London Financial Modelling Group 2015 04 30 - Model driven solutions to BCBS239
•Download as PPTX, PDF•
2 likes•1,257 views
The London Financial Modelling Group meeting of 2015 04 30 - Model driven solutions to BCBS239. You will learn how to: – Demonstrate compliance to each of the principles by re-purposing your information architecture – Meet the obligations more efficiently by leveraging FIBO and semantic technology – Show your management team how data architecture helps meet BCBS239 using his “BCSB239 Model driven solutions checklist” tool.
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Similar to London Financial Modelling Group 2015 04 30 - Model driven solutions to BCBS239
Similar to London Financial Modelling Group 2015 04 30 - Model driven solutions to BCBS239 (20)
Recently uploaded
Recently uploaded (20)
London Financial Modelling Group 2015 04 30 - Model driven solutions to BCBS239
- 1. Regulation BCBS239 Model driven solutions
- 2. My co-ordinates • LinkedIn: Greg Soulsby • Twitter: #model_dr • Email: greg.soulsby@modeldrivers.us
- 3. You will learn how to: • Show your management team how data architecture helps meet BCBS239 using his “BCSB239 Model driven solutions checklist” tool. • Demonstrate compliance to each of the principles by re-purposing your information architecture • Meet the obligations more efficiently by leveraging FIBO and semantic technology
- 4. Deutsche Bank Libor fine: Seven crazy things traders said about Libor that cost the bank billions 4 On 21 February 2005, a trader requested of another trader who performed submitter duties on a back-up basis: Trader 1: "can we have a high 6mth libor today pls gezzer?" Trader 2: "sure dude, where wld you like it mate ?" Trader 1: "think it shud be 095?" Trader 2: "cool, was going 9, so 9.5 it is." Trader 1: "super – don’t get that level of flexibility when [the usual submitter] is in the chair fyg [for your guidance]!“ London CitiAm 2015-04-23
- 5. Merrill Lynch hit with £13.2m fine • The watchdog said the amount levelled at the firm “reflects the severity of MLI's misconduct, failure to adequately address the root causes over several years despite substantial FCA guidance to the industry, and a poor history of transaction reporting compliance”. • Georgina Philippou, FCA's acting director of enforcement and market oversight, said: “Proper transaction reporting really matters. Merrill Lynch International has failed to get this right again – despite a private warning, a previous fine, and extensive FCA guidance and enforcement action in this area. London CitiAm 2015-04-23
- 6. Many sources Multiple views of multiple sources is the data challenge OpenGamma (Postgress) Reference Data Corep Finrep (XBRL Taxonomy) Spreadsheets View 1 View 2 View 3 View 4 6
- 7. 7 Traditional data architecture uses data warehouse
- 8. Data warehouse Front office system Client ID Name Client type 1 Thatcher Gold 2 Blair Silver 3 Cameron Bronze Risk system Client ID LEI Name Client type Risk Rating 1 987 Thatcher Gold High 2 654 Blair Silver Medium 3 654 Cameron Bronze Medium 321 Obama LowLEI Name Risk rating 987 Thatcher High 654 Blair Medium 321 Obama Low 8 Warehouse duplicates & transforms the data
- 9. Therefore BCBS239 • Why? • What? o 1 Governance o 2 Data architecture and IT infrastructure o 3 Accuracy and Integrity o 4 Completeness o 5 Timeliness o 6 Adaptability o 7 Accuracy o 8 Comprehensiveness o 9 Clarity and usefulness o 10 Frequency o 11 Distribution o 12 Review o 13 Remedial actions and supervisory measures o 14 Home/host cooperation 9 • Who? o Systemically important banks o Others to follow • When? • Jan 2016 for SIB • Others later • How? • Periodic review by supervisor • Supervisor to supervisor cooperation http://www.bis.org/p ubl/bcbs239.pdf
- 10. Show your management team how data architecture helps meet BCBS239 using his “BCSB239 Model driven solutions checklist” tool.10
- 11. BCBS239 Programme management tool • Each row is a obligation paragraph • Columns for o Output • Obligation # • Section • Description etc • Evidence required o Input • Level of compliance • Level of evidence • Priority • Owner • Tool calculates o Readiness score for each obligation o Programme reediness score o Averages 11
- 12. Breakouts For your BCBS239 paragraphs report back on • Appropriate evidence • How it implement in Magicdraw 12
- 13. Meet the obligations more efficiently by leveraging FIBO and semantic technology 13
- 14. 14 The Data Point Model Value Value Set Data Point Aspect Context Many Value 1 The data point meta model separates the data points and their values And treats concept relationships as data
- 15. Traditional strategy 15 Example DPM with instance data DoBs Client 1925-10-13 Thatcher 1953-05-06 Blair 1953-05-06 Cameron Data Point Model Aspect Values 1945-01-01 1953-05-06 Aspect Values Thatcher Blair Cameron DPs !6#^ 7f%$ ^rrA FQ!@ ~A¬0 Aspects DoB Client Rules
- 16. ModelDRExisting systems 16 Data Point Model architecture OpenGamma (Postgress) EBA COREP (Access) Corep Finrep (XBRL Taxonomy) Spreadsheets Data point models EBA COREP Spreadsheets Corep Finrep FIBO Open Gamma In place access Semantics View 1 View 2 View 3 View 4
- 17. Demonstration subject matter Para 33: Must have taxonomy Para 34: Data is aligned to that taxonomy Para 40: Data quality Para 50: On demand slice and dice ….. …… 17 http://www.bankofengland. co.uk/statistics/Documents /about/dqf.pdf http://www.edmcouncil.org /dataquality EDM Council Data Quality standard • Completeness, • Coverage, • Conformity, • Consistency, • Accuracy, • Duplication, • Timeliness
- 18. Demonstration 1) Model Spreadsheet report 2) Model database table 3) Enrich the data points of the reports with their Source system 4) Enrich the data points of the reports with their Time cycles 5) Synthesis a common view of the 2 data sources using FIBO 6) Assign the data points to the US GAAP 7) Provide analysis, report and data quality metrics 18
- 19. 19 1: Untangle a spreadsheet Report NameY Axis Aspect Y Axis Value Set Name Y Axis Coordinate Aspect Values Reportable Aspect X Axis Aspect X Axis Value Set X Axis Coordinate Aspect Values Reportable Aspect Values Reverse engineer
- 20. 20 Business Entity Class Level Adaptor Attribute Adaptors Adaptors Class Level Aspect Attribute Level Aspect Attribute Level Value Sets Resources 2. Untangle an existing data base Reverse engineer
- 21. 3) Enrich the data points of the reports with their Source System 21 Wire together
- 22. 4) Enrich the data points of the reports with their Time Cycles 22 Wire together
- 23. 5) Synthesis a common view of the 2 data sources using FIBO 23 Wire together FIBO Model Spreadsheet model Integrated model
- 24. 6) Assign the data points to the US GAAP 24 Reverse engineer Integrated model US GAAP model Wire together
- 25. 7) Provide analysis, report and data quality metrics You now have a complete database • Reports • Ad-hoc queries • Inferencing queries 25
- 26. Systems Data management tooling Reporting and analytics Regulator Duplication Coverage Consistency Accuracy completeness conformity 26 The model in the BCBS239 ecosystem Roles modelDR Trading systems Calculation Engine Reg Report needs (e.g. XBRL taxonomy) Spreadsheets Risk systems Data warehouses Report writer Data modelling Ontology management Data Architect Business Domain Expert Data Owner FIBO Cross DB DQ Queries Data quality metrics Meta data Analysis Definition of Good Data Quality Report Designs
- 27. Resource search terms • Data Quality Webinar: May 19th “How can I measure the quality and integrity of my data” • Paper: BCBS239 Principle of Risk Data Aggregation • Progress report: BCBS Progress in adopting the principles for effective risk data aggregation and risk reporting • Data Point Model: ECB Data Point Model • XBRL: XBRL Abstract Model 27
Editor's Notes
- It is really great to see you all here – to be talk 239 with you all makes the inner data nerd in me leave for joy.
- I say this only because I have always wanted to
- So why BCBS239? 9 years ago when I started with one of the big banks here in London I asked what data modelling tool did they use. “Whats data modelling?” was the answer. I should not have been so surprised at the “what’s data modelling” line. I have been doing data models for 30 years. For 25 of those years it was always the same depressing story – for me it was obvious. - “Separate logical of that you want to achieve from physical implementation !” – yes, of course, brilliant, that’s obvious! “Give everyone in the firm fingertip access to 100% of the data architecture !” – wow – compared to all the IP of firm being looked up in the heads of developers that’s a total game changer – brilliant”. Why? Why has data architecture been such a failure? One common reason I hear is the “I am a simple person” reason. Person 1 in front – would you chose a simple solution to a problem or a complex solution? Person 2 in front – which is the simpler tool – Magicdraw or a spreadsheet? You have just proved Magicdraw is a big waist of time because the best solution to the BCBS239 is a spreadsheet 2) Another is the mis-diagnosis of root cause of failure. It was a project on Sydney for a large insurance company……. So if a project fails is fails because of a But no. While every time I used data modelling and tooling it worked great, just as common sense and the theory they teach you at computer science 101 says it should, in 25 years I never saw a firm make a success of it.
- Firstly I think public servent types are community mined. They think of bankers as …… So they say “if we are going to indemnify you against loosing all the publics money then we want to monitor you very closely”. Hence all the regulatory reporting. You have to understand the scale of reporting they are asking for Corep and Finrep and the Data Point Model are not really reports – they are data cubes – little star schema databases. So what do these community minded public servants get? Next slides Deutsche Bank has been slapped with a multi-billion pound fine by regulators, including the largest ever handed out by UK authorities, over Libor and Euribor rate-fixing. Germany's biggest lender has been fined £227m by the Financial Conduct Authority (FCA) over Libor and Euribor misconduct and for "misleading" the regulator during its investigation. Three regulators in the US have slapped the bank with an even larger fine of $2.175bn (£1.4bn), bringing the total penalty to more than £1.6bn, the largest total fine handed out to any financial institution over the scandal.
- FCA The FCA said it had increased the fine rate (Source: Getty) Merrill Lynch International (MLI) has been fined £13.2m for failing to properly report a number of transactions between 2007 and 2014. This is the highest fine imposed by the FCA for this type of failing. The watchdog said the amount levelled at the firm “reflects the severity of MLI's misconduct, failure to adequately address the root causes over several years despite substantial FCA guidance to the industry, and a poor history of transaction reporting compliance”. The fine equates to £1.50 per line of incorrect or non-reported data, instead of the usual £1, because “past fines have not been high enough to achieve credible deterrence”. The fine relates to two groups of failings – the first totalling 35,034,810 transactions and the second a further 121,387 transactions Georgina Philippou, FCA's acting director of enforcement and market oversight, said: “Proper transaction reporting really matters. Merrill Lynch International has failed to get this right again – despite a private warning, a previous fine, and extensive FCA guidance and enforcement action in this area. "The size of the fine sends a clear message that we expect to be heard and understood across the industry. "Accurate and timely reporting of transactions is crucial for us to perform effective surveillance for insider trading and market manipulation in support of our objective to ensure that markets work well and with integrity." MLI received a 30 per cent reduction in their overall fine because it agreed to an early settlement. Without this discount the fine would have been nearly £19m.
- Traditional solutions to this challenge create the problems of data quality, slow rates and high costs of change. Left hand side examples of As Is systems are from our demonstration site. Thought experiment: Number of databases: 500 Number of tables per database: 50 Number of columns per table: 30 Number of columns: 750,000 Number of relationships between columns = 750,000! = #NUM!
- This example is from OpenGamma – which is a modern system. Uses a warehouse in the traditional approach. The reason they are doing this is that a query in current approaches uses SQL. SQL can only run against one database at a time. Therefore, for complex reg reports across multiple databases the data must be copied out to a data warehouse.
- The source data is all in different formats – it must also be made congruent, which requires complex transforms during the load –. Expensive! And then the data is in another hard coded format. New business requirement = new data warehouse. Oooh, that hurts!
- How many cans do you need to open to test a batch of bake beans?
- Secret sauce: Separating each data point from its value is unique to the DPM approach. Allows data points to be re-purposed without duplication = Agile, Massively reduced data duplication. It shares with rdf and other semantic technologies the idea of making structural and meta data explicit, in data, Enable queries to reason based on “knowledge”, New rules and relationships are introduced without database changes
- Current architecture strategies treat the value as the data point. If there are 1,000,000 clients there are 1,000,000 DoB instances. But between 1915 and 2015 there are only 36500 days to have birthday. A 30 fold duplication of data. This 30 fold inefficiency is necessary as the Client and DoB concepts are hard wired together in the Client table. The DPM breaks the data down into its atomic level – negating this constraint. The rule that all clients must have a DoB is in a Rule, not hard coded into the database structure = agile.
- In the Data Point Model based approach Data congruence is created in the DPM layer. This is a design layer – no heavy weight technology = agile. The data is used in place by preference, not transformed certainly. SPAQL query language a) allows access across multiple databases and B) has the extra power of semantics and inference. MEANS No data warehouse
- Likewise ModelDR can convert regulatory reports, spreadsheet and any other data format to a Data Point Model
- modelDR can easily transform all kinds of data, in this case an Oracle database, into a Data Point Model
- My 3 main points: - The scale of data architecture demanded by BCBS239 is massive Modelling is the only way to make it scale Within modelling the only way to scale is with the semantic DPM approach The benefit is fantastic agility and the emimination of the data warehouse The seven key dimensions associated with data quality (completeness, coverage, conformity, consistency, accuracy, duplication, timeliness). These dimensions facilitate better communications among and between stakeholders about data quality objectives, challenges and remediation approaches. Input to ModelDR are Schema from database, data models, FIBO, Reg report obligations etc Definition of good from Business Domain experts Output will be Cross database querys Data quality Report designs Meta data analysis Data quality metrics. Definitions: Core data Attributes - a composite set of all critical data attributes for all primary applications (i.e. research, trade, clear, settle, price, value, risk, compliance, books and records) throughout the full transactions lifecycle. Definitions of core attributes will be aligned with FIBO Standard Measurement criteria - working on the development of standard data quality metric categories, standard root cause categories, standard set of data governance criteria and standard set of business rules for both reference and transactional data attributes.