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Maxeler Real Time Risk

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Maxeler Real Time Risk (RTR is a comprehensive suite of financial risk management tools designed to deliver high-performance analytics for complex risk models. Leveraging Maxeler's Dataflow Engines (DFEs, RTR significantly accelerates computations, reducing processing times from hours to minutes and minutes to seconds. This solution is available both on-premise and in the cloud, including compatibility with Amazon EC2 F1 Instances, providing flexibility and scalability to meet diverse organizational needs. Key Features and Functionality: - Comprehensive Risk Tools: RTR includes modules for Credit Value Adjustment (CVA, Standard Initial Margin Model (SIMM, and a full derivatives pricing library, all driven by Bloomberg market data. - High-Performance Computing: Utilizing Maxeler's DFEs, RTR delivers ultra-fast real-time processing capabilities, enabling rapid recalculations of complex risk models. - Flexible Deployment: RTR can be deployed on AWS CPU cloud instances for standard processing or on Amazon EC2 F1 Instances for ultra-fast real-time purposes. - Standardized Data Integration: Clients can upload trades and portfolios using the industry-standard Financial products Markup Language (FpML format, ensuring seamless integration with existing systems. - Customizable Solutions: RTR is ideal for building tailored solutions for Fundamental Review of the Trading Book (FRTB, Counterparty Credit Risk (CCR, and extended scenario analysis, offering complete dashboards and an optional API-based library with full source code. Primary Value and User Benefits: Maxeler Real Time Risk addresses the critical need for rapid and accurate risk assessment in the financial sector. By dramatically reducing computation times, RTR enables financial institutions to perform real-time risk evaluations, enhancing decision-making processes and regulatory compliance. Its flexible deployment options and comprehensive toolset provide organizations with a scalable and efficient solution to manage complex risk models effectively.

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