xp
    Conformity of census data
    100 xp
    Benford's Law for fraud detection
    50 xp
    Conformity to Benford's Law
    50 xp
    Fire insurance claims
    100 xp
    Payments data set
    100 xp
    Detecting univariate outliers
    50 xp
    Computing robust z-scores
    100 xp
    Boxplot
    100 xp
    Detecting multivariate outliers
    50 xp
    Multivariate outlier detection
    100 xp

Datasets

Chapter 1 datasetsChapter 2 datasetsChapter 3 datasetsChapter 4 datasets

Collaborators

Hadrien Lacroix
Sara Billen
Chester Ismay

Prerequisites

Unsupervised Learning in RSupervised Learning in R: Classification
Bart Baesens HeadshotBart 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 HeadshotSebastiaan 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 HeadshotTim 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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