#Kdk At εεχμτ 2010 12 03 V10


Published on

a presentation delivered to the 1st National Conference of the

Published in: Business, Technology
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

#Kdk At εεχμτ 2010 12 03 V10

  1. 1. 1o Εκνικό Συνζδριο Επιςτθμονικισ Εταιρείασ Χρθματοοικονομικισ Μθχανικισ και Τραπεηικισ (Ε.Ε.Χ.Μ.Τ.)operational risk:management Vs measurement a practitioner’s view 03/12/2010 kdkarydias Group OpRisk Officer Eurobank EFG
  2. 2. introductionoperational risk is as old as the banking industryitself institutions had been reactive and responsive to OpRisk as it arose rather than managing it in a proactive mannerthe confluence of the collapse of Barings andthe derivatives blow-ups in the mid-1990s wasone among several factors that led to Basel II banks must compute an explicit capital charge for operational risk©2010 kdkarydias 2
  3. 3. the human factorOpRisk is defined as the risk of loss resulting frominadequate or failed: internal processes designed, drafted, performed by people people are people systems designed, implemented, operated by people or from external events due to people or act of God OpRisk is heavily related to people and to human behaviour©2010 kdkarydias 3
  4. 4. OpRisk characteristics mainly endogenous Unwanted by-product of the business activity Not willingly incurred Positively related to the complexity of the operations permeates the entire enterprise, involving virtually every employee, every business process and every system highly idiosyncratic Tend to be less correlated to each other and to other risk types Less directly linked to business cycles in principle (partially) controllable ex ante©2010 kdkarydias 4
  5. 5. OpRisk characteristics cont’d a trade off between risk and cost of avoidance more qualitative rather than quantitative potential OpRisk losses can be practically unbounded Observed losses are not related to bank size Losses are not capped Often significant time lags between cause and effect Usually recognised “after the fact” Loss severity distributions are fat-tailed is sizeable compared to other risk types©2010 kdkarydias 5
  6. 6. why measure OpRisk? “What can’t be measured, can’t be managed…” Joe Sabatini, Head of OpRisk, JP MorganChase & Co to enable an effective risk mgt process to measure progress to quantify exposure in a forward looking manner as a minimum you need “measures” with some directional and relative reliability is risk going up or down? is risk higher here or there?©2010 kdkarydias 6
  7. 7. objective of measuring OpRisk provide an accurate view of the OpRisk profile of the business over the next 12 months what are the expected losses from OpRisk what is the worst case loss from OpRisk support the analysis of OpRisk what are the top OpRisks what is the worst case loss under stress conditions how will changes to business strategy or control environment affect the potential losses how does the potential hit compare with other banks©2010 kdkarydias 7
  8. 8. OpRisk capital (Basel II)as per AMA, banks should put an OpRisk capitalaside in line with the 99.9% or even higherconfidence level over a one-year holding periodBasel II requires that a bank directly or indirectlyuses information from all four elements ofoperational loss data, namely: internal loss data external loss data scenario/workshop data business environment & internal controls data©2010 kdkarydias 8
  9. 9. measurement quantification can be a powerful tool for enhancing transparency, as long as it is credible. financial industry managers and regulators have an increasing interest in quantifying OpRisk numerous conferences convened on quantifying OpRisk and involving the top specialists - little substantive has emerged critiques, however, have been raised about the limitations and less desirable consequences of blind quantification©2010 kdkarydias 9
  10. 10. critiques “As far as the laws of mathematics refer to reality, they are not certain; and as far as they are certain, they do not refer to reality” Albert Einstein, German Physicist, 1922 “But there are also unknown unknowns; there are things we dont know we dont know” Donald Rumsfeld, American Politician, 2002 “… a message to banks that have placed too much emphasis on modeling operational risk to the detriment of operational risk management” House of Lords, Banking Supervision & Regulation Report, 2009©2010 kdkarydias 10
  11. 11. modelling for OpRisk  many banks have focussed on meeting the capital modelling requirements of Basel II and have lost sight of the need to manage the business most of the models currently in use to quantify OpRisk are based on historical loss data considering the constant change of technology and organizational structures these data prove not sufficient to quantify the OpRisk of today©2010 kdkarydias 11
  12. 12. using historical loss data  sparse existing data  employment of third-party loss data is impractical and partly impossible  past loss data may no longer reflect current state and structure of risks  risk prevention strategy “interferes” with loss history  due to non-transparent risk drivers, VaR (Value at Risk) levels are of little informational value to management (too aggregated, too abstract)  current structure / non-materialised risks can only be integrated by direct estimation of a new loss distribution, which is complex and difficult to reason  present knowledge about risk correlation cannot be formalised©2010 kdkarydias 12
  13. 13. limitations of models  a model is always a strong reduction/ approximation of a more complex reality  models are as good as the underlying assumptions: "garbage in– garbage out effect"  not all risks are relevant and/or quantifiable: also here, use 20/80 approach  new external parameters and continuous restructurings can make models questionable, as there is no reliable base material  comparisons of absolute model figures with those of third parties are questionable: The prime internal value added of a good model – including the stress test – is its trend over time  theoretical rigidity may not prevail over practical relevance and credibility  models are always only part of an overall risk management approach and must include common sense©2010 kdkarydias 13
  14. 14. goal of OpRisk management ...... is to reduce the frequency and severity of large, rare events Minor Events Generally not bank threatening Experience makes easy to High understand problems, measure Not relevantfrequency issues, take action [already out of business] “cost of doing business” Generates efficiency savings than reduce material risks Major Events Can put banks out of business or Low Does not really matter harm reputationfrequency Difficult to understand & prioritise in advance Small losses Large losses©2010 kdkarydias 14
  15. 15. OpRisk management  OpRisk has, until now, baffled experts due its lack of meaningful mathematical models  complex data requirements  the broad range of areas in which it occurs  most serious OpRisk losses can not be judged as mere accidents  the only way to gain control over operational risk is to improve the quality of control over the possible sources of huge operational losses©2010 kdkarydias 15
  16. 16. epilogue “We need to overcome the tendency to believe that a single number can summarise a distribution of possibilities ... We need to be creative in managing the unexpected” John Trundle “We believe that quantification should not be exaggerated. Equally important is the need for creativity in operational risk management” Stefan Look The PRiM Risk Newsletter, issue No 23, 10/2010©2010 kdkarydias 16
  17. 17. The views expressed in this presentation havenot been subjected to peer review, and should beinterpreted accordinglyWe thank our colleagues and partners for themany fruitful interactions that have contributedto this workThe views expressed in this presentation do notnecessarily reflect the views of Eurobank EFG©2010 kdkarydias 17