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The risk data challenge

"The pessimist sees difficulty in every opportunity. The optimist sees the opportunity in every difficulty." Winston Churchill. Why might this data policy stuff be of interest?. Do your QIS?answers make sense to your regulator versus your peers?Can your board see the stress testing wood for the t

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The risk data challenge

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    1. The risk data challenge

    3. Why might this data policy stuff be of interest? Do your QIS answers make sense to your regulator versus your peers? Can your board see the stress testing wood for the trees?  Will your data be deemed ‘good enough’ if you use consolidated reporting, >T+3 verification, estimates, guesses and pro rating? Should the board commit millions to transform your infrastructure? Your need to know which counterparty deposits are insured Public sector may be asked to produce standards (or not) ... The cost of ‘bad data’ may not be tolerated again ...

    4. JWG: helping implement the right regulations in the right way We have holistically catalogued over 130,000 pages of G20 regulatory rulemaking We mirror the consolidated ‘regulatory radar’ back to the community and shape and define the operational requirements Then we create heatmaps of how individual rules will affect the various divisions, functions and geographies of the marketplace We work with trade bodies/standards organisations to produce ‘fit for purpose’ technical standards … … and catalyse development of robust solutions

    5. Executive summary Data policy requirements are on the books … with more to come Responses thus far have been limited … it may never work However, the future of data-intensive regulation could be painful without standards Risk professionals have a number of strategic choices to make that they will have to live with for some time You need to know what you need to know to make these choices now

    6. The risk data bar has been raised

    7. What is the microprudential requirement?

    8. What are the consequences of failure?

    9. What are the macroprudential views? “For G-SIFIs, the quality of information exchanged in supervisory colleges should be adequate to enable a rigorous co-ordinated assessment of the risks facing the institution ” FSB Reducing the moral hazard posed by systemically important financial institutions 20/10 “There appears to be significant variation in the quantity and type of data collected and utilised by each supervisor ... [information gap closure] will ultimately only be possible if the data systems that support the individual banks’ operations are sufficiently flexible and robust” FSB Intensity and effectiveness of SIFI supervision 02/11 “... one of the basic prerequisites for an effective macro-prudential function is the availability of a comprehensive set of information on the financial system that can be used for the detection and assessment of systemic risk. ... the national supervisors and national statistical authorities have the obligation to cooperate closely with the ESRB” ECB: 29/9 The establishment of the European Systemic Risk Board – challenges and opportunities

    10. What does it all mean? Board-level data policies More granular and flexible Current and accurate Judgements on evidence of judgements no longer 'pressed and laminated'

    11. Will we ever get there?

    12. It may never work … Too few winners, too many losers They’re not serious Nobody believes the business case I’ll be retired before this matters This is just something for compliance There aren’t any quick wins Nobody will ever figure out what ‘good’ looks like All top shops look the same No good has ever come from collaboration We don’t even know what success looks like!

    13. Case study: how it CAN work

    14. What does good policy look like?

    15. This could be painful without standards Understand the business problem Aggregate requirements in business context Define reference process imperatives Agree definitions and define metrics

    16. What could ‘good standards’ look like? Stress testing guidelines by asset class Data sources Risk factors Assumptions (e.g., stickiness) Reference risk data policies  Governance Data models/definitions Processes, procedures and checklists Data quality standards Accuracy Timeliness Management information ...

    17. When?

    19. In conclusion You can wait to define your strategy … but it will cost you You can go it alone … but you risk being off the curve Someone may tell you what to do … but they won’t know what you know Your team may not have the time … but this will quickly become ‘the day job’ You can sit on the outside … but they won’t necessarily come and find you in time You can wait to involve the supply chain … but you will pay a lot more later

    20. How many optimists do we have with us?

    21. Next steps Read the paper: ‘Clearing the risk MI bar?’ http://www.jwg-it.eu/library.php?typeId=15 Discuss the approach Attend the 12 July breakfast seminar: ‘Is your trading and risk data good enough’ http://www.jwg-it.eu/events/event20110712.php ALMA / AFGAP training? ...

    22. Thank you

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