Machine Learning layers artificial intelligence on GBG Digital Risk Management and Intelligence Platform, to help detect evolving and complex financial crimes, protecting businesses from fraud losses and enabling frictionless customer experience in the digital onboarding and transaction process.
Complement rule-based detection to improve fraud detection accuracy.
Continuous learning and automated model update to identify new fraud patterns.
Improve performance and support regulatory compliance.
Machine learning complements rule-based systems to strengthen fraud detection capability by identifying new complex financial crimes that may be missed, improving accuracy by up to 30%.
Using leading algorithm choice for fraud detection models, such as random forest, gradient boosting machine and neural networks, Machine Learning models can be built and trained by leveraging past and existing data, and be easily deployed to production.
Machine Learning model continues to learn and update its detection capability automatically based on newly identified fraudulent behavioural and outcome patterns.
User-controlled design enables model performance fine-tuning; and the building, training and throttling of score threshold to balance alert trigger rate, facilitating better focus and management of fraud investigation resource.
Access to sample data, modelling parameters, contributing features and audit log helps to support regulatory reporting and comply to governance requirements.
Find out how GBG Machine Learning Module can help you improve fraud detection rates and operational efficiency.