Carsten Weske, Neo4j

2. How come
In the United States,
AML compliance
staff increased up to tenfold
at major banks between
2012 and 2017 !
McKinsey Report:
New Frontier in AML Laundering
There is still no automated
STP Path automatisation ?

3. Cross-Influence
Credit Risk
do not lose funds
neither customer’s nor own
Due Diligence
do not support any kind of financial crime
know customer backgrounds
Payments
transfer funds from A to B
instantly and at lowest cost

4. Origins
AML Criminal Activity Onboarding Underwriting Counterparty Domestic Cross-Border
Due Diligence Credit Risk Payments
STP
Transparency
Ultimate
Entities

6. Origins | Ultimate Entities
Beneficiary name & address
Transaction service levels & fees
Fees charged to whom
Sender’s reference
Beneficiary bank details
Sender Name & address
Currency and amount (Transaction
Monitoring)
Transaction settled date
Account credited or debited
Transaction ID
Transaction start date
Intermediary bank
Sending bank
Payment message network

7. Origins | Ultimate Entities
Beneficiary name & address
Transaction service levels & fees
Fees charged to whom
Sender’s reference
Beneficiary bank details
Sender Name & address
Currency and amount
Transaction settled date
Account credited or debited
Transaction ID
Transaction start date
Intermediary bank
Sending bank
Payment message network
Zaki-ur Rehman
Lakhvi
Mastermind 2008
Mumbai attacks
one of them
School of Thought
Ahl-i Hadith
Charity Foundation
Falah-e Insaniat
Terrorist group
Lashkar-e Taiba
supportsvarious
underprivileged
schoolsinIndia

8. Origins | Ultimate Entities
Payment Messages
queue
Not suspicious
to payment
processing system
Suspicious
to investigation
Source:
ACAMS - Association of Certified Anti-Money Laundering Specialists
http://www.acams.org/wp-content/uploads/2015/08/AML-Rule-Tuning-Applying-St
atistical-Risk-Based-Approach-to-Achieve-Higher-Alert-Efficiency-U-Luccehtti.pdf
Transaction Monitoring System
Matching Rules Engine
(nickname “Bucket”)

9. Origins | Ultimate Entities
Sender Depth 1 Beneficiary Depth 2
Beneficiary Middleman
Depth 3
Sender Middleman
Depth 2
Ultimate Beneficiary
Depth 4
Ultimate Sender
Depth 3
Ultimate Sender
Depth 3
Depth MySQL Neo4j
2 0.016 0.010
3 30.267 0.168
4 1,543.505 1.359
5 Not finished in 1 hour 2.132
Execution time in seconds for 1,000 users
https://neo4j.com/news/how-much-faster-is-a-graph-database-really/
Real Transaction Depth 5
not automated = not scalable

10. KYC / DD
• Onboarding, PEP,
Watchlists, CRM,
News Aggregators
• Transaction History,
Payment Patterns
Origins | Ultimate Entities
Screening
Analysis
• effective false positive / negative
reduction
• improved detection capability
(i.e. fraud, mirror trades, criminal
activity funding, tax evasion)
• lower opex
• faster decisions equals faster
funds releases

12. Origins | STP Transparency
SWIFT
40 USD
Paris, France
EUR
Fax
0 USD
Henan, China
Renminbi
7 days to pay invoice

13. Origins | STP Transparency
1 day 3 days
SWIFT
SWIFT
Cirrus
Cirrus
Fax
20 USD
Geneva, Swiss
EUR
20 USD
Zhenzhou, China
EUR -> Renminbi
40 USD
Paris, France
EUR
2 days 1 day
Fax
Link
Fax
SWIFT
Link
15 USD
Yokohama, Japan
USD -> Renminbi
10 USD
Rome, Italy
EUR -> USD
0 USD
Henan, China
Renminbi
SWIFT
Fax
80 USD
Singapore
EUR -> Renminbi
1 day
1 day
1day
5days

14. Payment Efficiency
Data Files
• Correspondent
Management System
• Rating Agencies
• KYC & DD
Origins | STP Transparency
Screening
STP Transparency
• always optimal path
• less manual payment repairs
• end to end transparency
• full integration with compliance
• less need for recouping funds
• loop back into payment repairs

16. Origins Not Symptoms
lower risklower cost
closer to real-time
get 3 out of 3, not 2 out of 3