Fraud Detection in R

Overview

Fraud Detection in R

Learn to detect fraud with analytics in R. 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 increase further in the 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. 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.

University:

DataCamp

Provider:

DataCamp

Categories:

R Programming Courses, Anomaly Detection Courses, Data Analysis Courses, Statistical Modeling Courses

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Taught by

Bart Baesens, Sebastiaan Höppner and Tim Verdonck

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Subjects