Fs.real timerisk.webinar.20131217.vnow

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Fs.real timerisk.webinar.20131217.vnow

  1. 1. Real-Time Risk Management & Regulatory Reporting - Jim Duffy: Business Architect Global Financial Services - Kunal Taneja: Solutions Architect Financial Services
  2. 2. Agenda •  The Challenges •  Evolution of Information Management in Finance •  Common Positioning of mongoDB for Risk & Regulatory •  A bit about mongoDB terms •  How our cluster topology addresses Risk & Regulatory Challenges •  Aggregated Risk on-Demand 2
  3. 3. FS/Banking Challenges Changing Regulatory Requirements SWAPS Push Volker Rule – Out – Dodd EU Reg on Dodd Frank Frank Credit Recovery & Rating Resolution EMIR Agencies EU Transparency Directive PRIP Short Selling 2012 Crisis Management 2013 PD Close Out Netting Securities Law Directive (SLD) 3 FATCA CRDV Financial Transaction Tax Cross ICB / Competition ICB Ring-fencing Border Debt Recovery 2014 ICB Loss Absorbency MiFID II T2S 2015 Internal Governance Audit Guidelines Accounting Policy AIFM Directive Directive Market Review Abuse Directive (MAD II) 2016 LCR – Basel III 2017 NSFR – Basel III 2018 2019 Leverage Ratio Basel III
  4. 4. The Evolution of Information Management in Finance - Jim Duffy Business Architect Global Financial Services
  5. 5. Evolution of Information Management Regulatory Reporting Platform Risk Compute Grid (VaR) Swaps Rates Market Abuse and Compliance Equities Markets, MTFs, Internal Liquidity, etc 5 Derivatives
  6. 6. Asset Class Silos Regulatory Reporting Platform Risk Compute Grid (VaR) Market Abuse and Compliance Reference Data Reporting Reporting Reporting Reporting Warehouse / Repository(s) Warehouse / Repository(s) Warehouse / Repository(s) Warehouse / Repository(s) Operational Data Store(s) Operational Data Store(s) Operational Data Store(s) Operational Data Store(s) Operational Systems Operational Systems Operational Systems Operational Systems Swaps Rates Equities Markets, MTFs, Internal Liquidity, etc 6 Derivatives
  7. 7. Cross Asset Class Data Warehouse Regulatory Reporting Platform Risk Compute Grid (VaR) Market Abuse and Compliance Reference Data Reporting Cross Asset Class Warehouse / Repository(s) Operational Data Store(s) Operational Data Store(s) Operational Data Store(s) Operational Data Store(s) Operational Systems Operational Systems Operational Systems Operational Systems Swaps Rates Equities Markets, MTFs, Internal Liquidity, etc 7 Derivatives
  8. 8. Cross Asset Class Caching Layer Regulatory Reporting Platform Risk Compute Grid (VaR) Market Abuse and Compliance Cross Asset Data Warehouse Reference Data Data Services / Reporting In Memory Cache, Replication and Relational Database Technology Operational Data Store(s) Operational Systems Operational Systems Operational Data Store(s) Operational Data Store(s) Swaps Rates Operational Systems Equities Markets, MTFs, Internal Liquidity, etc 8 Operational Systems Derivatives
  9. 9. mongoDB as an Operational Data Layer Regulatory Reporting Platform Risk Compute Grid (VaR) Market Abuse and Compliance Cross Asset Data Warehouse Reference Data Data Services / Reporting Operational Data Layer (ODL) Operational Systems Swaps Operational Systems Rates Operational Systems Equities Markets, MTFs, Internal Liquidity, etc 9 Operational Systems Derivatives
  10. 10. What is an ODL?
  11. 11. 4 Important Terms •  Shard: Essentially a partition of horizontally scaling data •  Replica: Copies of data for high availability, redundancy and work load isolation •  Shard Tagging: Method of dispatching data in a cluster •  Replica Tagging: Method of isolating work loads in a cluster 11
  12. 12. mongoDB Terminology Secondary Secondary Primary US EU Swaps 12 Asia US EU Rates Asia US EU Asia Equities US EU Asia Derivatives
  13. 13. mongoDB Terminology Secondary Secondary Primary US EU Swaps 13 Asia US EU Rates Asia US EU Asia Equities US EU Asia Derivatives
  14. 14. mongoDB Terminology Shard: A subset of a horizontally scaling data set Shard Secondary Secondary Primary US EU Swaps 14 Asia US EU Rates Asia US EU Asia Equities US EU Asia Derivatives
  15. 15. mongoDB Terminology Shard: A subset of a horizontally scaling data set Replica: A copy of a data set for high availability, redundancy and work load isolation Shard Replica Secondary Secondary Primary US EU Swaps 15 Asia US EU Rates Asia US EU Asia Equities US EU Asia Derivatives
  16. 16. mongoDB Terminology Shard Tagging: Dispatches writes by asset class and geography Secondary Secondary Primary US EU Asia Swaps 16 US EU Rates Asia US EU Asia Equities Shard Tag By Asset Class and Geography US EU Asia Derivatives
  17. 17. mongoDB Terminology Shard Tagging: Dispatches writes by asset class and geography Replica Tagging: Ensures isolation of work loads Replica Tag dedicated to the Intraday VaR data service Secondary Secondary Primary US EU Asia Swaps 17 US EU Rates Asia US EU Asia Equities Shard Tag By Asset Class and Geography US EU Asia Derivatives
  18. 18. mongoDB in the context of Risk
  19. 19. Active Risk Control Framework Task: Implement globally consistent active risk controls while maintaining local governance of asset class specific controls Secondary Secondary Primary US EU Swaps 19 Asia US EU Rates Asia US EU Asia Equities US EU Asia Derivatives
  20. 20. Active Risk Control Framework Task: Implement globally consistent active risk controls while maintaining local governance of asset class specific controls Blacklisted instruments centrally controlled and monitored Secondary Secondary Primary US EU Swaps 20 Asia US EU Rates Asia US EU Asia Equities US EU Asia Derivatives
  21. 21. Active Risk Control Framework Task: Implement globally consistent active risk controls while maintaining local governance of asset class specific controls Blacklisted instruments centrally controlled and monitored Secondary Secondary Primary US EU Swaps 21 Asia US EU Rates Asia US EU Asia Equities Asset Class specific controls locally governed US EU Asia Derivatives
  22. 22. Adaptive Regulatory Reporting Task: Implement a cross asset class regulatory reporting platform which will keep pace with change and enable a 360 degree view of risk Secondary Secondary Primary US EU Swaps 22 Asia US EU Rates Asia US EU Asia Equities US EU Asia Derivatives
  23. 23. Adaptive Regulatory Reporting Task: Implement a cross asset class regulatory reporting platform which will keep pace with change and enable a 360 degree view of risk MiFID2 Dodd-Frank Secondary Secondary Primary US EU Swaps 23 Asia US EU Asia Rates Libor Review, What’s coming? US EU Asia Equities US EU Asia Derivatives
  24. 24. Benefits of an Operational Data Layer •  Change management of source systems is handled by the dynamic schema •  Elimination of many data stores for one data layer cuts down cross-talk and data duplication •  Having one data layer geographically distributed allows global governance and a holistic view while not impeding local entities to function as need be •  Workload isolation is achieved via tagging data for specific use 24
  25. 25. Aggregated Risk on Demand - Kunal Taneja Solution Architect Financial Services
  26. 26. Aggregated Risk on Demand •  Regulators are pushing for “better” Risk aggregation capabilities in banking post 2007 26 http://www.bis.org/publ/bcbs239.pdf
  27. 27. Aggregated Risk on Demand Principle 4 – Completeness •  “… Data should be available by business line, legal entity, asset type, industry, region and other groupings that permit identifying and reporting risk exposures, concentrations and emerging risks” Secondary Secondary Primary US EU Bonds 27 Asia US EU Rates Asia US EU Equities Asia US EU Derivatives Asia
  28. 28. Aggregated Risk on Demand Principle 5 – Timeliness •  “…A bank should be able to generate aggregate and up to date risk data in a timely manner while also meeting the principles relating to accuracy and integrity, completeness and adaptability ….” VaR Calculator Cross Asset Data Warehouse Extract – Transform - Load Operational Systems Bonds 28 Operational Systems Rates Operational Systems Equities Time?? Operational Systems Derivatives
  29. 29. Aggregated Risk on Demand Historical Simulation •  Historical Simulation –  Recent surveys points to gaining acceptance of this methodology –  Basic versions of this methodology don’t make use of Var/CoVar •  Generate future scenarios by making use of historical market data –  1 day holding period using 220 days of history –  10 day holiday period using 2200 days etc.. •  Re-value position based on simulated return scenarios, order the loss distribution and read of and confidence level (99% VaR or 95% Var) 29
  30. 30. Aggregated Risk on Demand Why MongoDB? •  Fast access to large amounts of stored data –  Historical data spanning up to 10 years •  Parallel aggregation across stored data –  Sort time series •  Scale out and Parallel execution across stored data –  Use Map Reduce e.g. Black-Scholes •  Flexible schema (document) for storing return series –  Linear scalability and de-normalise without Joins 30
  31. 31. Aggregated Risk on Demand Why MongoDB? Risk Application (Historical Simulation) Quant Library Aggreg ation Aggregation Primary Aggregation Operational Systems 31 Bonds Aggregation Operational Systems Rates Aggregation Operational Systems Equities Aggregation Operational Systems Derivatives
  32. 32. An approach with Monte Carlo Sim Representing Hierarchy “Udf_h1” “Book_1” “Book_1_eq” “Book_1_eq_ftse” 32 “Book_1_ir” “Book_1_fx”
  33. 33. Risk Repository Representing Hierarchy { "_id" : ObjectId("5277f00e8de2b30a03d9b8b2"), "book_id" : "Book_1_eq_ftse", "parent_book_id" : ”Book_1_eq", ”ancestors" : [ “Book_1_eq”, db.ensureIndex({…,ancestors:1}) “Book_1” ], "isLeaf" : true } 33 db.hierarchy.find({ ancestors: “Book_1” })
  34. 34. Risk Repository Representing Packages { "_id" : ObjectId("527f505f3004da4f4e5b53d2"), "pkg_id" : 1, "book_id" : "Book_1", "cob_date" : ISODate("2013-12-10T00:00:00Z"), "report_status" : "O", "risk_factor" : "ftse100", "trigger_file" : "fileName", "pnl" : [ { m : 1, v : 1234.34 }, { m : 2, ………… ………… ] } 34 v: 2211.22 },
  35. 35. Risk Repository Aggregating VaR db.pkg.aggregate( List of Book’s in Hierarchy { $match : {book_id:{$in:[<book_id_list>]}, "risk_factor":"ftse100"} }, { $group:{_id:{"cob_date":"$cob_date", "report_status":"$report_status"}, "temparray":{$push:{"book_id":"$book_id","pnl":"$pnl"}}} }, Group by MC Run Id { $sort:{"_id.cob_date":-1} }, { $unwind:"$temparray" }, { $unwind:"$temparray.pnl" }, { $group:{ "_id":{"cob_date":"$_id", "mcrun":"$temparray.pnl.r"}, "var": {$sum:"$temparray.pnl.v"}} }, { $project:{"_id":0,"var":1} }, { $sort:{var:-1} }, Sort by var { $skip:100 }, Skip 100 records (1%) { $limit:1 } ) 35 Read of VaR
  36. 36. Thank You 36
  37. 37. For More Information Resource MongoDB Downloads mongoDB.com/download Free Online Training Education.mongoDB.com Webinars and Events mongoDB.com/events White Papers mongoDB.com/white-papers Case Studies mongoDB.com/customers Presentations mongoDB.com/presentations Documentation docs.mongodb.org Additional Info 37 Location info@mongoDB.com
  38. 38. Aggregated Risk on Demand Risk on Demand != Brute Force •  Event Driven Architecture •  Intelligent application design Marketdata Trades open_position{ "Asset_Type" : "Equity" "Exchange Currency" : "USD" "Underlying" : "IBM" "Exchange" : "Nasdaq” “Date_History” : {“03/10/13’, “02/10/13”, “01/10/13”, “30/09/13” ………} ”Price_History” : {”184.96”, ”183.00”, ”185.12”, ”181.00”, ”179.00”………..} Portfolio Mapping } MTM VaR 39
  39. 39. FS/Banking Challenges 1. Changing Regulatory Requirements ETL Corporate Data Warehouse Source Layer Acquisition Layer Extraction & Staging Cleansing Atomic Layer Normalisation & Storage Transformation & Access Layer Transformation & Calculation Change Data Performance & Access BI Abstraction & Reporting Layer Web Services Dashboards & Web Reports Data not null MDM Data in right format Data within range Ad-hoc reports & Analytics ! Reject Data 40 Data Lineage and Metadata

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