"AMLO training for AMLO offficers" by AMLO, Speaker for "FinTech and New Technologies: AML/CTF Perspectives" (5 Jul 2017)

1. FinTech and New Technologies:
AML / CTF Perspectives
Anti-Money Laundering Office
5 July 2017

2. © 2017 Baker & McKenzie Ltd.
What is FinTech?
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• a contraction of “finance” and “technology”
• use of technology and innovative business models in financial services
• innovation applied to traditional financial services
• innovative financial services offerings which disrupt the existing
financial services market
• FinTech companies, tech firms, incumbent financial service providers
(e.g. banks, securities companies, insurance companies, etc.)

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FinTech Trends in Financial Markets
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Payments
• e-Payment / Mobile Payment
• P2P Value Transfer Networks
• Cryptocurrencies
Deposits and Financing
• P2P Lending
• Automated Lending
• Alternative Credit
Adjudication
• Online invoice finance
Investments
• Micro-Investing
• Social Trading Platforms
• Robo Advice
Capital Raising
• Equity crowdfunding
• Virtual Exchanges
• ICO
Capital Markets
• AI
• Automated Trading Platforms
• Blockchain and Smart
Contracts

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FinTech Risk Events
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Fintech Risk Events
1. Entity
2. Publication
Date
3. Country 4. Category 5. Event Description
6. Event
Type
7. Loss
Amount
8. Links
MtGox Jun 2011 Japan Cryptocurrency Security Breach EF
2,609
BTC
Wikipedia
Clinkle Jan 2014 US Payments API Security Breach EF N/A Techcrunch
Wonga
April - Oct
2014
UK Payday Loans
Bad Debt Collection
Practices, Debt writeoffs
CPBP
~240mln
Pounds
Wikipedia
MtGox Dec 2014 Japan Cryptocurrency Theft IF
744,408
BTC
Wikipedia
TrustBuddy Aug 2015 Sweden P2P Lending
Misuse of client funds,
Bankruptcy
IF N/A
FT,
Telegraph
Prosper Dec 2015 US P2P Lending
Enabling financing to
suspected terrorist
CPBP N/A LAT
Powa
Technologies
Feb 2016 UK POS, Payments Bankruptcy
Business
Failure
N/A Wikipedia
Ezubao Feb 2016 China P2P
Fake investment products
to one million retail
investors
CPBP $7.6bln NYT
Dwolla Mar 2016 US
Online
Payments
Poor Consumer Data
Protection Practices
CPBP $100k WSJ
LendingClub May 2016 US P2P Lending
Altering Loan
Information
IF,CPBPN/A Wikipedia
Slock.io Jun 2016 Germany Cryptocurrency Software Hack EF, BE
3,641,694
ether
NYT
Bitfinex August 2016
Hong
Kong
Cryptocurrency Security Breach EF
120,000
BTC
Reuters
LendUp
September
2016
US Payday Loans
Misleading borrowers
about pricing, failing to
report credit information
CBPB
$6.4
million in
fines
WSJ
Wonga April 2016 UK Payday Loans Client Data Breach EF N/A BBC
Prosper May 2017 US P2P Lending Overstating Returns BE N/A Bloomberg
Source: http://www.openriskmanual.org/wiki/Fintech_Risk_Events

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Case Study: Ezubao
- a large peer-to-peer lending platform in China
- most of the projects on the platform were fake
- police had arrested 21 people connected to Ezubao for defrauding
about 900,000 online investors (value around $7.6 billion)
- China started to regulate P2P lending platform
In Thailand, BOT will regulate P2P lending platform
 reduce risks of ponzi schemes
 requires customer identification (both investors and borrowers)

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What is blockchain?
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“A blockchain is a digital, distributed transaction ledger, with
identical copies maintained on multiple computer systems
controlled by different entities”
(Davit Schatsky and Craig Muraskin, Beyond Bitcoin: Blockchain is Coming to Disrupt your Industry, Deloitte University Press,
2015, page 2)
How does blockchain work?

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Key Elements of Distributed Ledger Technology
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Ledgers
Distributed
Ledgers
Blockchain
Crypto
currency
Cryptography to ensure identity authentication for
each transaction
Non-repudiation to preserve integrity of data and
create an audit trail.
Smart contracts for the automatic execution of
business logic when certain criteria are met.
Share ledger so each participant sees the same
view of the same data, updated in real time.
Distributed consensus to ensure the state of the ledger
represents the agreed-upon truth of all stakeholders.

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Blockchain applications
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Key use cases relating to financial services
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Global payments
• international
money transfers
• real-time
settlement
• reduce costs
• new business
models (e.g.
micropayments)
• newer models of
regulatory
oversight
Syndicated lending
• automate
syndicate formation,
underwriting and
disbursement of
funds
• reduce loan
issuance time and
operational risks
Trade finance
• store financial
details in DLT
• replace paper with
digitalized
processes via
blockchain
• facilitate real-time
approval of financial
documents
• reduce
counterparty risk &
enable faster
settlement
(eliminate
correspondent
banks)
• e.g. PoC by
BAML+HSBC+IDA
Automated
compliance
• store financial
information in DLT
• eliminate human
errors/fraud
• reduce reporting
costs
• support deeper
regulatory oversight
Equity Post-Trade
• using DLT and
smart contracts to
facilitate post-trade
activities
• disintermediate
processes
• reduce
counterparty and
operational risk
• and potentially
reduced settlement
time and costs
• e.g. ASX

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bitcoin
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 What are bitcoins?
- decentralized virtual currency / digital currency / cryptocurrency
- used to transfer and exchange values online without intermediary
trusted third party
- proof of possession and payment stored in multiple public ledgers
 allowing peer-to-peer review of bitcoin spending
 quicker and cheaper way to transfer money
 How to acquire bitcoins?
 How to use bitcoins?
 Bitcoin challenges – especially AML/CTF

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Bitcoin wallet
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Case Study: Silk Road
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• Online black market where the virtual currencies could be used to buy
illegal drugs and other goods
• Virtual currencies are anonymous  “Silk Road – Anonymous Market”
• Shut down by the FBI on 10/2/2013 and leader was arrested
• Many millions of dollars worth of BTC were confiscated from people all
over the world (US$ 100 million)
• Main Operator sentenced to life imprisonment

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Case Study: Silk Road
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Case Study: MtGox
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- was the largest bitcoin exchange in the world, handling 70 percent of all
bitcoin transactions
- thousands of bitcoin owners found their assets frozen as the exchange
shut down
- bitcoins worth more than $500 million USD had been stolen from the site

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Case study: Ransomware
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Case Study: Ethereum and DAO
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• Ethereum - open software platform based on blockchain technology that enables developers to build
and deploy decentralized applications
• The Ethereum Virtual Machine - allows creation of various types of blockchain applications on one
blockchain platform (smart contracts, loan services, title registries, voting systems, regulatory
compliance etc.)
• Ethereum was used to build Decentralized Autonomous Organizations (DAO)
• DAO - owned by everyone who purchases tokens, which are used to give people voting rights
instead of shares
• 2016 hack of a DAO (The DAO) almost lead to USD 53M in stolen token sales
• Although here problem was mitigated, evident potential risk that cryptocurrency can be stolen and
could potentially be paid out in context for ML/TF purposes

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Legal status of bitcoin by country
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Source: http://www.coindesk.com/bitcoin-legal-map/
More AML/CTF issues
- see FATF Report: Virtual Currencies: Key Definitions and Potential AML / CFT Risks (June 2014)
- EU 4th Anti-Money Laundering Directive - amended to encompass virtual currencies (2016)

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Case Study: ICO
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ICO - Initial Coin Offering, fund raising for a new cryptocurrency venture
• supporters buy some of the distributed cryptocoins with fiat or another virtual currency (similar to
crowdfunding)
• these coins are referred to as tokens and are similar to shares of a company sold to investors in
an Initial Public Offering (IPO) transaction
• ICOs don’t offer equity, instead offer discounts on cryptocurrencies before they hit the exchanges
ICO could potentially fall under some securities law:
• Comments by regulatory agencies in US suggest that desire to regulate ICOs still present problem
of approach
• Switzerland and Singapore view ICOs as assets, not securities
ICO Risks to AML/CTF
• For IPOs, intermediaries are FIs and reporting entities  subject to AML requirements
• For ICOs, no AML/KYC legal frameworks in place
• prone to be fraud/scam

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Case Study: ICO
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Case Study: Online gaming
• Chinese cyber-criminals stole e-wallet and credit card information
from small-business owners
• Using this stolen information, they purchased in-game accessories
on gaming accounts (No KYC/CDD required)
• After making the purchases, they resold the online game accounts
to buyers for cash
• The total sales of gaming accounts totaled USD 48M before being
apprehended by police

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RegTech: Basics
 Regulatory Technology
 the use of new technologies to solve regulatory and
compliance requirements more effectively and
efficiently.
(Institute of International Finance, “Regtech: exploring solutions for regulatory
challenges,” Washington DC, October 2015.)
 Relevant in the context of regulatory monitoring,
reporting and compliance benefiting the finance industry
 Two aspects the use of technology by (i) institutions and
market participants to improve the compliance function,
and (ii) regulators to enhance oversight.
 Utilizes many technologies e.g. AI, big data, blockchain,
biometrics, cloud computing

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RegTech: Advantages
 cost savings by replacing costs in manual analysis of
data and regulatory compliance
 real-time and proportionate regulatory regime
 enhance efficiency and accuracy of regulatory
compliance
 trained to identify transactions over time using many
factors: e.g. home address, time of account opening,
duration between transactions, patterns among
merchants, location of transactions, relationships
between customers and merchants, use of mobile
phone, and even change in social media presence

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RegTech: Uses
 Automate risk management processes
 Facilitate regulatory reporting
 Fraud prevention
 AML/CTF including KYC/CDD/ reporting requirements
 Examples:
 IBM Watson (AML/CTF)
 Currently used by ABANCA (Spanish FI)
 Thomson Reuters (Org ID KYC Managed Service)
 identify and classify a client’s risk category, verify their identity, and
screen and monitor all related parties to create a KYC record.
 Ayasdi System (AML+)
 Used by HSBC to reduce unnecessary investigations by 20%
 Also used to comply with FED “Stress Test”
 Accuity (KYC)

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RegTech: Market and Potential
 Global demand of RegTech to reach USD 118.7M by 2020 (Medici
report)
 GBP 2.7B could be saved globally by use of RegTech (FortyTwo Data)
 55% of False Positives could be eradicated leading to 42% Reduction
in Cost for regulating AML/CTF(FortyTwo Data)
 Companies adopting modern machine learning technologies are
expected to reduce the capacity of their compliance departments by at
least 75% by 2025 (FortyTwo Data)

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