A recent report by Coalition Greenwich sheds light on the state of preparedness among risk professionals for potential market shocks. According to the findings, less than half of surveyed risk professionals (37%) express confidence in their ability to manage risks associated with the next market shock.
The survey reveals a nuanced perspective among risk professionals regarding their readiness for market shocks. While a majority (61%) consider themselves somewhat prepared, only a small fraction (2%) feel entirely unprepared. Confidence levels vary depending on market conditions, with respondents showing a nearly equal split between feeling confident and somewhat confident during normal market conditions.
The report underscores challenges faced by risk professionals in their risk management processes. Among the top concerns are difficulties in aggregating data essential for portfolio risk management, cited by 45% of respondents. Additionally, 39% express challenges in obtaining a comprehensive view of risk across multiple systems, while 32% highlight the complexity and manual nature of scenario building.
Despite confidence in their risk management practices, many professionals encounter obstacles in data aggregation due to siloed data and inefficiencies in combining disparate sources. Some still rely on multiple systems, leading to fragmented views of enterprise risk, particularly concerning nuanced asset class exposures.
Moreover, the complexity of scenario building emerges as a significant issue, with in-house system users facing more challenges compared to those utilizing third-party platforms. This could be attributed to the pre-canned tests or stress-testing modules inherent in vendor systems.
Audrey Costabile, senior analyst at Coalition Greenwich Market Structure and Technology, emphasizes the evolving nature of risk management practices in response to the dynamic global marketplace. She underscores the pivotal role of data in this evolution, as managers seek improved methods for acquiring, aggregating, and normalizing data to analyze larger and more complex datasets.
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