Requirements for Systemic Risk Management in the Financial Sector

King, Alan J.; Dillenberger, Donna N.; Orani, Aviv; Parr, F. N.; Gong, Sitang

doi:10.1017/cbo9781139151184.042

Cited by 1 publication

(2 citation statements)

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“…Early impetus to the work was provided via a joint effort with the Enterprise Data Management Council to develop an end to end data model relating mortgages, mortgage pools, mortgage backed securities and their issuing, insuring and holding organizations reported on in [8]. This systemic risk work exposed the requirements for a specific middleware approach, consisting of a data model for gathering and integrating data from diverse sources, which could then be used by componentized analytic workflows to provide predictive modeling and analysis.…”

Section: Findings and Resultsvconclusionmentioning

confidence: 99%

“…The key asset that underlies the IBM Research approach is a data model that supports the level of integration needed in order to perform risk data aggregation at the enterprise level. King et al [8] applied an earlier implementation of this data model to mortgage supply chains. The data model enables the implementation of high-level component application programming interfaces, which are stable across all the applications that are to be integrated into the risk data aggregation process.…”

Section: Introductionmentioning

confidence: 99%

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Middleware for financial risk data aggregation

King¹,

Orani²,

Parr³

2014

IBM J. Res. & Dev.

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The ability of Global Systemically Important BanksVthose banks whose failure would materially affect the global financial systemVto identify risk concentrations and to aggregate their risk exposures in a timely fashion is now recognized as an important step for safeguarding the stability of the financial system and avoiding future crises. Regulators representing the G20 countries are in the process of providing guidelines to banks on best practices for risk data aggregation, together with initial reporting requirements and timetables. The goal of large banks to deliver the required information in a timely fashion poses numerous technical and logistical challenges, largely due to their dependence on silo-specific legacy information systems and the widespread use of manual processes for risk data aggregation. This paper addresses the challenges of risk data aggregation in Global Systemically Important Banks, particularly in terms of meeting the regulatory schedules for gathering, validating, and reporting the information. It introduces an innovative data model and middleware approach to address these requirements in an efficient, flexible, and scalable manner. The approach can accelerate development of a bank-wide exposure-reporting solution that meets regulatory requirements while leveraging existing investments and infrastructure of the reporting bank.

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Section: Findings and Resultsvconclusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%