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Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB
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Webinar: Real-time Risk Management and Regulatory Reporting with MongoDB

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Real-time risk management coupled with the requirements for regulatory reporting are top of mind for many heads of risk. In this webinar, we will cover how MongoDB can help with: …

Real-time risk management coupled with the requirements for regulatory reporting are top of mind for many heads of risk. In this webinar, we will cover how MongoDB can help with:

Implementing proactive risk controls
Aggregated Risk on Demand
Creating an Adaptive Regulatory Reporting Platform
Cost Effective Risk Calculations

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  • Lots of new regulations coming in, and most of them deal with Data!. Some of them ask you to keep more data while others ask you to get a holistic view across your data.
    Other than regulation, there is an increased focus on “Better Risk Management” How do you ensure that you have good risk practices and controls in place.
  • Center aligned – other option?
    Second shouldn’t be wider than first
    Document too dark green
    Missing vertical dotted line
  • comments is an array of JSON documents
    we can query by fields inside embedded documents as well as array members.
  • Each regulation requires at-least 5 changes in your architecture. Time to deliver this is 6 months!!
  • Transcript

    • 1. Real-Time Risk Management & Regulatory Reporting - Jim Duffy: Business Architect Global Financial Services - Kunal Taneja: Solutions Architect Financial Services
    • 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. 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. The Evolution of Information Management in Finance - Jim Duffy Business Architect Global Financial Services
    • 5. Evolution of Information Management Risk Compute Grid (VaR) Swaps Regulatory Reporting Platform Rates Market Abuse and Compliance Equities Markets, MTFs, Internal Liquidity, etc 5 Derivatives
    • 6. Asset Class Silos Risk Compute Grid (VaR) Regulatory Reporting Platform 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 Derivatives Markets, MTFs, Internal Liquidity, etc 6
    • 7. Cross Asset Class Data Warehouse Risk Compute Grid (VaR) Regulatory Reporting Platform 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 Derivatives Markets, MTFs, Internal Liquidity, etc 7
    • 8. Cross Asset Class Caching Layer Risk Compute Grid (VaR) Regulatory Reporting Platform 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 Data Store(s) Operational Data Store(s) Operational Systems Operational Systems Operational Systems Operational Systems Swaps Rates Equities Derivatives Markets, MTFs, Internal Liquidity, etc 8
    • 9. mongoDB as an Operational Data Layer Risk Compute Grid (VaR) Regulatory Reporting Platform 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. What is an ODL?
    • 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. mongoDB Terminology Secondary Secondary Primary US EU Swaps 12 Asia US EU Rates Asia US EU Asia Equities US EU Asia Derivatives
    • 13. mongoDB Terminology Secondary Secondary Primary US EU Swaps 13 Asia US EU Rates Asia US EU Asia Equities US EU Asia Derivatives
    • 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. 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. 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. 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. mongoDB in the context of Risk
    • 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. 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. 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. 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. 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. 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. Aggregated Risk on Demand - Kunal Taneja Solution Architect Financial Services
    • 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. 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. 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 Time?? Operational Systems Operational Systems Operational Systems Operational Systems Bonds Rates Equities Derivatives 28
    • 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. 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. Aggregated Risk on Demand Why MongoDB? Risk Application (Historical Simulation) Quant Library Primary Operational Systems 31 Operational Systems Operational Systems Operational Systems Bonds Rates Equities Derivatives
    • 32. An approach with Monte Carlo Sim Representing Hierarchy “Udf_h1” 32
    • 33. Risk Repository Representing Hierarchy { "_id" : ObjectId("5277f00e8de2b30a03d9b8b2"), "book_id" : "Book_1_eq_ftse", "parent_book_id" : ”Book_1_eq", ”ancestors" : [ “Book_1_eq”, “Book_1” ], "isLeaf" : true } 33 db.ensureIndex({…,ancestors:1}) db.hierarchy.find({ ancestors: “Book_1” })
    • 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. 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. Thank You 36
    • 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. 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. FS/Banking Challenges 1. Changing Regulatory Requirements ETL Corporate Data Warehouse Corporate Data Warehouse Source Layer Source Layer Acquisition Layer Acquisition Layer Extraction & Staging Cleansing Atomic Layer Atomic Layer Normalisation & Storage Transformation && Access Transformation Access Layer Layer Transformation & Calculation Change Data Performance & Access BI Abstraction && BI Abstraction Reporting Layer Reporting Layer Web Services Dashboards & Web Reports MDM Ad-hoc reports & Analytics ! Reject Data 40 Data Lineage and Metadata

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