Real-time Risk Monitoring

Firms that engage in financial trading are exposed to counterparty risk (i.e. credit risk), market risk and operational risk. In the wake of recent stresses in the financial markets, firms have become aware of the limitations of their existing system for managing risk: the information they provide is not timely enough and is too fragmented – it doesn’t provide a real-time consolidated view of the entire trading operations. When the markets are volatile, causing the value of holdings to change rapidly, and economic conditions that make counterparty risk very real, having access to timely information can be critical.

Using manual or batch processes to consolidate data across systems, and running reports on a nightly basis (or worse!) don’t give you the insight you need to make decisions quickly, before small problems become big problems.
What the financial industry needs is a new approach to consolidating, monitoring and managing risk, leveraging the best of new technologies that have become available in a way that does not require the wholesale replacement of existing systems. This new approach needs to provide:

Speed: In fast moving, highly volatile markets and economic conditions that make counterparty risk very real and dynamic, a trading operation cannot wait until the next day to understand what their exposure is. Traditional systems that rely on lengthy overnight global batch processing to consolidate and net-out positions and exposures simply aren’t good enough.
Consolidated, Enterprise-level Insight: Most larger firms struggle with the problem of having multiple disparate trading and risk management systems – systems that weren’t designed to share information with each other. Normalising and consolidating information across these systems can be difficult but is essential for seeing the big picture.
Data Availability and Quality: the traditional silo-based approach that separates the management of market risk from credit risk and further separates compliance from both often means that each group works with different sets of data that also may be of different quality. The result is data that is not only delayed but different reports present different irreconcilable views of the information, making it difficult to understand the real risk position across the firm.
Adaptability and Ease of Deployment: The high cost of change rules out big projects that require large budgets and lengthy deployment cycles. Any new stress scenarios require quick response times - often in hours rather than days. New tools need to easily integrate with existing systems and adaptable to rapidly changing business needs.

The key benefits of the Aleri framework over traditional systems include:

Real-time, continuous monitoring, analysis and alerting. No need to wait for overnight consolidation and batch computations. Data is always current and always available.
Enterprise-wide view. Consolidation across systems, asset classes, departments and geography. Avoid the fragmented view that each trading system provides.
Roll-up and Drill-down. Aggregate the data along any number of dimensions. View at any level, with the ability to drill down all the way to individual transactions.
Scenario Simulation modeling (Liquidity Risk), pre-defined situations such as extreme events or special market conditions. Ease of changing definitions, powerful simulations in under seconds.
Real-time limit management. Set non-additive limits at multiple levels of the data hierarchy and flag exposures in real-time when reach or exceed limits. An example of “non-additive limits” would be: limit of 10 to each of 5 related entities, but an aggregate limit of 40 to the group.
Pre-trade approval. System can automatically check and incoming order to confirm sufficient “capacity” by checking current positions against limits before approving, denying or restricting the order.
Monitor trading patterns in real-time. Generate alerts or impose restrictions when pre-defined patterns are observed.
Non-intrusive aggregation with existing systems. The Aleri system can accept data in native format from any number of different systems, normalising and cleansing/validating the data within the Aleri consolidation engine. The Aleri server can accept live data streams from systems that can provide them – either directly or via a message bus, and can load data from databases and files for systems that can’t produce live output streams. Aleri has a variety of connectors for integration – if there’s a way to get the data out of the existing system, it can be consolidated in Aleri.