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

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IntroductionBLOCKCHAIN @ GFT

Source: Gartner's 2016 Hype Cycles Highlight Digital Business Ecosystems Gartner (11 August 2016)

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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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