Bcbs 239 v4 30 oct
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The document discusses regulations from the Basel Committee on Banking Supervision (BCBS) that require banks to aggregate risk data and report it comprehensively (BCBS 239). It introduces the Model DR approach, which provides a panoptic and congruent view of a bank's data set to enable compliance. Model DR breaks data down into standardized elements and provides tools to flexibly restructure data and generate reports from any viewpoint. It advocates establishing a global language like FIBO and XBRL to define financial terms and facilitate integrated and transparent reporting.
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Bcbs 239 v4 30 oct
- 1. BCBS 239 Risk Data Aggregation: The data scope is enormous - where & how to begin? The Basel Committee on Banking Supervision (BCBS) has issued Regulation 239; a mandatory requirement for banks to aggregate risk data and provide comprehensive reporting. A significant upgrade of Risk IT and Systems is imperative
- 2. Panoptic & Congruent • Congruent data structures allow viewing flexibility – any data request can be satisfied • Hard coded data solutions do not allow a panoptic data view – we want to ask for anything Senior management requires a panoptic view of the banks dataset To be panoptic the data set needs to demonstrate congruence.
- 3. BCBS 239 compliance requires an Action Plan G-sib banks must be BCBS 239 compliant by Jan 2016 Assembling the data is too big a job for one Project Team • The Model DR Action Plan introduces three new concepts – Industrialisation – Global language – Congruent panoply Model DR does not provide a bolt on solution. This is baking the solution into your business 2013-09 3 How to industrialize business reporting
- 4. Many pieces of data, but a universal way to view it • What is the data set - Assets & Liabilities - multi currency - Counterparty Credit - Value at Risk (VAR) - Derivatives……. • Data cascades, horizontally and vertically, in multifarious but interconnected hierarchies, across the bank • One data aggregation model is required, using a common language, to sit over the various internal system architectures • Model DR provides a congruent, panoptic data view from which data may be aggregated and reports assembled • Model DR breaks data into atomic elements, providing the tools for flexible restructuring 2013-09 4 Datapoint modelling and its opportunity
- 5. Regulatory Demand for Data – when & what • Banks cannot predict from what view point regulators may demand reports and data • Model DR provides the tools to build a new viewpoint ‘on the fly’ Regulatory scenario - Greece defaults on its bank debt, as Europe slides into recession. What is exposure to German/ French banks – debt, derivatives, Greek counterparties? Aggregation Model Model DR - congruent panoptic view Foreign Currency Loans Derivatives Securities Counterparty credit 2013-09 5 How to industrialize business reporting
- 6. Model DR Concept: Industrialization • Model DR enables banks to open up and examine existing systems by mapping to the DPM • The industrialization process automates, provides new tools and makes systems more accessible • Each project team is given tools & training • Progress is measured and project teams are accountable • Are teams following the process correctly • Are they mapping to the global language • Are they exposing data in the agreed way • The industrialization process is too large for one project management team – instead responsibilities are shared by project groups • Data point modelling provides the methodology to reverse and forward engineer Model DR production design into the global language 2013-09 6 How to industrialize business reporting
- 7. Model DR Concept: The global language • FIBO (Finance Industry Business Ontology) - an industry initiative to define finance industry terms - provides for data congruence - facilitates data integration, supports business process automation and enables consolidated views across the financial industry - driven by Dodd Frank regulatory requirements for improvements in data quality and transparency - FIBO ontology defines financial terms and concepts without ambiguity • XBRL (eXtensible Business Reporting Language) - a business language used by major regulators to standardise financial reporting terms - XBRL allows universal communication through metadata taxonomies which capture and define financial concepts, terms & relationships 2013-09 7 How to industrialize business reporting
- 8. Model DR Concept : data set must be congruent to model • Model DR provides all users with a single, enterprise view of portfolio risk and exposure. It is not a data warehouse • Data Point Model (DPM) provides a literal representation of the data, identifying all the business concepts and relations, as well as validation rules. It contains all the relevant technical specifications necessary for developing an IT reporting solution. 2013-09 8 How to industrialize business reporting
- 9. The Data Point Model Cube region Aspect The data point meta model has a dozen main concepts Table Axis Value Set Resource Value Many 1 Context Value Data point Cube Business class Resource link Axis coord. 2013-09 9 How to industrialize business reporting
- 10. Example 1: Reverse engineer a report into a Data Point Model Reportable Aspect X Axis Aspect X Axis Value Set X Axis Coordinate Aspect Values Report name Y Axis Aspect Y Axis Value Set Name Y Axis Coordinate Aspect Values Reportable Aspect Values Package name
- 11. Business entity Attribute Adaptors Adaptors Class level adaptor Class level Aspect Attribute level Aspects Attribute level Value Sets Resources Example 2: Reverse engineer a data base into a DPM
- 12. Example 3: Designing a new viewpoint = wiring up DPM to DPM (with comparability) A third DPM wired together from 2 existing DPM 2013-09 12 How to industrialize business reporting
- 13. The Model DR product offering: • Dynamically builds an alternate data viewpoint • Allows forward and reverse engineering of data in existing system architecture • Allows universal structuring and data reassembly at an atomic level • Provides the tools for BCBS strategy development • TTE -Tooling for industrialization • TTE -Training : • BCBS specification • Developing the global language (Meta modelling) • Designing and building viewpoints (Data Point Modelling) • Tools usage (reverse engineering, design, forward engineering) • TTE –Execution: • DPM Hard wire existing systems into a congruent data panopoly • Model DR is a data design & analysis solution ; not a massive technology play • Model DR pilot module enables transition towards the larger system architecture decisions 2013-09 13 How to industrialize business reporting
- 14. Model DR provides a congruent data set in a universal code ready for reconciled and validated report generation TTE – Tooling, Training, Execution Regulation may be the driver but efficiencies will evolve from a universal language across the big data technology curve 2013-09 14 How to industrialize business reporting
Editor's Notes
- Stick to Action Plan on this page and in the pages following look at 3 big ideas --- then go onto Demonstration Measurement: How far down the road to complet are the teams / applciaitons ? Are they following the process correctly e.g. are they mapping to the global language? Are they exposing their data in the agreed way? Industrialization = scalability = you give each team tools, training and then measure progress - This is too big a job for one team
- We are providing the congruent panoptic views, from which you do the aggregation . We provide the tooling for building the aggregation model. -> flexibility We break down the data into atomic elements (lego pieces) so you can re-structure in your own way. Isnt it the other way around ? many pieces of data but there needs to be one common way to see it? Cut the globe or apple into vertical and horizontal sections Do a chart of all the systems leading up into the Risk aggregation model One piece of data, many ways to need to see it All this must be true: There can only be one source of truth It must be concrete and accurate Many players must view it in their own, very differently ways Unlike, say, the Arts, in business data there can be only version of the truth. But there can, is, and should be many uses and interpretations of that. Client billing people say “Clients must have a name”. So the client on boarding people make that mandatory. Then the marketing department want to send the customer sales to the ad agency, and no way can the client name be passed over. So client is one data concept, with 2 directly opposite requirements. Obviously a trite example but that problem exists in thousands of cases, and there is no economy of scale, the problems compound and you get a bigger and broader organization.
- Situation – you can t predict the from which view point data may be demanded - giving the ability to demand data in customised reporting style on the fly
- Slide 11 will come earlier -- but really needs simple explanation Expansion --- still want a bit more here Industrialization: Tooling, automation, scalable We are allowing them to re-use existing internal systems in a new way
- Industrialization first Then global language Then congruent panopoly
- Big idea 2: You must be able design against a congruent panoply of your data This needs expansion
- This needs a lot of explanation
- Need to explain what point you are trying to make here
- Why do you want to reverse engineer?
- More explanation – congruence across DPM AFTER CONVERSION TO CONGRUENT LANGUAGE Need to go through the stages of industrialization
- Xbrl – extensible business reporting language All the regulators have standardised on XBRL – A GLOBAL LANGUAGE – IF YOU REPORT DATA TO LOCAL REG. – report in XBRL Standardised way of reporting XBRL to compete with FIBO – a common language – FIBO is generic – but XBRL specifies the taxonomy for each country Building pitch based on data aggregation – efficiencies will come from a common language across the technology curve Say two isolated systems that bring the aggregation data to gether for a few systems and build a congruent view based on a