AI for fraud detection uses many machine learning models to detect client behavior, connection irregularities, and fraudulent account and activity patterns
Fraudsters can create contextual emails without typos or grammar issues using human-like LLM writing. Generative AI lets crooks create several phony emails
Deep fake technology can clone a customer’s voice if an attacker steals speech samples from banking systems. Voice-activated spam calls can capture voice data.
Strong new fraud review tool. LLM-based assistants executing RAG on the backend can use policy documents to speed up manual fraud reviews
Deep learning using GNNs, NLP, and computer vision can improve KYC and AML identity verification, cutting costs and enhancing regulatory compliance
NVIDIA works with the Deep Graph Library and PyTorch Geometric teams to provide a containerized GNN framework with the latest improvements, NVIDIA RAPIDS libraries, and more to keep users current
GNN developers can use Bootstrapped Graph Latents or negative sampling to pretrain models without labels and fine-tune models with fewer labels to produce strong graph representations
Heterogeneous graph transformer/attention network Attention methods in each layer of GNN models allow developers to identify message paths to a final result