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GFT‘S DATA SCIENCE PRACTICE
Blockchain Technology: The Future for
Financial Services Infrastructure Summit
Accelerating adoption in financial services
- centralised vs de-centralised business models
Nick Weisfeld – Co-head Blockchain and Head of Data Science, GFT Technologies
22nd November 2016
GFT Group 30/11/2016 2
IntroductionBLOCKCHAIN @ GFT
Source: Gartner's 2016 Hype Cycles Highlight Digital Business Ecosystems Gartner (11 August 2016)
This graphic was published by Gartner, Inc. as part of a larger research
document and should be evaluated in the context of the entire document.
The Gartner document is available upon request from [insert client name or
reprint URL].
Gartner does not endorse any vendor, product or service depicted in its
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research publications consist of the opinions of Gartner's research
organization and should not be construed as statements of fact. Gartner
disclaims all warranties, expressed or implied, with respect to this
research, including any warranties of merchantability or fitness for a
particular purpose.
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Blockchain – What’s all the fuss about?
Driver for change Vision for Blockchain enabled future
High costsDistributed transactions cleared and settled between counterparties
within seconds could reduce transaction costs
Lack of transparency Distributed data may improve market transparency reducing
requirements for regulation
Slow time-to-marketDistributed transactions cleared and settled between counterparties
within seconds could reduce transaction times
Complexity
Distributed transactions cleared and settled between counterparties
within seconds could reduced requirements for reconciliations and
regulation
Regulatory ComplianceRegulator could monitor market in near-real time. Near real time
clearing and settlement could reduce counterparty risk
Reduce Fraud Immutability reduces fraud
Reduce Risk / Market
Disruption
Cryptographically guaranteed transactions. Distributed near real time
clearing and settlement could reduce counterparty risk.
BLOCKCHAIN @ GFT
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BLOCKCHAIN @ GFT
Barriers to Adoption
Problem Description
ScalabilityTechnology some way to go before meeting non-functional
requirements
Asset representation Some big questions on how you tokenise assets into a
blockchain
Smart contracts Do the coders become lawyers or the lawyers become coders?
Interoperability Extensive work is required to build robust interphases
Standardisation Standardization is required to drive adoption
RegulationRegulators and central banks are enthusiastic but a way to go
before endorsement
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BLOCKCHAIN @ GFT
Emerald – Does Ethereum Scale for Clearing?
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BLOCKCHAIN @ GFT
Potential adoption models
Adoption Model Description Example
Open source Solution developed in collaborative public manner Linux foundation
Licenced Product Solution developed by a privately funded FinTechRipple
Circle
Platform as a ServiceMoving a defined solution to a platform to deliver a
range of products or solutions
Amazon moving
from a bookshop to
a market place
Distributed
peer-to-peer funded
model
A model that brings investors and innovators
together in a distributed peer-to-peer innovation
accelerator
DAO
Consortium based Solution developed by an industry consortium R3 CEV
Existing Centralised
Utility Solution developed by an existing centralised utility
Swift
CLS
Adoption through a
Service Provider
Solution owned by a light weight service provider
with a remit for driving adoption as well and
platform maintenance
Linux Red Hat
Led by regulator or
central bank
Solution prescribed by governmental central
authorityBank of England
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GFT at a Glance – Focus on dataBLOCKCHAIN @ GFT
Canada (12)
Toronto
USA (80)
Boston
New York
Costa Rica (92)
Mexico (116)
Heredia
Mexico City
Brazil (676)
Alphaville
Curitiba
São Paulo
Sorocaba
UK (237)
London
Germany (289)
Bonn
Eschborn/
Frankfurt
St. Georgen
Stuttgart
Italy (551)
Florence
Genoa
Milano
Montecatini Terme
Padova
Piacenza
Siena
Torino
Spain (1,859)
Alicante
Barcelona
Lleida
Madrid
Valencia
Zaragoza
Switzerland (49)
Poland (532)
Lodz
Poznan
Warsaw
Basel
Zurich
Peru
Lima
(founded 2016)
Data Science Experts
250Data Innovation Specialists
50
Employees
4700+Blockchain Specialists
15
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Innovation, Automation & Simplification BLOCKCHAIN @ GFT
Natural Language Processing Sentiment, intent and information extraction from
unstructured data of human texts for process
automation
Machine Learning
Algorithm development to support data-driven
predictions and decision-making
Blockchain Distributed ledger architecture, design and build
supporting use case adoption in financial
services
Robo Advisorycombining natural language processing and
machine learning to automate advisory
processes
Open API / PSD2Architecture, design and implementation to
enable open API access to bank data
Data lineage AutomationBCBS239 in production. Moving from manual
data lineage construction to a federated
approach
MIFID2 Front-to-back data profiling to support near
zero false exception management on go-live
FRTBCompliance advisory including risk model
efficiency and calculation path analysis
Front-to-bank big data platformingdesigning and implementing in-memory and
near real-time streaming of data for strategic
regulatory solutions
Credit Scoringreal-time calculation of the credit rating
of millions of credit contracts, enabling
early risk recognition
Fraud Detectionwith real-time monitoring of card transactions
for protection against fraud and abuse
Big Data in the Clouddata estate cost reduction and agility by
migrating on premise to latest cloud solutions
INNOVATION BIG DATA & ANALYTICS DATA MANAGEMENT
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Introduction to PanellistsBLOCKCHAIN @ GFT
RICHARD CROOK
Head of Innovation Engineering, Royal Bank of Scotland
Richard leads a team of engineers and innovators looking at emerging
technologies and their application across RBS to reduce costs, gain
efficiency and better customer experiences. His current focus is on the
application of distributed ledger technology including block chain across
RBS. Richard has a 15 year career in investment banking technology,
specializing in the building of financial ledgers and regulatory reporting
for the largest financial service institutions.
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Introduction to PanellistsBLOCKCHAIN @ GFT
ADRIAN POOLE
Head of Financial Services for Google Cloud Platform
Adrian is responsible for helping Google’s clients adopt and use its
Enterprise Cloud technologies. This includes the Google Computer
Engine, App Engine, BigQuery, Machine Learning and Big Data
solutions. Before joining Google, Adrian was Head of Sales for Grant
Thornton's Management Consultancy in Banking and Capital Markets, as
well as leading the development of Key Account Management for GT
UK. Prior to this, Adrian spent 14 years working for IBM, where he was
responsible for a team of Client Executives, Technical Architects and
Business Development Executives. Collectively, they led a series of multi-
million dollar opportunities across Infrastructure, Securities Servicing,
Corporate Banking and Capital Markets.
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Introduction to PanellistsBLOCKCHAIN @ GFT
JULIAN EYRE
Head of GFT’s Commodities and Blockchain Practices
Julian has extensive experience in working with clients to support
innovation and business solutions across the enterprise. A successful
Sales and Business Development Manager, Julian creates longstanding
customer relationships between GFT and its clients, and works
proactively to identify their key business needs and the solutions
necessary to meet tactical and strategic requirements. Julian joined GFT
UK from OpenLink in 2015, the leading Energy Trading and Risk
Management solution provider.
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Introduction to PanellistsBLOCKCHAIN @ GFT
NICK WEISFELD
Head of GFT’s Data and Blockchain Practice
Nick has been leading GFT’s Data Practice for the last four 4 years with a focus
on bringing new and innovative technology and services to market. With
extensive experience identifying and responding to industry trends early, Nick
has built successful propositions with big data analytics, simplification and
automation themes. In partnership with the fintech community and Google’s
Cloud Platform, Nick helped develop GFT’s Blockchain Incubator that aims to
advance blockchain ideas and insights by providing insights through rapid
prototyping and performance benchmarking. Some of the most recent use cases
to pass through the incubator have been ideas related to commodities, clearing
and settlements. Most recently Nick has been is building propositions to improve
customer experience through advanced analytics, automation and machine
learning techniques.
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