Advanced Machine Learning Technology to Efficiently Detect and Prevent Fraud
Secure and protect customers with powerful machine learning capabilities, part of the ACI Fraud Management for Banking and ACI Fraud Management for Merchants solutions. Guided and automated machine learning serves as part of a multi-layered fraud strategy for merchants and is specifically designed for rapid deployment and ease of use by business users to accelerate the benefits of machine learning.
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Rapidly Deploy Machine Learning for Banking Fraud Prevention
Many financial institutions struggle with artificial intelligence projects that necessitate big data consolidation into lakes and warehouses. ACI's model generator capabilities enable banks, processors, acquirers and networks to rapidly create and adapt predictive machine learning models and business rules, leveraging data directly from payment flows.
- Create and maintain predictive machine learning models within minutes
- Launch as a fully certified private offer via the Microsoft Azure Marketplace for automated deployment
- Streamline deployment of fraud strategies
- Gain easy access to examine and analyze data, calculate scenarios and document key modeling steps
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Access Powerful Community Models Tailored to Your Merchant Segment
Get customized models and a vast array of community data to accelerate and improve the efficiency of your fraud management.
- Benefit from network fraud intelligence and in-depth sector expertise of our data scientists and fraud analysts
- Leverage models customized to your business or tailored to retail, travel, telecommunications and QSR sectors
- Deploy machine learning models as part of a multi-layered approach to online and mobile fraud prevention
- Build customer profiles, spot emerging fraud signals and benefit from patented incremental learning technology that increases the lifespan of your models
Create Operational Efficiencies in Fraud Prevention
Banks, processors, acquirers and payment networks need to balance preventing confirmed fraud with reducing false positives. This necessitates modern technologies to enhance fraud strategies without increasing manual reviews.
- Leverage available data in real time to pinpoint assertive and strong signals of fraud
- Use network intelligence capabilities to collaborate within the community and build hybrid machine learning models
- Enforce effective methodology while resolving deployment, maintenance, PCI, data access and highly specialized skill needs
Everlink launched a powerful machine learning model in just one day >
Empower Business Users with Transparency and Insight
Traditional machine learning technologies required specialist data science skills, preventing business users from directly inputting fraud prevention expertise or extracting data. ACI Model Generator puts the power back into the hands of the business.
- Create features, define coverage metrics and select candidate features in the workbench with automated analytics and results publishing
- Comprehensively assess models with intuitive graphics
- Sustain relevance with static and adaptive features individually configured to maintenance plans
- Build further models from a self-maintainable features library
- Benchmark findings over an outside window and publish results with automated model training
- Adjust models in real time with control over incoming financial and non-financial events
Explore ACI's adaptive analytics machine learning model generation >
The Six-Step Guide to Leveraging Machine Learning for Payments Intelligence
How to utilize the smartest solutions to reduce fraud and improve customer experience.
Leveraging Machine Learning for Fraud Prevention
Machine learning can be leveraged as part of a multi-faceted fraud prevention strategy to derive payments intelligence that improves the end-to-end customer experience.
Incremental Learning: Adaptive Cruise Control for Fraud Prevention
ACI’s patent-pending incremental learning technology adapts to new consumer behaviors, automatically adjusting your fraud prevention models to keep them running at their peak for longer.