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The seven most popular machine learning algorithms for online fraud detection and their use in SAS

Started ‎07-09-2020 by
Modified ‎07-09-2020 by
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Today, illegal activities regarding online financial transactions have become increasingly complex and borderless, resulting in huge financial losses for both customers and organizations. Many techniques have been proposed for fraud prevention and detection in the online environment. All these techniques have the same goal of identifying and combating fraudulent online transactions. However, each machine learning technique comes with its own characteristics, advantages, and disadvantages. This session reviews the use of the most common machine learning algorithms used in online fraud detection, the strengths and weaknesses of these techniques, and how these algorithms are developed and deployed in SAS®. Types of fraud discussed in this 20-minute video by SAS’ Patrick Maher include credit card fraud, financial fraud, and e-commerce fraud. Algorithms reviewed include neural networks, decision trees, support vector machines, K-nearest neighbor, logistic regression, random forest, and naïve Bayes.



Video highlights

00:36 – Overview

02:18 – Approaches to modeling

03:00 – Machine learning algorithms

12:00 – SAS Visual Data Mining and Machine Learning

13:40 – Algorithm performance

15:52 – Model assessment

16:45 – Model results

17:18 - Insights


Related Resources

Read Patrick’s SASGF paper (proceedings)
Fraud prevention (SAS insights)
SAS Detection and Investigation (product overview)

Version history
Last update:
‎07-09-2020 11:37 AM
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