MétaCan
Menu
Back to cohort
Record W345936866

Bankers & Supervisors Prepare for Operating Risk Capital Charges

2001· article· en· W345936866 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueABA banking journal · 2001
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsAudit committeeAccountingRisk-weighted assetBusinessOperational riskCompetitor analysisBasel IIIFinanceRisk managementCapital requirementRevenueBasel IIAuditEconomicsIncentiveFinancial capitalMarketingCapital formation
DOInot available

Abstract

fetched live from OpenAlex

In 1998, an international team of bank supervisors reported that awareness of operational risk as a separate risk category was relatively low at major banks. That began to change for bankers after June 1999, when the powerful Basel Committee on Bank Supervision released a reform proposal that would have added an operations-risk-weighting factor to banks' regulatory capital ratios. Then, in April 2000, bankers' awareness hit orbital levels when a draft committee report referred to such measures as fees, commissions, gross interest income, transaction values, volumes, assets under management, and value of securities in custody, as potential standards for capital charges in certain business lines. Bankers and their trade groups quickly mobilized to protect the non-interest revenue sources built up over decades in defending against encroachments from nonbank competitors. But they had a long route to travel. Organizing for management Few banks in 1998 kept analytic records of their operational losses and causes. Any data that existed was held in the business units, with oversight of operational risk performed at a high level by the banks' directors, management committees, or audit committees. At about half the surveyed banks, an internal Operating Risk (OpR, in Basel committee-speak) monitoring role was filled either by a risk manager or by a committee, such as a product review committee. In some banks, the financial controller, chief information officer, or internal auditor took on this role. Researchers took this lack of a formal infrastructure as an indication of the immaturity of the discipline. But then, it began changing rapidly. Today, the locus of responsibility for OpR measurement and monitoring has become far more identifiable at large banks. According to the Risk Management Association, several of its members have recently started chief risk officer positions, including Bank One, Citibank, First Union, Fleet Boston, and Royal Bank of Canada. This is often a senior level position, with reporting lines up from the chief credit officer and the insurance divisions. Among their challenges, says Nick Hayes, RMA director of member relations for global financial institutions, are the creation of institution-wide operating loss databases and the development of mitigants for operational risk. To help their members organize properly and prepare for the allocation of operating risk-capital charges, members of the RMA, British Bankers Association and International Swaps and Derivatives Association had commissioned a 1999 survey by PricewaterhouseCoopers. Released in February 2000, the study identified two kinds of operational loss events: (a) frequent, relatively low-cost experiences, such as write-offs from reconciliation failures and breakdowns in cash operations or payment systems; and (b) infrequent catastrophic events, the kind often reported in the media, especially when the institutional victim declared bankruptcy. Most banks surveyed in 1998 said that not only was their tracking of operational risk at an early stage, but their metrics were very primitive. Only a few had formal measurement systems. However, even at that point, there was movement towards the use of qualitative risk factors and subjective assessments. Among these were internal audit ratings; generic operational data, such as volume, turnover and complexity; and data on quality of operations such as error rate or measures of business riskiness, such as revenue volatility. These are today being translated by bankers into grades, like audit assessments, to create a set of factors and variables that measure and model business unit risks. Organizing to model swap risk In October 2000, the International Swaps and Derivatives Association (ISDA) published a suggestion on methods of qualifying internal models. ISDA argued that qualitative criteria should be a mandatory, not optional, factor in any regulatory appraisal of operational risk management. …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.263
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.238
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it