– Enables real-time analysis and insight to credit and market risk, with ability to manage exposure by counterparty, asset class, industry sector and trader
– Risk monitoring framework combines real-time event processing capabilities with the Aleri live OLAP server for roll-up and drill-down
Aleri Inc., a leading provider of enterprise-class complex event processing (CEP) technology and CEP-based solutions, today announced the development of a Real-Time (RT) Risk Monitoring solution framework. The framework provides a template for rapid implementation of a customized, comprehensive solution for consolidating and managing credit and market risk.
Aleri’s RT Risk Monitoring solution provides consolidation and analysis of positions in real-time, which can help a firm identify unacceptable levels of exposure along a number of configurable dimensions, including counterparty, trader, asset class, region and industry sector. The solution gives users the unique ability to determine the underlying cause of the concentration, right down to individual trades. Additionally, Aleri’s event-driven architecture allows for non-intrusive integration with existing systems, making it easy for firms to quickly move from end-of-day monitoring to immediate insight.
"Market volatility and the increased focus on counterparty risk have prompted trading firms to look for better tools to manage their exposure," said Jeff Wootton, vice president of Product Strategy at Aleri. "The current tools many firms are using can’t provide consolidated information on a timely enough basis across their trading operation. Aleri’s RT Risk Monitoring solution can quickly provide firms with this capability."
The Aleri RT Risk Monitoring solution leverages the Aleri CEP platform and provides immediate and continuous monitoring and analysis, eliminating the need to wait for overnight consolidation and batch computations. By extending the capabilities of the Aleri CEP technology with the Aleri Live OLAP server, users can analyze data at any level of aggregation across different dimensions. Hierarchies applied to each of the dimensions can specify how the data gets rolled up into aggregate values, with the ability to drill down all the way to the individual transaction level.
