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ACI Worldwide launches ACI Fraud Scoring for financial institutions

By Edlyn Cardoza

March 10, 2022

  • ACI Worldwide
  • Financial Crime
  • Financial Institutions
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 ACI Worldwide, Real Time Payments Software, Fraud Scoring Services, Fraud Management, Financial Institutions, Incremental Learning technology, Financial Crime, USAACI Worldwide, a global leader in mission-critical, real-time payments software, recently announced the launch of Fraud Scoring Services— a fraud scoring platform delivering next-generation machine learning capabilities for financial institutions of all sizes to deliver real-time fraud detection and prevention.

Underpinned by ACI’s patented Incremental Learning technology, ACI Fraud Scoring Services (FSS) can enable banks to reduce fraud losses by up to 75%. The service is being rolled out in North America and Europe first, with plans to expand globally in the coming months.

“We are excited to launch Fraud Scoring Services as part of ACI’s layered approach to machine learning,” said Cleber Martins, head of Payments Intelligence and Risk Solutions, ACI Worldwide. “In today’s real-time environment, machine learning is crucial as part of an effective fraud prevention operation, but developing, managing and staying up-to-date with machine learning strategies is a challenge for most businesses. ACI’s new managed service enables financial institutions of all sizes to access state-of-the-art artificial intelligence capabilities for a fraction of the cost, making fraud prevention more inclusive and helping to combat fraud more effectively.”

Incremental learning technology is an integral part of ACI Fraud Management, ACI’s enterprise fraud management and prevention solution. The solution offers advanced machine learning and behavioural biometrics capabilities, predictive analytics, expertly defined rules, and ACI’s Network Intelligence Technology to help banks identify and mitigate financial crime.

Incremental learning considerably enhances fraud protection for merchants and financial institutions. While traditional machine learning models need to be ‘retrained’ as fraud patterns change, models using incremental learning make minor adjustments on an ongoing basis, allowing the model to adapt itself in production when new behaviours are observed.

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