Golden Copy: Risk Data Nuances ... And Déjà Vu
Could BCBS risk capital framework changes prove useful for risk data management?
A new development in the Basel Committee on Banking Supervision (BCBS) risk capital calculation and measurement standards makes me wonder if it could be beneficial for addressing risk data management issues considered at length in a feature in the March issue of Inside Reference Data.
The BCBS has ostensibly simplified the measurement approach for its risk capital framework, something it began thinking about in 2014. The committee recentered its measurement around a financial statement-based assessment of operational risk, compared to past operational figures. At the same time, it eliminated a duplicate standard as well as the advanced measurement approach it had offered as an option.
The data officers who shared their challenges in the feature story are contending with data that can arrive with different characteristics, using different standards, depending on whether it comes from a firm's finance or risk side. Perhaps they should design and apply a risk measurement model that is common to both departments, and a prescription for such an approach -- or at least a good model for what to do -- could be found in the BCBS's latest work.
Some of these executives see risk data management as a data governance issue. Mizuho Securities' Marc Alvarez says his firm uses a two-part strategy to improve the governance of risk data -- first, educating management about data, and second, getting more quantitative risk data as a foundation. Having more granular data available to feed into a single standard measurement approach can only produce a more consistent and accurate result, but the key is making sure that approach stays the same on both the finance and risk sides, and doesn't stray by making exceptions for certain pieces of data.
Considering other stories in this issue finds us going from the sublime (those fine distinctions concerning risk measurement and modeling methods) to the ridiculous (the prospect of the New York Stock Exchange's (NYSE) parent, the Intercontinental Exchange, taking a stab at acquiring the London Stock Exchange (LSE), at the last minute, while the LSE appears to be more amenable to the merger offer already on the table from the Deutsche Börse. NYSE's management ought to study what happened about 10 years ago, when the US-based Nasdaq followed Australian bank Macquarie's unsuccessful bid for the LSE with a failed bid of its own. Although Deutsche Börse took repeated shots at acquiring the LSE in the early 2000s, the current attempt at a merger finds the German exchange on an inside track. The global exchange merger game can give you repeated cases of déjà vu.
Speaking of déjà vu, another piece in this issue has some echoes of the past. "London's Fintech Start-Ups Mature" covers the most recent round of financial technology start-up companies, whose missions include data management advances. We may have caught this wave on the back end, but it appears to be morphing into a specific "regtech" (regulatory technology) strain, because entries that were too broad have washed out.
Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.
To access these options, along with all other subscription benefits, please contact info@waterstechnology.com or view our subscription options here: http://subscriptions.waterstechnology.com/subscribe
You are currently unable to print this content. Please contact info@waterstechnology.com to find out more.
You are currently unable to copy this content. Please contact info@waterstechnology.com to find out more.
Copyright Infopro Digital Limited. All rights reserved.
As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (point 2.4), printing is limited to a single copy.
If you would like to purchase additional rights please email info@waterstechnology.com
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (clause 2.4), an Authorised User may only make one copy of the materials for their own personal use. You must also comply with the restrictions in clause 2.5.
If you would like to purchase additional rights please email info@waterstechnology.com
More on Data Management
Asset manager Saratoga uses AI to accelerate Ridgeline rollout
The OMS provider’s AI assistant helps clients summarize research, client interactions, report generation, as well as interact with the Ridgeline platform.
CDOs evolve from traffic cops to purveyors of rocket fuel
As firms start to recognize the inherent value of data, will CDOs—those who safeguard and control access to data—finally get the recognition they deserve?
It’s just semantics: The web standard that could replace the identifiers you love to hate
Data ontologists say that the IRI, a cousin of the humble URL, could put the various wars over identity resolution to bed—for good.
The art of communication: Data pros need better messaging
As the CDO of a tier-one bank puts it, when there’s an imbalance in communication between the data organization and the business (much less other technology heads) “that creates problems.”
Does TP Icap-AWS deal signal the next stage in financial cloud migration?
The IMD Wrap: Amazon’s deal with TP Icap could have been a simple renewal. Instead, it’s the stepping stone towards cloudifying other marketplace operators—and their clients.
T. Rowe Price’s Tasitsiomi on the pitfalls of data and the allures of AI
The asset manager’s head of AI and investments data science gets candid on the hype around generative AI and data transparency.
Waters Wavelength Ep. 298: GenAI in market data, and everything reference data
Reb is back on the podcast to discuss licensing sticking points for market and reference data.
Back to basics: Taxonomies, lineage still stifle data efforts
Voice of the CDO: While data professionals are increasingly showing their value when it comes to analytics and AI adoption, their main job is still—crucially—getting a strong data foundation in place. That starts with taxonomies and lineage.