New Fraud Management Tool Uses Machine Learning to Suggest Rules

March 03, 2018
Read time: 2 min
Scott Boding
Scott Boding
Cybersource | Vice President, Product Management

Generate fraud rules based on historical data. It is important for you to monitor your fraud strategy as fraudsters continually change their tactics.

But how do you identify potential new fraud rules based on historical fraud patterns?

Cybersource Rules Suggestion Engine draws on your unique transaction data to automatically present recommended rules that can augment your existing fraud strategies. As well, each suggested rule is accompanied with appropriate metrics to help you measure its performance against the selected transaction data.


Capitalize on machine learning


Cybersource has extended the capabilities of Cybersource Decision Manager beyond transaction scoring by now bringing machine learning into the realm of rules creation. Rules Suggestion Engine uses the outputs of Decision Manager’s advanced machine learning models as inputs into the rule creation process, suggesting new fraud rules to consider. Incorporating machine learning models helps increase the effectiveness of your fraud rules, enabling you to fine-tune your fraud strategies.

Stay one-step ahead


With Rules Suggestion Engine, Cybersource is enhancing the analytical capabilities of Decision Manager beyond Decision Manager Replay. You can help meet the changing requirements to stay one-step ahead of fraudsters and accept more good customer orders.

Test the impact of suggested rules before implementing


Rules Suggestion Engine, in conjunction with Decision Manager Replay enables you to test any proposed rule against historical data. With Decision Manager Replay, you can measure and understand the effectiveness of a particular fraud rule before implementing it in a live environment.

Learn more about our Rules Suggestion Engine and how it can help you optimize your fraud management strategies.