Sign up for The Paypers newsletter Follow The Paypers on LinkedIn Follow The Paypers on Twitter Follow The Paypers on Facebook
The Paypers, paypers, Insight in payments, News, Reports, Events
Expert opinion

Why implement a fraud management solution that combines machine learning with rules?

Friday 1 February 2019 | 09:37 AM CET

Mark W. Hall from CyberSource explains how machine learning supports fast detection of emerging fraud patterns, while enabling the injection of human ingenuity

According to artificial intelligence (AI) pioneer Arthur Samuel, machine learning is a ‘field of study that gives computers the ability to learn without being explicitly programmed.’ For fraud management, this means that machine learning can detect subtle emerging fraud patterns that are impossible to see on a human level. Virtually, all fraud management systems today use some form of machine learning, so what sets CyberSource Decision Manager apart?

Importance of data: Decision Manager has had machine learning from the beginning. Decision Manager is the only machine learning fraud solution that draws insights from Visa and CyberSource’s 68B+ annual transactions processed from around the globe. These transactions come from tens of thousands of merchants across a wide variety of industries and specialities. With this depth and breadth of data, it’s like having more high-quality neurons in the machine learning ‘brain.’ It just makes sense that better data leads to better fraud detection decisions.

Why rules are needed: Another very important distinction with Decision Manager is the inclusion of powerful rules, which adds a level of precision control for Risk Analysts. But why are rules important? Let’s explore a theoretical example of what can happen without rules in the following diagram.

Line L1 shows revenue growth before applying a fraud prevention tool. In the diagram, line L1 represents a theoretical revenue growth trajectory.

Line L2 shows fraudulent activity as a percentage of revenue. As revenues grow, if fraud losses are left unchecked, they too would continue to grow as a percentage of revenues, as shown on line L2.
Line T0 represents the point in time when an organisation implements a fraud management solution. Once a business realises they have significant fraud losses, they will institute a fraud management system as shown at time T0.

Line L3 shows the reduced level of fraud by using a fraud management programme. As the fraud management system starts learning from that business’ transaction data, the fraud loss level should gradually reduce as shown on the red line L3.

Line L4 represents the reduced level of revenue due to a poor customer experience while managing fraud. False positives can lead to lost revenues, as shown on the yellow line L4, not only due to the loss of the immediate sale, but even more by potentially losing a customer forever due of the rejected transaction. This has the impact of reducing revenue growth not only by interfering with business one transaction at a time, but tarnishing the experience for a legitimate buyer and compromising the lifetime value of customers.

Line L5 shows what active fraud management can do to restore revenues closer to the theoretical level. By combining rules with good manual review practices, many businesses may actually see an increase in revenue that comes very close to their theoretical revenue trajectory, as seen in the green line L5. Decision Manager’s rules can be configured to activate at a specific time of day or date ranges, which can accommodate a variety of cyclic, seasonal, and periodic sales promotions – helping maximize acceptance rates and revenues.

Rules provide customised control: By instituting rules, a risk analyst can inject human intelligence and set common-sense parameters for their specific business. For instance, if the item being sold is a low priced digital good, like a picture or a song, the risk analyst might have a higher tolerance for the fraud risk score because there is no cost of goods. This is much different than an online retailer of big-ticket luxury items where the cost of goods is high – and there’s an open market for fraudsters to easily turn those goods into cash. Obviously, in the latter case, the risk analyst will want to send questionable transactions to manual review prior to shipment.

The best of both worlds: Decision Manager employs machine learning that operates on insights from 68B+ global Visa and CyberSource processed transactions, enabling fast detection of emerging fraud patterns, while at the same time offering powerful rules that enable the injection of human ingenuity. Machine learning, combined with rules, provides an excellent fraud management solution.

This editorial was first published in the Web Fraud Prevention, Identity Verification & Authentication Guide 2018-2019. The Guide covers some of the security challenges encountered in the ecommerce and banking, and financial services ecosystems. Moreover, it provides payment and fraud and risk management professionals with a series of insightful perspectives on key aspects, such as fraud management, identity verification, online authentication, and regulation.

About Mark W. Hall

Mark is a seasoned entrepreneurial leader who is passionate about crafting multi-channel marketing programmes that communicate differentiation and clarity in the Enterprise B2B space. At CyberSource, Mark heads global cross-functional marketing, positioning, and messaging for the company’s fraud solutions.


About CyberSource

CyberSource is a global, modular payment management platform built on secure Visa infrastructure, with the insights of a USD 427 billion global processing network. It helps businesses enhance their customer experience, grow revenue, and mitigate risk. For more information, visit