Top 3 Data Challenges in Financial Risk Management & How to Solve Them

Presented by

Stephen Darlington, Principal Consultant, GridGain Systems

About this talk

Financial institutions are facing increasing pressure to deliver true real-time risk management solutions across massive amounts of data from increasingly disparate data sources. All areas of risk analysis – including market risk, credit risk, liquidity risk, collateral risk, margining – are data- and compute-intensive, making data processing with low latency and massive scale a critical requirement for leaders in the financial risk management space. In order to provide the most timely insights, risk management teams must fully address 3 complex data challenges: How to integrate data from numerous data sources and in different formats into a fast-access data hub How to process, query, and analyze ALL of the data (not a subset) that are necessary for true risk analysis How to deliver intraday risk analysis as close to real-time as possible Join this webinar to learn both architectural and technological strategies to overcome all three of these data challenges. The "before" and "after" solutions and performance gains of 3 Fortune 500 financial institutions who successfully addressed these challenges will also be presented. Save your spot now!
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GridGain is a unified real-time data platform that enables a simplified and optimized data architecture for enterprises that require extreme speed, massive scale, and high availability from their data ecosystem. GridGain’s distributed memory-first architecture and colocated compute deliver data processing and analytics at millisecond latencies, with configurable disk-based persistence for added durability. Horizontally scalable clusters can be deployed both on-premises and natively in public or private clouds, empowering companies to handle even the most demanding workloads in multi, hybrid, and inter-cloud environments. GridGain is trusted by companies like Citi, Barclays, American Airlines, AutoZone, and UPS to accelerate their existing applications, speed operational analytics and fraud detection, train machine learning models for AI, and provide fast-access data hubs. To learn more, please visit