Understanding Machine Learning and Fraud Detection

Fraud and Financial Crime

EmilyRodenhuis HeadshotwFilter

Emily Rodenhuis

Nov 19, 2018

You’ve no doubt noticed that the buzz about machine learning and fraud detection has been everywhere. Finally it appears as though there’s a solution to combat the fiercely aggressive payment fraud problem organizations have been faced with for years. It’s come just in time, too -- according to the Association of Certified Fraud Examiners, the typical organization loses 5% of its revenue to fraud in any given year.

But how exactly is machine learning used to detect and prevent fraud and what does it’s use mean for your organization?

The recent whitepaper “The Impact of Machine Learning on Fraud Detection” answers that question and more, providing an overview of machine learning for companies just beginning the process of investigating it as a fraud prevention tool.

To start, the paper discusses in plain language what machine learning really means to the practice of fraud detection and investigation:

“When taken in the simplest terms, machine learning algorithms provide tools to automate fraud analysis processes. In the same way that a human would analyze transactional data, machine learning algorithms look at data, identify patterns, and raise a flag when an outlier is identified.”

The Impact of Machine Learning on Fraud

It also goes into detail about how machine learning can supplement a traditional, rules-based fraud detection methodology, how it can bring out the unique value in data to make fraud detection faster and more accurate and even provides guidance on what characteristics organizations should focus on when looking for an effective fraud solution, such as the ability to:

• Identify known ‘bad behaviors’ through rules based on payment and fraud industry expertise
• Continuously identify hidden insights about fraud indicators in transactional data through unsupervised machine learning
• Utilize investigator feedback to consistently classify alerts based upon the likelihood to be a ‘true positive’ through supervised machine learning models, reducing false positives

To learn more about the impact of machine learning on fraud detection, check out the full whitepaper.

Related topics

EmilyRodenhuis HeadshotwFilter

Posted by

Emily Rodenhuis

Emily Rodenhuis is a creative marketing writer specializing in content creation. Her work has been featured by BankNews, InfoSecurity, AFP magazine and more.
Browse all posts
footer curve