Quantitative developers need to run back-testing simulations to see how financial algorithms perform from a profit and loss (P&L) standpoint. Statistical techniques are important to visualize the possible outcomes of the algorithms in terms of the possible P&L paths. GPUs can greatly reduce the amount of time needed to do this. In the broader picture, mathematical modeling of financial…
]]>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…
]]>On April 21, 2021, the EU Commission of the European Union issued a proposal for a regulation to harmonize the rules governing the design and marketing of AI systems called the Artificial Intelligence Act (AIA). AI systems are considered to be risky by regulatory bodies. High-risk AI systems are subject to specific design and implementation obligations to improve transparency.
]]>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.
]]>Machine learning (ML) can extract deep, complex insights out of data to help make decisions. In many cases, using more advanced models delivers real business value through significantly improving on traditional regression models. Unfortunately, using traditional infrastructure to explain what drove a particular decision with a more advanced model can be difficult, time-consuming, and expensive.
]]>Imagine an AI program that can understand language better than humans can. Imagine building your own personal Siri or Google Search for a customized domain or application. Google BERT (Bidirectional Encoder Representations from Transformers) provides a game-changing twist to the field of natural language processing (NLP). BERT runs on supercomputers powered by NVIDIA GPUs to train its…
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