Solutions: AI Workflows
Detect and prevent sophisticated fraudulent activities in payments with high accuracy and reduced false positives.
Transaction fraud detection is a $43B problem annually and poses a big challenge for financial institutions to detect and prevent sophisticated fraudulent activities. With NVIDIA’s fraud detection AI workflow, enterprises can augment their fraud detection using deep learning techniques including graph neural networks (GNNs), allowing for real-time analysis and improved accuracy.
The fraud detection AI workflow example demonstrates how the GNN model-building process can be run, the frequency of which is dependent on model training latency.
Given how dynamic the consumer finance industry is with evolving fraud trends, financial institutions that train models adaptively on a frequent basis tend to have better fraud prevention KPIs as compared to their competitors.
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Financial institutions can improve fraud detection by utilizing the NVIDIA AI workflow. Leveraging tools available on NVIDIA AI Enterprise allows fraud detection systems to process data efficiently, make faster decisions without disrupting transaction flow, and reduce the risk of financial losses.
AI workflows accelerate the path to AI outcomes. The fraud detection workflow gives developers a reference solution to start building applications that can improve the customer experience.