For financial institutions, all data is risk data. Data from a variety of sources – internal and external – and differing types, including client, market, reference, financial and operational – is the foundation of risk management and supports the design and launch of new products, and the ability to extend strategies and address client and regulatory reporting. Across the front office, middle office and treasury, many financial institutions have complex infrastructures involving nonsense systems from multiple vendors. while IT teams grapple with long-term reforms to integrate data and analytics more effectively across systems, business pressures require a more immediate and efficient approach.
Asset managers and banks worldwide have implemented SS&C‘s Algorithmics Workspace Analyzer (AWA) to address broad business and regulatory requirements. at its core, AWA Enables data aggregation, analytics and reporting across business functions, sources and systems. The software is configured with a wide range of risk measures, including an integrated market and credit approach with views on liquidity indicators. An innovative analytics approach supports multiple products, allows users to model potential trades and to see the impact on limits as well as simulating market, credit, liquidity risk analysis and portfolio construction. Users can compute relevant risk analytics out of the box and run extensive what-if analysis, including changes to asset holdings, proxying assets, benchmark construction and hedges. In particular, the analytics functionality calculates changes in value-at-risk (VAR) and allows for portfolio construction that meets return, risk, asset allocation and trading constraints, while adhering to emerging environmental, social and governance (ESG) factors.
Moreover, institutions can aggregate to the top of the house or drill down to the deepest constituent. Data is loaded in-memory and spread across multiple servers, allowing for efficient reporting on millions of records, and integrated from different input sources including obligor, credit correlation, scenarios, timesteps and risk factors. In-memory caching of relevant information supports interactive dashboards capable of drilling into the underlying data and analytics, and avoids common latency problems of deploying reporting on an enterprise-wide scale. Finally, a series of preconfigured add-ons exist to support specific enterprise reporting requirements, such as the Fundamental Review of the Trading Book, market risk, balance sheet management and the standardized approach to counterparty credit risk.
In the past year, SS&C Algorithmics enhanced its solutions with scenario management capabilities: the ability to share reports as a batch process, managed service in an ad hoc manner; to toggle between reporting and configuration; and to combine ex post reporting in the same software as ex ante reporting. Finally, further enhancements of cloud features released last year aligned with clients’ increased focus on cloud migration. These include cloud-native micro-services, certification, refactoring and containerisation. Consequently, end-users can use the most economical computational resources available, such as big data clusters or grid computing environments, depending on their infrastructure.
The judges said:
- “This is a classical risk dashboard that works well with the client requirements, is configurable and multi-product.”
- “SS&C AWA is very innovative, allowing users to simulate potential trades and see the risk impact on limits.”
- “The dashboard looks impressive and it indicates VAR changes and ESG elements.”
- “This solution has a good set of inputs, systems and representation of key information.”
Curt Burmeister, Chief technology officer at SS&C Algorithmics, said:
“We are proud to receive this Risk Technology Award for SS&C‘s AWA. Against today’s financial risk management backdrop, AWA allows fund managers to explore data beyond market risk by supporting an integrated market and credit approach with views on liquidity indicators. The ability to visualize these large-scale results in a common interface is a key differentiator for AWA.”