Data scientists are combining generative AI and predictive analytics to build the next generation of AI applications. In financial services, AI modeling and inference can be used for solutions such as alternative data for investment analysis, AI intelligent document automation, and fraud detection in trading, banking, and payments. H2O.ai and NVIDIA are working together to provide an end-to…
]]>End clients are working on converged HPC quant finance and AI business solutions. Dell Technologies, along with NVIDIA, is uniquely positioned to accelerate generative AI workloads and data analytics as well as high performance computing (HPC) quantitative financial applications where converged HPC quantitative finance plus AI workloads are the need of the hour for clients.
]]>Generative AI is taking the world by storm, from large language models (LLMs) to generative pretrained transformer (GPT) models to diffusion models. NVIDIA is uniquely positioned to accelerate generative AI workloads, but also those for data processing, analytics, high-performance computing (HPC), quantitative financial applications, and more. NVIDIA offers a one-stop solution for diverse workload…
]]>In the high-frequency trading world, thousands of market participants interact daily. In fact, high-frequency trading accounts for more than half of the US equity trading volume, according to the paper High-Frequency Trading Synchronizes Prices in Financial Markets. Market makers are the big players on the sell side who provide liquidity in the market. Speculators are on the buy side…
]]>The business-to-business (B2B) payments ecosystem is massive, with $25 trillion in payments flowing between businesses each year. Photon Commerce, a financial AI platform company, empowers fintech leaders to process B2B payments, invoices, statements, and receipts instantly. Over two-thirds of B2B transactions are processed through automated clearing house payments (a type of electronic…
]]>Data Scientists and Machine Learning Engineers often face the dilemma of “machine learning compared to deep learning” classifier usage for their business problems. Depending upon the nature of the dataset, some data scientists prefer classical machine-learning approaches. Others apply the latest deep learning models, while still others pursue an “ensemble” model hoping to get the best of both…
]]>Recently, one of Sweden’s largest banks trained generative adversarial neural networks (GANs) using NVIDIA GPUs as part of its fraud and money-laundering prevention strategy. Financial fraud and money laundering pose immense challenges to financial institutions and society. Financial institutions invest huge amounts of resources in both identifying and preventing suspicious and illicit activities.
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