GBG's Machine Learning Module helps you improve financial crime detection in the onboarding process. By leveraging your own historical customer data, you can train neural networks to detect financial crime that has not been identified by rules or scorecards, improving detection rates and operational efficiency. Build and train machine learning models that can identify the most complex financial crime using cutting edge artificial intelligence, known as deep learning.
Identify bad actors quickly and accurately, thereby speeding up onboarding.
Improve operational efficiency by reducing false positives by 30%
Advanced analytics increases fraud detection and lowers alert rate leading to improved ROI.
With the Machine Learning Module, models can be utilised in conjunction with rules to identify bad actors with 20% more accuracy, reducing the number of referrals thereby speeding up onboarding and enabling a more frictionless experience.
Enjoy flexibility in analysis and feature definition with an easy to use setup wizard. You can create one or multiple models that can be utilised at different stages in the workflow, seamlessly integrating with rule alerts to provide a fraud rating for the customer.
With advanced analytics, the Machine Learning Module can identify more fraud, with a much lower alert rate. This can deliver a large improvement in ROI and manageable workload and efficiency for investigators.
Build and manage your own machine learning model. The module provides continuous learning through investigator feedback loop and offers data enrichment with Orchestration connectors. It can be combined with Instinct rules and matching to offer complete detection control.
Find out how GBG Machine Learning Module can help you improve fraud detection rates and operational efficiency.