Webinar: How Financial Organizations use MongoDB for Real-time Risk Management & Regulatory Reporting
 

Webinar: How Financial Organizations use MongoDB for Real-time Risk Management & Regulatory Reporting

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Real-time risk management coupled with the requirements for regulatory reporting are top of mind for many heads of risk; to meet the demands of new regulation financial organizations must have ...

Real-time risk management coupled with the requirements for regulatory reporting are top of mind for many heads of risk; to meet the demands of new regulation financial organizations must have technology that enables the business to easily calculate and analyze risk across products and channels. In this webinar, we will cover how organizations use MongoDB for:
* Implementing proactive risk controls
* Aggregated Risk on Demand, Creating an Adaptive Regulatory Reporting Platform
* Cost Effective Risk Calculations

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Webinar: How Financial Organizations use MongoDB for Real-time Risk Management & Regulatory Reporting Webinar: How Financial Organizations use MongoDB for Real-time Risk Management & Regulatory Reporting Presentation Transcript

  • How Financial Services Organizations use MongoDB for Real-Time Risk and 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 •  4 Important terms •  How MongoDB’s cluster topology addresses Risk & Regulatory Challenges •  Aggregated Risk on-Demand
  • 3 Challenges The single largest Challenge in Risk Management is Achieving a Holistic and up to date view of the Business
  • 4 Challenges in Regulatory Requirements 2012 2013 2014 2015 2016 2017 2018 2019 ICB Ring-fencing ICB Loss Absorbency Leverage Ratio - Basel III NSFR – Basel III MiFID II T2S LCR – Basel III ICB / Competition Audit Policy Cross Border Debt Recovery Financial Transaction Tax Market Abuse Directive (MAD II) PRIP Accounting Directive Review AIFM Directive EU Transparency Directive EU Reg on Credit Rating Agencies CRDV Internal Governance GuidelinesFATCA PD EMIR SWAPS Push Out – Dodd Frank Securities Law Directive (SLD) Volker Rule – Dodd Frank Short Selling Close Out Netting Crisis Management Recovery & Resolution
  • The Evolution of Information Management in Finance - Jim Duffy Business Architect Global Financial Services
  • 6 Evolution of Information Management Risk Compute Grid (VaR) Regulatory Reporting Platform Market Abuse and Compliance Markets, MTFs, Internal Liquidity, etc EquitiesSwaps DerivativesRates
  • 7 Asset Class Silos Warehouse / Repository(s) Risk Compute Grid (VaR) Regulatory Reporting Platform Market Abuse and Compliance Reference Data Markets, MTFs, Internal Liquidity, etc Operational Systems Operational Data Store(s) Reporting Operational Systems Reporting Operational Systems Reporting Operational Systems Reporting Warehouse / Repository(s) Operational Data Store(s) Warehouse / Repository(s) Operational Data Store(s) Warehouse / Repository(s) Operational Data Store(s) EquitiesSwaps DerivativesRates
  • 8 Cross Asset Class Warehouse / Repository(s) Risk Compute Grid (VaR) Regulatory Reporting Platform Market Abuse and Compliance Reference Data Markets, MTFs, Internal Liquidity, etc Operational Systems Operational Data Store(s) Reporting Operational Systems Operational Systems Operational Systems Operational Data Store(s) Operational Data Store(s) Operational Data Store(s) EquitiesSwaps DerivativesRates Cross Asset Class Data Warehouse
  • 9 In Memory Cache, Replication and Relational Database Technology Markets, MTFs, Internal Liquidity, etc Operational Systems Operational Data Store(s) Data Services / Reporting Operational Systems Operational Systems Operational Systems Operational Data Store(s) Operational Data Store(s) Risk Compute Grid (VaR) Regulatory Reporting Platform Market Abuse and Compliance Reference Data Cross Asset Data Warehouse EquitiesSwaps DerivativesRates Cross Asset Class Caching Layer
  • 10 Markets, MTFs, Internal Liquidity, etc Operational Systems Data Services / Reporting Operational Systems Operational Systems Operational Systems Operational Data Layer (ODL) Risk Compute Grid (VaR) Regulatory Reporting Platform Market Abuse and Compliance Reference Data Cross Asset Data Warehouse EquitiesSwaps DerivativesRates mongoDB as an Operational Data Layer
  • What is an ODL?
  • 12 4 Important Terms •  Shard: Essentially a partition of horizontally scaling data •  Replica: Copies of data for high availability, disaster recovery and work load isolation •  Shard Tagging: Method of dispatching data in a cluster •  Replica Tagging: Method of isolating work loads in a cluster
  • 13 mongoDB Terminology EquitiesSwaps DerivativesRates US EU Asia US EU Asia US EU Asia US EU Asia Primary Secondary Secondary
  • 14 mongoDB Terminology EquitiesSwaps DerivativesRates US EU Asia US EU Asia US EU Asia US EU Asia Primary Secondary Secondary
  • 15 mongoDB Terminology EquitiesSwaps DerivativesRates US EU Asia US EU Asia US EU Asia US EU Asia Primary Secondary Secondary Shard Shard: A subset of a horizontally scaling data set
  • 16 mongoDB Terminology EquitiesSwaps DerivativesRates US EU Asia US EU Asia US EU Asia US EU Asia Primary Secondary Secondary Shard Replica Shard: A subset of a horizontally scaling data set Replica: A copy of a data set for high availability, redundancy and work load isolation
  • 17 mongoDB Terminology EquitiesSwaps DerivativesRates US EU Asia US EU Asia US EU Asia US EU Asia Primary Secondary Secondary Shard Tagging: Dispatches writes by asset class and geography Shard Tag By Asset Class and Geography
  • 18 mongoDB Terminology EquitiesSwaps DerivativesRates US EU Asia US EU Asia US EU Asia US EU Asia Primary Secondary Secondary Replica Tag dedicated to the Intraday VaR data service Shard Tagging: Dispatches writes by asset class and geography Replica Tagging: Ensures isolation of work loads Shard Tag By Asset Class and Geography
  • mongoDB in the context of Risk
  • 20 Active Risk Control Framework EquitiesSwaps DerivativesRates US EU Asia US EU Asia US EU Asia US EU Asia Primary Secondary Secondary Task: Implement globally consistent active risk controls while maintaining local governance of asset class specific controls
  • 21 Active Risk Control Framework EquitiesSwaps DerivativesRates US EU Asia US EU Asia US EU Asia US EU Asia Primary Secondary Secondary Task: Implement globally consistent active risk controls while maintaining local governance of asset class specific controls Blacklisted instruments centrally controlled and monitored
  • 22 Active Risk Control Framework EquitiesSwaps DerivativesRates US EU Asia US EU Asia US EU Asia US EU Asia Primary Secondary Secondary Task: Implement globally consistent active risk controls while maintaining local governance of asset class specific controls Blacklisted instruments centrally controlled and monitored Asset Class specific controls locally governed
  • 23 Adaptive Regulatory Reporting EquitiesSwaps DerivativesRates US EU Asia US EU Asia US EU Asia US EU Asia Primary Secondary Secondary Task: Implement a cross asset class regulatory reporting platform which will keep pace with change and enable a 360 degree view of risk
  • 24 Adaptive Regulatory Reporting EquitiesSwaps DerivativesRates US EU Asia US EU Asia US EU Asia US EU Asia Primary Secondary Secondary Task: Implement a cross asset class regulatory reporting platform which will keep pace with change and enable a 360 degree view of risk MiFID2Dodd-Frank NFA, CFTC, FSA, etc MiFID2
  • 25 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
  • Aggregated Risk on Demand - Kunal Taneja Solution Architect Financial Services
  • 27 •  Regulators are pushing for “better” Risk aggregation capabilities in banking post 2007 Aggregated Risk on Demand http://www.bis.org/publ/bcbs239.pdf
  • 28 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” EquitiesBonds DerivativesRates US EU Asia US EU Asia US EU Asia US EU Asia Primary Secondary Secondary
  • 29 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 ….” Cross Asset Data Warehouse Operational Systems Operational Systems Operational Systems Operational Systems EquitiesBonds DerivativesRates Extract – Transform - Load VaR Calculator Time??
  • 30 •  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) Aggregated Risk on Demand Historical Simulation
  • 31 •  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 Aggregated Risk on Demand Why MongoDB?
  • 32 Aggregated Risk on Demand Why MongoDB? Primary Risk Application (Historical Simulation) Aggregation Aggregation Aggregation Operational Systems Operational Systems Operational Systems Operational Systems EquitiesBonds DerivativesRates Aggregation Quant Library Aggregation Aggreg ation
  • 33 “Book_1” “Book_1_eq” “Book_1_eq_ftse” “Book_1_ir” “Book_1_fx” “Udf_h1” An approach with Monte Carlo Sim Representing Hierarchy
  • 34 { "_id" : ObjectId("5277f00e8de2b30a03d9b8b2"), "book_id" : "Book_1_eq_ftse", "parent_book_id" : ”Book_1_eq", ”ancestors" : [ “Book_1_eq”, “Book_1” ], "isLeaf" : true } Risk Repository Representing Hierarchy db.ensureIndex({…,ancestors:1}) db.hierarchy.find({ ancestors: “Book_1” })
  • 35 { "_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, v: 2211.22 }, ………… ………… ] } Risk Repository Representing Packages
  • 36 db.pkg.aggregate( { $match : {book_id:{$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"}}} }, { $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} }, { $skip:100 }, { $limit:1 } ) Sort by var Skip 100 records (1%) Read of VaR Group by MC Run Id List of Book’s in Hierarchy Risk Repository Aggregating VaR
  • 37 Thank You
  • 38 For More Information Resource Location 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 info@mongoDB.com Resource Location
  • 40 •  Event Driven Architecture •  Intelligent application design 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”………..} } Aggregated Risk on Demand Risk on Demand != Brute Force Marketdata MTM Portfolio Mapping Trades VaR
  • 41 Source Layer BI Abstraction & Reporting Layer Acquisition Layer Extraction & Staging Cleansing Atomic Layer MDM Ad-hoc reports & Analytics Dashboards & Web Reports Web Services Corporate Data Warehouse Data Lineage and Metadata ETL Transformation & Access Layer Transformation & Calculation Performance & Access Change Data ! Reject Data Data not null Data within range Data in right format Normalisation & Storage FS/Banking Challenges 1. Changing Regulatory Requirements