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Fraud Detection in R
Learn to detect fraud with analytics in R.
- 4 hours
- 16 Videos
- 49 Exercises
- 2,780 Participants
- 3,900 XP
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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.
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1
Introduction & Motivation
FreeThis 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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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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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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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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1
Introduction & Motivation
FreeThis 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.
-
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.
-
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.
-
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.
What do other learners have to say?
“I've used other sites, but DataCamp's been the one that I've stuck with.”
Devon Edwards Joseph
Lloyd's Banking Group
“DataCamp is the top resource I recommend for learning data science.”
Louis Maiden
Harvard Business School
“DataCamp is by far my favorite website to learn from.”
Ronald Bowers
Decision Science Analytics @ USAA
Bart Baesens
Professor in Analytics and Data Science at KU Leuven
Professor Bart Baesens is a professor of Big Data & 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 co-authored more than 250 scientific papers and 10 books some of which have been translated into Chinese, Kazakh and Korean, and sold more than 20,000 copies of these books world-wide. Bart received the OR Society’s Goodeve medal for best JORS paper in 2016 and the EURO 2014 and EURO 2017 award for best EJOR paper. His research is summarized at www.dataminingapps.com. He also regularly tutors, advises and provides consulting support to international firms with respect to their analytics and 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.
See MoreCollaborators
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Hadrien Lacroix
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Sara Billen
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Chester Ismay