NICSA Webinar | Reimaging the Future of the Fund Industry Through Emerging Technologies

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

Deloitte Digital Copyright © 2016 Deloitte Digital LLC. All rights reserved.

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


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

1

2

Big data

D2C channels

Product management and marketing1


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

2


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

3


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

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


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?

mailto:[email protected]




http://www.alfi.lu/node/3193

http://www.alfi.lu/node/3193

https://www.youtube.com/watch?v=wRXT5DkM4q8

NICSA Webinar | Reimaging the Future of the Fund Industry Through Emerging Technologies

Economy & Finance

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