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.
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.
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.
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.
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.
Professor in Analytics and Data Science at KU Leuven
PhD researcher in Data Science at KU Leuven
Professor at KU Leuven
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Lloyds Banking Group
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Harvard Business School
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