Reimagining the Future of the Fund Industry Through Emerging TechnologiesMay 26, 2016
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PANELISTS: Simon RamosPartner, Advisory & Consulting Investment Management, Deloitte Luxembourg
Robert PalatnickManaging Director, Chief Technology Architect, Depository Trust Clearing Corporation, (DTCC)
Mary Jane AjodahSenior Associate, Client Service Delivery, BNY Mellon
Brian M. MelterManaging Director, E-Business Solutions Division, Boston Financial Data Services
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
LendingPayments
Personal FinanceEquity Finance
Retail investmentSmall Business Tools
Banking InfrastructureOther*
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Fintech is reshaping the financial services sectorInvestment 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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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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Benefits for asset managersPractical 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 applicationsHow 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
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2
Big data
D2C channels
Product management and marketing1
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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 servicesEfficiency 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
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Regtech to address continuousregulatory challenges
Confidentiality
Payment services
Smart contracts
New financial products
Consumer protection
Cyber-security
Increased regulatory requirements in a Fintech world
Regtech as supportNew, 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 edgeWhite 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
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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. platformAsset
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
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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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Blockchain/Distributed LedgerRob PalatnickManaging Director, Chief Technology Architect, Depository Trust Clearing Corporation, (DTCC)
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
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
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
Near Real-timesecure, information sharing
Standardsopen source, data, business rules the same for all
Securityeverything is encrypted, all the time, selective access
Resilienceimmutable history, many copies, many servers
Distributed Ledger Opportunities
Technology is new and evolvingfunctional, non-function, technical viability needs to be proven
There are no standardsmultiple consortiums are evaluating different ledger technologies
There are no integration toolsIntegration with enterprise systems and operations is new
Skill maturity and depthVendors are new and small, skills are almost non-existent
Distributed Ledger Challenges
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
Machine LearningMary Jane AjodahSenior Associate, Client Service Delivery, BNY Mellon
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
Machine Learning vs. Data ScienceMachine 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 Clusterin
g
Market Basket
Analysis
Sentiment
Analysis
Recommendation Engines
$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
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
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
Sourceshttp://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.shtmlBNY Mellon – Intelligent Data Solutions Team (Client Service Delivery)BNY Mellon – Client Technology Solutions Team
QuestionsBrian MelterManaging Director, E-Business Solutions Division, Boston Financial Data [email protected]
Robert PalatnickManaging Director, Chief Technology Architect, Depository Trust Clearing Corporation, (DTCC)[email protected]
Mary Jane AjodahSenior Associate, Client Service Delivery, BNY [email protected]
Simon RamosPartner, Advisory & Consulting Investment Management, Deloitte [email protected]
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?