Chapter 9 anti-money laundering

Chapter 9
Anti-Money Laundering
The Presentation Slides for Teaching Regulatory Technology
Website : https://sites.google.com/site/quanrisk
E-mail : quanrisk@gmail.com
Copyright © 2021 Dr. LAM Yat-fai

Declaration
 Copyright © 2021 Dr. LAM Yat-fai
 All rights reserved. No part of this presentation file may be
reproduced, in any form or by any means, without written
permission from Dr. LAM Yat-fai.
 Authored by Dr. LAM Yat-fai (林日辉),
Chief Data Scientist, CapitaLogic Limited,
Adjunct Professor of Finance, City University of Hong Kong,
Doctor of Business Administration,
CFA, CAIA, CAMS, CFE, FRM, PRM, MCSE, MCNE.
Copyright © 2021 Dr. LAM Yat-fai 2

 Regulatory requirements on AML
 Remote account opening
 Facial recognition
Outline
3
Copyright © 2020 CapitaLogic Limited

CAP 615 Anti-Money Laundering and Counter-Terrorist
Financing Ordinance (AMLO)
 To provide for the imposition of requirements relating to customer
due diligence and record-keeping on specified financial institutions
and designated non-financial businesses and professions
 To provide for the powers of the relevant authorities and regulatory
bodies to supervise compliance with those requirements and other
requirements under this Ordinance
 To provide for the regulation of the operation of a money service
and the licensing of money service operators
 To provide for the regulation of the operation of a trust or company
service and the licensing of trust or company service providers
 To establish a review tribunal to review certain decisions made by
the relevant authorities under this Ordinance
 To provide for incidental and related matters
Copyright © 2020 CapitaLogic Limited 4

AMLO
Part Topic
1 Preliminary
2
Requirements relating to customer due diligence and record
keeping
3 Supervision and investigations
4 Disciplinary actions by relevant authorities
5 Regulation of operation of money service
6 AML and CTF Review Tribunal
7 Miscellaneous Provisions

AML regulators for FIs
Financial Services
and the
Treasury Bureau
Hong Kong
Monetary
Authority
Banks
Insurance
Authority
Insurance
companies
providing long
term insurance
Securities
and Futures
Commission
Securities firms
and asset
management firms

AML Guideline
 The HKMA’s Guideline
on Anti-Money
Laundering and
Counter-Terrorist
Financing
 Revised October 2018

Major regulatory AML components
AML
regulations
Know your
customer
Basic
CDD
Enhanced
CDD
Ongoing
monitoring
Suspicious
transactions
Sanctions list
matching
Transaction
monitoring
Internal
investigation
Exception
Assessment
Reporting
Risk based
approach
Record
keeping

Basic CDD information
 Identity document showing
 Full name
 Date of birth
 Nationality
 Document number
 Residential address provided by customer in
the application form

Revenue estimation information
 Type of account
 Initial deposits amount
 Residential address
 Language
 Age
 Occupation
 Investments
 Mobile phone
 Outlook
 Dress
 Conversation
 People around

Watch list searching
 Search a customer name in a watch list database
containing
 Sanctioned persons designated by
 the United Nations
 the home country of the FI
 other major countries
 Politically exposed persons
 Other watched persons
 Critical criminals
 Practitioners of industries with higher ML exposures
 Other black listed entities
 Major connected parties of above

Commercial watch list databases
 Refinitiv World-Check
 Dow Jones RiskCenter
 Lexis-Nexis Identity Verification

Sanctions
 Sanctioned person
 Government designated person to whom funds,
financial services and/or economic resources cannot be
provided
 Statutory sanctions lists in Hong Kong
 United Nations sanctions list
 Excluding persons in China
 Non-statutory sanctions lists in Hong Kong
 US OFAC sanctions lists
 European Union sanctions lists
 and many more

Politically exposed person (PEP)
 Defined in the AMLO
 Deemed to be higher risk
 An individual who is entrusted with a prominent
public function outside the People’s Republic of
China
 Head of state, head of government, senior politician,
senior government, judicial or military official, senior
executive of a state owned corporation and an
important political party official
 A spouse, child, parent or partner of a PEP
 An entity having close relationship with a PEP

Domestic PEP
 Defined in the AML guideline
 Not defined in the AMLO
 The major group of higher profit customers in Hong Kong
 Watched but not deemed higher risk
 An individual who is entrusted with a prominent public
function inside the People’s Republic of China
 Head of state, head of government, senior politician, senior
government, judicial or military official, senior executive of a
state-owned corporation and an important political party official
 A spouse, child, parent or partner of a domestic PEP
 An entity having close relationship with a domestic PEP

International organization PEP
 Defined in the AML guideline
 Not defined in the AMLO
 Watched but not deemed higher risk
 An individual who is or has been entrusted with a
prominent function by an international organization
formed and recognized by a group of country
governments
 Senior management, i.e. directors, deputy directors and
members of the board or equivalent functions
 A spouse, child, parent or partner of a IO PEP
 An entity having close relationship with a IO PEP

Know your customer
Basic customer due diligence
Risk assessment
Regular
Open account
Other watched persons
Potential revenue analysis
Poor potential
revenue
Reject customer
Good potential revenue
Higher risk
Enhanced CDD
Justifications of
no ML activities
Open account
Medium risk
Enhanced
monitoring
Justifications of
no ML activities
Open account
Sanctioned
Report to JFIU

Enhanced CDD
 More customer information
 Beneficial owner
 Purpose of the account
 Employment background
 Business background
 Family background
 Estimated net worth
 Source of wealth
 Source of funds
 Referees
 Most of the information cannot be verified

A transaction with a counterparty
Counterparty

Counterparty information
 Country
 Financial institution
 Account name
 Account number
 Unverified address

Sanctions list matching
 To assess whether the counterparty matches
any entity in the sanctions lists
 To be performed before processing a
transaction
 Sanctions list matching of the counterparty as
the primary tool
 Use computer systems

Sanctions list matching
Counterparty name
Not matched in the
sanctions lists
Process
transaction
Matched in the sanctions lists
Internal investigation
False positive
Process
transaction
True positive
Report to
the JFIU

What is match?
 Objective
 A and B are the same person
 You will never know
 Sanctions list matching
 Name A and name B look the same
 Name A and name B sound the same
 Name A and name B mean the same

Name alternations
 They look the same
 Completeness Philip W.M. LI vs Philip LI
 Order Philip LI vs LI Philip
 They sound the same
 Soundex Philip LI vs Philippe LEE
 They mean the same
 Synonym Philip LI vs Phil LI
 Trade name HKU SPACE
 Short name SPACE
 Commercial code 0484 3849 3838

Matching algorithm
 The rules which define a counterparty name as “match”
or “not match”
Most stringent, least sensitive
 First name + Middle name + Last name + Country
 First name + Middle name + Last name + Region
 First name + Middle name + Last name
 First name + Last name
 …
 ..
 .
Least stringent, most sensitive
 There is no partial match

False positive
 The counterparty name identified as “match”
by the matching algorithm, after investigation,
is not an entity designated in the sanctions list
database
 The more sensitive the matching algorithm, the
more false positive

How to calibrate
the matching algorithm?
 Regulator’s expectation
 As sensitive as possible
 Financial institution’s expectation
 As insensitive as possible
 Rule-of-thumb
 To produce the number of matched counterparties
such that you can complete all internal
investigations by 6 pm every day on average

Transaction monitoring
 A 360 degree process to identify suspicious ML
transactions, taking into account
 Customer
 Customer profiles
 Account history
 Many transactions
 Original parties
 Destination parties
 Amounts
 Frequencies
 Patterns

Exception –
Far from norm, close to suspicious scenario
Norm
Transaction
29
Suspicious
scenario
Threshold

Transaction monitoring
Transactions
Regular Exception
Internal investigation
False positive
Internal report
True positive
Report to
the JFIU

Exceptions
Exception Highlighted by
Customer name Sanctioned entity
Watch list
searching
Counterparty
name
Matched
counterparty
Sanctions list
matching
Transactions
Transactions close to
suspicious scenario
Transaction
monitoring

Assessment and reporting
Exception
Assessment
False positive
Internal
assessment
report
True positive
Suspicious
transaction
report

Outline
33

HKMA circular 1 Feb 2020
 Identity document authentication
 Where the individual customer’s identity is obtained through electronic
channels, such as transmission of a document image acquired by
mobile application, AIs should take appropriate measures to ensure
reliability of the document, data or information obtained for the
purpose of verifying the customer’s identity
 This includes utilising technology to ascertain the genuineness of the
identity document by, for example using holograms detection or
detection of security features of identity documents; and
 Identity verification
 AIs should use appropriate technology to link the customer
incontrovertibly to the identity provided
 e.g. biometric solutions like facial recognition and liveness detection

Mobile bank account opening
https://www.hkma.gov.hk/gb_chi/smart-consumers/account-opening/whats-new/

Mobile securities account opening
https://www.youtube.com/watch?v=5XKZZCsjLwY

SFC circular 1 Sep 2020 (1)
 Identity document authentication
 Access the embedded data in the client’s official ID Document such as
a biometric passport or an identity card, or obtain an electronic copy of
the relevant sections of the ID Document, including a high-quality
photograph of the client.
 Use appropriate and effective processes and technologies to
authenticate the client’s ID Document. For example, check the security
features of the ID Document or verify the data using a reliable and
independent source. In the case of a biometric passport, authentication
may include scanning the data page, capturing data through optical
character recognition and checking the captured data against the client’s
personal information stored in a chip in the passport.

SFC circular 1 September 2020 (3)
 Identity verification
 Use appropriate and effective processes and technologies to obtain the
client’s biometric data and match it with the authenticated data in the
client’s ID Document or other reliable and independent sources to
verify the client’s identity. For example, intermediaries may capture the
client’s facial image in real time and match it with the photograph
stored in the chip of the client’s biometric passport using facial
recognition technology.
 Implement appropriate safeguards such as data encryption and
presentation attack detection to protect the client’s biometric data and
the integrity of the identity verification process from any potential
presentation attacks

SFC circular 1 September 2020 (3)
 Designated bank account
 Successfully transfer to the intermediary’s bank account an initial
deposit of not less than HKD 10,000 or an equivalent amount in other
currencies from a bank account in the client’s name maintained with a
licensed bank in Hong Kong or an overseas bank which is supervised
by a banking regulator an eligible jurisdiction
 Conduct all future deposits and withdrawals for the client’s investment
account only through a designated bank account

Eligible jurisdiction
 Australia
 Austria
 Belgium
 Canada
 Ireland
 Israel
 Italy
 Malaysia
 Norway
 Portugal
 Singapore
 Spain
 Sweden
 Switzerland
 United Kingdom
 United States

Outline
41

Same person?

Photo on the HKID card
1
 
N points
N N-1
distances
2

Facial distances

One class historical data

Facial recognition
 When selfie and ID photo are from the same
person
 The difference between the distances should be
very small

Error in facial distances
 Error
 ID photo – selfie
 There must be error due to
 Change in face
 Change in camera
 Change in environment
 How large the errors should be?
48

Min & Max
Error 1
Error 2

Lonely figure
Error 1
Error 2

Machine learning
 For the same person’s photo ID and selfie
 Feature 1: Error 1
 One group classification problem

Chapter 9 anti-money laundering

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