Sunday , December 5, 2021

ACI’s Network Intelligence Technology Takes Aim at Real-Time Payments Fraud

As criminals increasingly target real-time payments networks with fraud scams, ACI Worldwide Inc. on Monday announced the launch of its proprietary Network Intelligence Technology as part of its ACI Fraud Management platform.

The new technology enables banks, processors, acquirers, and networks to augment fraud-prevention strategies by allowing them to share industrywide fraud indicators through machine-learning models. As a result, users will be able to combine custom and proprietary fraud indicators and indicators exchanged from a consortium of industry participants to detect real-time payments fraud without exposing raw data. 

In addition, users will be able to incorporate fraud indicators from third-party fraud-intelligence sources and choose which insights to adopt into models and which to contribute to community findings. Other features include the ability to leverage individual fraud indicators and understand localized threats before they become endemic, according to ACI

Since the onset of the Covid-19 pandemic, criminals have been targeting consumers using real-time payments channels through phishing and social-engineering scams. In the United Kingdom, for example, authorized push-payment fraud increased 71% during the first half of 2021, overtaking card fraud rates for the first time, according to ACI. The level of fraud in the U.K. now poses a national-security threat, according to UK Finance, a trade association for banks and financial-services providers. 

“ACI’s unique Network Intelligence Technology adds another layer to” ACI’s fraud-prevention capabilities, Cleber Martins, head of payments intelligence and risk solutions at ACI, says in a prepared statement. “New ways of industry collaboration are crucial for improving fraud prevention. ACI’s unique network-intelligence approach will help financial institutions and payment providers to develop macro insights and beat fraudsters with valid models that are weighted appropriately across all parties.”

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