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17 June 2019© MARKLOGIC CORPORATION
Global Markets Beyond Legislations
Edwin Raubenheimer, IT Solutions Engineer
Building ABN AMRO’s Agile Data Foundation
ABN AMROGlobal Markets Beyond LegislationsBuilding a data driven and agile foundation
Edwin RaubenheimerSolution Engineer – Global MarketsMay 15th 2019
EDWIN RAUBENHEIMER
ABN AMRO Global Markets
Solution Engineer
Data and E-Cluster
9 Years - Banking – Data, Finance, Risk and Regulations19 Years - Software Engineering
Experience
17/2019
OUR JOURNEYBuilding a Data Driven and Agile Foundation
GLOBAL MARKETSNot about shouting
WHAT DO WE DO IN GLOBAL MARKETS?CUSTOMER FACILITATION
Hedge our daily position against the exchange or other parties to mitigate risk from marketfluctuations
• FX (Spot, Forward)• Fixed Income (Bonds, CP etc)• Equities (Shares of stock)• Commodities (Oil, etc)• Interest Rate Derivatives• Loan Deposits (Money Market)
Equity Capital Market
Issuing/Listing Shares(Primary Market)
Research
Generating Capital for companies via Equities (Shares)
Selling Existing Shares(Secondary Market)
Debt Capital Market
Creating, structuring and execution of Debt
Instruments
Advisory Services
Advising, generating or structuring capital-raising solutions via Debt
Instruments (Bonds) for corporate and financial institutions
Primary Market Secondary Market ABN AMRO
Financial InstitutionsCorporates
Public Institutions
Private and Retail Clients
Investment and Hedging Customer Risk
Options Swaps
Product Offerings
Hedging Bank Risk
GLOBAL MARKETS DATA TYPES
TRADE& Post Trade
Market Static
Instrument Static
Issuers and Guarantors
Request for Quote
or Sale
Trading Venues
Orders
Over the Counter
Market Data Front Office Data Back Office Data Functions Data
Etc..
Dealer Network/platform
or Voice
Exchange/ Trading facilities
Pricing Models
Common Market Pricing
Data and Curves
Streaming Pricing Data
Market Snapshots
per venue / instrument
Time and Sales per venue /
instrument
Confirmation and
Settlementsinstructions
Risk
Market and Credit
ExposurePositions
and Valuations
Regulatory
Payments
Finance and Risk
Product Control
Models
THERE IS A NEED FOR SPEEDTHE CHANGING WORLD IN WHICH WE OPERATE – NEW WAY OF THINKING
New/Changing technology
KEY CRITERIA FOR WHERE WE WANT TO MOVE
No big design upfront Must enable a gradual
implementation of data management in GM, starting from simple store-and-forward, growing towards a sophisticated data management platform.
Query off-loading The operational systems
provided a golden copythat enables off-loading of all requests for GM data, reducing load on expensive operational systems.
Easy upload / raw data Data ingestion must be based
on raw data (preferably in XML or JSON), without modification (no filtering / transformation), and using any technology (e.g. messages, files, ESB, APIs).
Central data modelling The ODS must be optimised
for achieving full control on the complex governance of how data is modelled and published.
Multiple delivery models Data delivery must support
multiple models (i.e. rawbut also modelled data) depending on knowledge of the consumer.
Lineage in published data Data (i.e. documents)
published by the ODS always include the data lineage back to the source.
Security/Rules based Regulatory compliance (e.g.
GDPR) is ensured by applying rules to queries (e.g. retention, access) that are stored as documents as well.
No direct interfaces Data providers deliver
once to the ODS, and not directly to consumers. The ODS controls and manages the distribution of data to all data consumers.
GLOBAL MARKETSBecoming more efficient…
TECHNICAL DECOUPLE
FUNCTIONAL DECOUPLE
14
DATA MOVEMENT PATTERNS USED IN GLOBAL MARKETS
PROVIDING ENTERPRISE
Streaming Pipeline
ESB / API Gateway
GLOBAL MARKETS CONSUMING GLOBAL MARKETS
Raw Data Store
PROVIDING GLOBAL MARKETS
CONSUMING
Analytics & operational use cases
MI/BI Reporting
Enterprise use cases (e.g. reporting)
IntegratedData Store
Application
Application
Application
Other Bounded Context(s), e.g. Payments, Credits
Application
Application
ESB/
API
Gat
eway
Application
Streaming Pipeline Application
ApplicationApplication
Legend: Raw data Harmonised data Raw dataevent stream
Harmonised dataevent stream
Global Markets
HarmonisedData
Central Metadata
Raw DataStore
Global Markets
HarmonisedData(IDS)
People Systems Processes Regulations
Catalogue
Order and Trading Systems
Financial Systems(Positions)
Pricing Engines
Market Sources
Reference SourcesCollectionsDocuments
Attributes / Tags
EnvelopeMeta data
Triples
Domain ModelEntities and Relationships
Attributes
DatesCalculationsCanonical
XMLJsonCSV
A
B
C
Modelled Data
Triples
Requests
Ingest
Raw Documents Harmonised Data
Information
Delivery
Hindsight View
Data Consumers
Reporting
Finance andRisk
Regulatory
Product Control
Business
Operations
DescriptiveAnalytics(What?)
Diagnostic Analytics(Why?)
OLAP
Data Analytics
Business Intelligence
Quantitative Models
MachineLearning
AI/CIDeep
Learning/Mining
Self ServingExplanatory
ModelsPredictiveAnalytics Making it happen
Data Orchestration
Monitoring
04 LESSONS LEARNED SUMMARYOur experience and challenges
DO’S AND DONT’S
EXPERIMENT EARLY, GET PATTERNS THAT WORK FOR YOU. ML IS TOO POWERFUL
NEW PARADIGM, COMMUNICATE OFTEN, WHY? WHAT?
TRAINING… TRAINING… BOTH TECHNOLOGY AND MIND SET. INVEST…
DATA IS CHIEF, UNDERSTAND YOUR DATA
USE TOOLS TO ACCELERATE DEVELOPMENT AND MANAGEMENT
DO’S
DO’S AND DONT’S
DON’T CREATE A BLACK BOXCONNECTION TO COMMERCIAL
VISUALISATION TOOLS ARE LIMITED
DON'T EXPECT ML TO INCREASE THE SPEED OF DELIVERY IN THE SHORT TERM
DONT’SDON’T USE EXCEL FOR SOURCE TO
TARGET
DON'T BUILD SOLUTIONS ON EXPERIMENTS, START CLEAN
DON’T DUMP RAW DATA INTO ML AND EXPECT MAGIC
Thank You