NVIDIA RAPIDS
2 use cases using this technology
Digital Bank Debunks Financial Fraud With Generative AI
bunq
European neobank bunq, with more than 12 million customers and 8 billion euros of deposits, built an automated, AI-powered transaction-monitoring system to detect fraud and money laundering, replacing labor-intensive rules-based systems with supervised and unsupervised learning. Using NVIDIA GPUs, bunq accelerated its data processing pipeline more than 5x and, using the open-source NVIDIA RAPIDS suite of GPU-accelerated data science libraries, trained its fraud-detection model nearly 100x faster, improving model accuracy and reducing false positives. Bunq is also exploring NVIDIA NeMo Retriever, part of NVIDIA NIM inference microservices, to improve the accuracy of Finn, its personal AI assistant powered by a proprietary large language model.
AT&T Drives AI Agents' Accuracy, Efficiency, and Performance With NVIDIA
AT&T
AT&T built 'Ask AT&T' customer service AI agents and worked with implementation partner Quantiphi to use NVIDIA AI Enterprise, NVIDIA NeMo and NIM microservices to build a data-flywheel platform for continuous fine-tuning and evaluation. The pipeline uses NeMo Curator to clean training data, NeMo Customizer to fine-tune base models (Mistral 7B was selected as optimal), NeMo Evaluator to measure performance (Rouge, BERT F1), and NeMo Retriever for up-to-date retrieval, with models deployed as NIM microservices. AT&T reports up to 40% improvement in response accuracy after fine-tuning and an 84% decrease in call center analytics cost, and is collaborating with Arize AI to automate identification of difficult AI interactions.