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Course

Fraud Detection in R

IntermediateSkill Level

4.8+

Updated 08/2024

Learn to detect fraud with analytics in R.

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RMachine Learning4 hr16 videos49 Exercises3,900 XP7,485Statement of Accomplishment

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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.

Prerequisites

Unsupervised Learning in R Supervised Learning in R: Classification

1

Introduction & Motivation

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.

Introduction & Motivation

Imbalanced class distribution

Cost of not detecting fraud

Time features

Circular histogram

Suspicious timestamps

Frequency features

Frequency feature for one account

Frequency feature for multiple accounts

Recency features

Recency feature

Comparing frequency & recency

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.

Social network analytics

Analyzing a network

Overlapping edges

Fraud and social network analysis

Looking for homophily in a network

Visualizing node attributes

Social network based inference

Relational vs non-relational models

Relational neighbor classifier

Social network metrics

Degree, closeness & betweenness

Adding network features

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.

Dealing with imbalanced datasets

How to deal with class imbalance?

Visualizing patterns in the data

Random over-sampling

Random under-sampling

Shrinking the majority group

Combining ROS & RUS

Synthetic Over-sampling

Have you met SMOTE?

From dataset to detection model

Build your own detection model

True cost of fraud detection

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.

Digit analysis using Benford's law

Benford's Law for first digit

Conformity of census data

Benford's Law for fraud detection

Conformity to Benford's Law

Fire insurance claims

Payments data set

Detecting univariate outliers

Computing robust z-scores

Detecting multivariate outliers

Multivariate outlier detection

Fraud Detection in R

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FAQs

What approaches to fraud detection does this course cover?

You learn robust statistics, digit analysis using Benford's Law, social network analytics based on homophily, and techniques for handling imbalanced datasets common in fraud scenarios.

How does the course handle the challenge of imbalanced fraud data?

Chapter 3 is dedicated to imbalanced class distributions and teaches over-sampling and under-sampling methods to prevent your detection models from being biased by rare fraud cases.

What prerequisites do I need for this fraud detection course?

You need introductory and intermediate R skills plus experience with both supervised classification and unsupervised learning in R before taking this course.

Does this course use real fraud datasets?

You work with both artificial and real datasets from a variety of fraud applications, giving you practical exposure to different types of fraudulent activity patterns.

What is the network analysis approach to fraud detection?

Chapter 2 teaches you to visualize transaction networks and apply the sociological concept of homophily to identify fraudulent transactions by analyzing connections between entities.

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