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

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, and 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 on-demand 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 that successfully addressed these challenges will also be presented.

Stephen Darlington
Stephen Darlington
Training and Consulting Services Manager

Stephen is the Training and Consulting Services Manager at GridGain, where he helps clients meet their business goals using unified real-time data and in-memory technologies. 

He's worked with high-performance, data-intensive applications for over 15 years and has experience across many industries, including telecoms and financial services. 

Also, he is an active Apache Ignite Committer and top non-code contributor helping Ignite developers on the user list and StackOverflow.