Technology innovation enables us to reimagine new ways of doing business and more importantly, how we service our clients. In this webinar, panelists will discuss recent research on new financial technologies with a specific focus on blockchain, robo-advisors, and machine learning.

1. Reimagining the Future of the Fund
Industry Through Emerging Technologies
May 26, 2016
SPONSORED BY:

2. MODERATOR:
PANELISTS:
Simon Ramos
Partner, Advisory & Consulting Investment Management, Deloitte Luxembourg
Robert Palatnick
Managing Director, Chief Technology Architect, Depository Trust Clearing
Corporation, (DTCC)
Mary Jane Ajodah
Senior Associate, Client Service Delivery, BNY Mellon
Brian M. Melter
Managing Director, E-Business Solutions Division, Boston Financial Data Services

3. How will Fintech impact fund
distribution?
Fintech is reshaping the operating model of asset managers, distribution
intermediaries and their service providers
Simon Ramos
Partner – Advisory & Consulting, Deloitte Luxembourg

4. 274
269
133
121
95
82
67
269
Lending
Payments
Personal Finance
Equity Finance
Retail investment
Small Business Tools
Banking Infrastructure
Other*
Fintech is reshaping the financial services sector
Investment in Fintech is growing globally Europe has a dynamic FinTech ecosystem
Active Fintech clusters, number of firms
 Emerging technological innovation in the financial sector
 Global Investments: from 4 billion USD in 2009 to 12.2
billion USD in 2014; US is at forefront
 EU showing promise: around 2 billion USD in 2014
 Lower costs, more efficiency: threat to financial
institutions
 FinTech partner with non-financial digital giants (Google)
 Asset managers to pay attention and act now
 Amazon Payments, PayPal, Rakuten, Yapital established
quarters in Europe, along with Startups
 Online discretionary portfolio service
 Storage and payment solutions for Bitcoin
 Payment solutions for (e-)merchants based on e-
wallet
 Anti-Money Laundering software
 Customer risk-profiler for Financial Advisers
 Free financial calculation tools for private
individuals
 Human Recognition Technology analyzing
behavioral data
 Stock market forecasting tools
 Incubators attracting new companies
 Proximity to clients, IT infrastructure, government
support, public research, talent pools in finance and
technology
*Other: Small business tools, crowdfunding, security and fraud, remittances, financial technology investments, financial technology investments
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5. Product
management &
marketing
New generation of investors
Technological innovations Data surge
• Stay in control (do it yourself)
• Tailored & multi channel advice
• Peers and social networks
• Digital front-end
• Downside protection and
hedging
• Yield and service of WM clients
• Social responsible investments
• Blockchain
• Digital payments
• Machine learning
• Digital investment platforms
• P2P lending
• Growing quantity of sources and
data
• Need for powerful data processing
• Allow for real-time information
• Shifting mix of required analytical
capabilities
— Algorithmic
— Predictive
Now
• AIFM, UCITS V,
PRIIPS, MiFID,
AMLD IV, CRD,
EMIR
Complex and evolving
regulatory landscape
Trade order
processing
Post trade
servicing
Tomorrow
• Shared ledger
• Smart contracts
• Online payment
• Data
confidentiality
• Cybersecurity
1 2
Regulation and Cybersecurity 3
Fund distribution
value chain
Mega trends are driving the Fintech revolution in
investment management
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6. Benefits for asset managers
Practical applications
 Tailor advice and products to client
needs
 Improve marketing strategies
 Tailored market intelligence
 Provide digital front end
 RegTech and Cybersecurity
 Intuitive guided online platforms
 Goal-based investments
 Robo advisory
 Engage with the end investor
 Offer better risk data analytics
 Improve wealth reports
Fintech applications
How can Fintech facilitate?
 Portfolio and transaction data
 AML & KYC client data
 Collect and analyze data
 Make sense of data
 Investor proximity with asset
managers
 Social investments
 Enhanced online platforms (more than
execution)
 Growing interest in execution only
 Banks and investment firms are
moving
Big data and the growing use of D2C channels see very tangible Fintech applications
1
2
Big data
D2C channels
Product management and marketing1
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7. Asset servicing: Blockchain and
other initiatives
Hybrid asset servicing transition model
Blockchain Ongoing initiativesNo ‘big bang’ blockchain
transition, but rather a hybrid
asset servicing model will offer
cost efficient and automated
services to asset managers
• Disruptive new technology providing access to
smart contracts and shared ledger technologies
• Potential process disintermediation:
• Order management
• Record keeping & ownership verification
• Asset servicing (e.g. corporate actions)
• Settlement and clearing
• Payments
• Fund order management and execution
platforms
• Digital passport
• Online KYC register
• Post trade asset servicing automation
• Automated reconciliation
• Data analytics and risk metrics
• RegTech
• T2S
Enhanced digital platforms Regtech White labelling & managed services
Efficiency for non-subjective tasks
via specialized Fintech solutions (e.g.
regulatory screening, online KYC)
• Social investments
• Tailored robo-advice
• Market intelligence
• Wealth and performance reporting
Act as a one-stop-shop managed
services and white label solutions for
asset managers
2
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8. Regtech to address continuous
regulatory challenges
Confidenti
ality
Payment
services
Smart
contracts
New financial
products
Consumer
protection
Cyber-
security
Increased regulatory requirements in
a Fintech world
Regtech as support
New, innovative and agile Fintech solutions to tackle regulatory
requirements. The proposition of Regtech principally articulates around
four advantages:
 Cost – you pay for what you use
 Flexibility – customised control over data, access to and sharing of
data
 Performance / Scalability – ability to easily add or remove service
features
 Security – data encrypted during transmission and while at rest
Regtech could provide a competitive edge
White label Regtech solutions could assist asset managers in the
following areas:
• Transactions reporting (MiFIR, EMIR, SFTR, MAD)
• Regulatory reporting (AIFMD, Solvency II)
• Regulatory watch (Automatic screening)
• Online KYC/AML/CFT
3
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9. Asset servicers can offer one-stop-shop Fintech services to asset managers
Product management and marketing
Trade processing & post trade
Transfer agent
Data
Custodian
Central administrator
Order mgmt. platform
Asset
servicers
AM1
AM2
AM3
White label Fintech solutions
• Online order management
• Risk metrics
• Online performance attribution reports
• Investment advisory algorithms (based on
investment patterns and behaviors)
• Digital payments
• Online market insights reports
Hybrid asset
servicing
model
Blockchain
1
2
D2C
Ongoing initiatives
Fintech provides opportunities for asset managers
from front to back
Improved automated reconciliation process
• Automated order aggregation
• Improved management and clearing industry
standards
• Distributed ledger KYC services
One-stop-shop managed services:
• Fund set-up and liquidation
• Distribution and registration support
• Operational tax management
• AML/CFT
• Regtech
Fintech provides opportunities for asset managers
from front to back
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10. Blockchain/Distributed Ledger
Rob Palatnick
Managing Director, Chief Technology Architect, Depository Trust Clearing
Corporation, (DTCC)

11. Abstract. A purely peer to peer version of electronic cash would allow online
payments to be sent directly from one party to another without going through a
financial institution. Digital signatures provide part of the solution, but the main
benefits are lost if a trusted third party is still required to prevent double spending.
We propose a solution to the double-spending problem using a peer to peer
network….
Bitcoin and Blockchain

12. Comparing Structural Models
DTCC Restricted (Red) Confidential Treatment Requested by DTCC/DTC/NSCC/FICC Pursuant to the Freedom of Information Act
• A central body controls
transaction recording and
distribution
• Other parties maintain
their own copies
Centralized
• All parties can hold the
same record of every
transaction
Distributed
• Multiple intermediaries
maintain local records of
transactions
• Other parties maintain
their own copies
De-centralized

13. Capability Opportunity?
Asset is built-in Bitcoin is created and exists only in Bitcoin network.
Transaction security All transactions, party information, is encrypted
Decentralized Ledger Immutable log of history, built in replication across network
Smart Contracts Standardize rules to support complex multi-party transactions
Public (untrusted)
Open, anonymous Transaction parties are “anonymous”, anyone can join
Block Mining Unknown, untrusted, database nodes incented to cooperate
Private (trusted)
Permissioned users, members Every party is permissioned, known, meets regulatory requirements
Consensus Protocols A single or set of trusted database nodes is master
Bitcoin/Blockchain brings a new database platform

14.  Near Real-time
secure, information sharing
 Standards
open source, data, business rules the same for all
 Security
everything is encrypted, all the time, selective access
 Resilience
immutable history, many copies, many servers
Distributed Ledger Opportunities

15.  Technology is new and evolving
functional, non-function, technical viability needs to be proven
 There are no standards
multiple consortiums are evaluating different ledger technologies
 There are no integration tools
Integration with enterprise systems and operations is new
 Skill maturity and depth
Vendors are new and small, skills are almost non-existent
Distributed Ledger Challenges

16.  Record Keeping:
• Immutable, traceable
records
• Reduced reconciliation
• Reference data
 Transfer of Value:
• Shorten settlement cycle
• International Payments
• Clearing and Settlement
 Smart Contracts:
• Swaps lifecycle events
• Asset/Collateral
Management
• Insurance, Mortgage, Loans
Consolidated taxonomy for disruptive innovation in Financial Services from
“The Future of Financial Services” World Economic Forum | June 2015
Potential Distributed Ledger
use in Financial Services

17. Machine Learning
Mary Jane Ajodah
Senior Associate, Client Service Delivery, BNY Mellon

18. What is Machine Learning
Recent advances in computing power and efficiency have led to an industry focus on “Machine
Learning” – covering multiple domains in the financial services industry
Machine Learning:
Any algorithm that uses a data set to optimize its
decision making capability, rather than pre-written
logical rules
Greater availability of “big data”
(high volume, real-time feeds)
Advances in computational
processing and data storage
Industry interest in practical
Machine Learning applications
"Humans can typically create one or two good models a
week; machine learning can create thousands of models
a week.“ – WSJ CIO Journal

19. Machine Learning vs. Data Science
Machine Learning algorithms can learn and improve independently – without the human
intervention needed in traditional data science
Automated model building
 Continuously discover patterns from real-
time data streams
Image / voice recognition
 Feature detection and representation
Recommendation Systems
 Collaborative (based on similar user
information) content based (similar,
complementary products), or hybrid
Core Capabilities
Decision
Predictive
Analytics
Classification
and
Clustering
Market
Basket
Analysis
Sentiment
Analysis
Recommendation
Engines

20. $45M
(6 deals)
$310M
(54 deals)
2010 2015
VC funding to AI / Machine Learning firms
totaled $967M in aggregate since 2010
• Split nearly equally btw. seed and
midstage deals
VC interest in startups focused on Machine Learning expanded greatly over the past five years
Machine Learning startups, on average,
raised $40 - $150M each (2010 – 2016
YTD)
• Many financial services firms are
investors and/or customers
Venture Capital and the Startup Ecosystem
Global Yearly Financing History

21. Many Machine Learning applications are focused on a few key use cases in the financial
services industry
Fraud Detection
 $80B annual cost (industry-wide)¹
 Automation of fraud models based on historical and real-time transaction feeds
Natural Language Processing
 Sentiment analysis of unstructured content (e.g. client correspondence, voice calls)
to determine tone and satisfaction
Intelligent Process Automation
 Machine assist on tasks performed by humans (routing, classification)
Industry Use Cases

22. BNY Mellon is a technology enabled financial services firm, and as such we are continually
investing in and innovating in the technology space
FinTech at BNY Mellon
 Robotics initiatives underway to automate time-consuming, repetitive tasks and manual, labor-intensive
functions
 Exploring Machine Learning to augment this initiative by tackling areas of decision-based work
 Dedicated teams with deep expertise in NLP, Classification, Predictive Analytics
Innovation at BNY Mellon

23. Sources
http://blogs.wsj.com/cio/2013/09/11/industrial-strength-analytics-with-machine-learning/
https://www.cbinsights.com/blog/artificial-intelligence-startup-funding-trends/
https://www.research.ibm.com/foiling-financial-fraud.shtml
BNY Mellon – Intelligent Data Solutions Team (Client Service Delivery)
BNY Mellon – Client Technology Solutions Team

24. Questions
Brian Melter
Managing Director, E-Business Solutions Division, Boston Financial Data Services
brianmelter@bostonfinancial.com
Robert Palatnick
Managing Director, Chief Technology Architect, Depository Trust Clearing Corporation, (DTCC)
rpalatnick@dtcc.com
Mary Jane Ajodah
Senior Associate, Client Service Delivery, BNY Mellon
maryjane.ajodah@bnymellon.com
Simon Ramos
Partner, Advisory & Consulting Investment Management, Deloitte Luxembourg
siramos@deloitte.lu
Additional Resources:
• “How can FinTech facilitate Fund Distribution”, paper published by Association of the Luxembourg Fund Industry (ALFI) &
Deloitte
• What’s the ‘How can FinTech facilitate fund distribution’ survey about?