Find out how financial companies analyse data using NVIDIA GPU-acceleration, impacting real-time risk management, regulatory reporting, fraud detection and cybersecurity, anti-money laundering, and trader surveillance. We will discuss real-world examples, including how a specific multinational bank uses a real-time risk management engine running on GPU cloud instances. The bank's analysts can now make time-sensitive, computation-intensive risk calculations involving hundreds of variables, using a real-time, interactive dashboard. This produces meaningful, timely, and consistent financial analysis, optimised to maximise profitability and power business in motion. This approach lends itself to a data-powered business. It allows banks to move applications â such as counterparty risk analysis â from batch overnight processing to streaming and real-time, creating flexible real-time monitoring of extreme data that makes it easy for traders, auditors, and management to take action.
Get the latest information on how financial markets are using advanced in-database analytics for real-time risk aggregation. Advanced in-database analytics allows the bank to run custom XVA algorithms at scale with the GPUs massive parallelization. This approach allows banks to move counterparty risk analysis from batchundefinedovernight to a streamingundefinedreal-time system for flexible real-time monitoring by traders, auditors, and management. Real-world examples and insights will be provided, including how a multinational bank is using Kinetica as a real-time risk modeling engine running on public cloud-based, Microsoft Azure GPU instances. The bank can now handle time-sensitive, compute-intensive risk computations to project years into the future across hundreds of variables.