The document discusses the application of machine learning in anti-money laundering (AML) compliance, detailing legally mandated controls for monitoring suspicious activities in financial institutions. It highlights the use of AI tools such as graph analytics and machine learning classifiers for detecting risks and managing alerts. The text also describes the multi-level analyst workflow in reviewing suspicious transactions and filing reports.

1. MACHINE LEARNING
FOR AML COMPLIANCE
INFORM Risk & Fraud
Tuesday, 5th July 2022
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reproduction, distribution, and utilization of this document and the communication of its contents to others without express
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of the grant of a patent, utility model, or design.
Ph.D. Kevin Nagel
Data Scientist / Consultant
kevin.nagel@inform-software.com

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ANTI MONEY LAUNDERING
(AML) // A set of legally
obligated controls to monitor
suspicious activity at financial
institutions:-
• Know Your Customer
• Record Management
• Software Filtering
• New Technology

3. PLACEMENT LAYERING INTEGRATION
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MONEY LAUNDERING

4. €
MATTER OF MILLISECONDS
AUTHORIZATION
RISKSHIELD
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IP

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6. All Accounts
within a bank
Level 1 analyst
flags financial
transactions
via RiskShield TM
Level 2 analyst
reviews flagged
transactions
via RiskShield SAM
Level 3 analyst
& compliance team
files Suspicious
Activity Reports
(SAR)
Flagged
Accounts
Reported
Accounts
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ANTI MONEY LAUNDERING
Suspicious
Accounts

7. LOW
Flagged
Accounts
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Level 1 analyst
flags financial
transactions
via RiskShield TM
ML classifier
predicts response
label
via RiskShield ML
Level 3 analyst
& compliance team
files Suspicious
Activity Reports
(SAR)
All Accounts
within a bank
ANTI MONEY LAUNDERING
Reported
Accounts
MID
HIGH

8. TRAINING SET
isSAR = False 9,609 instances
isSAR = True 529 instances
TEST SET
isSAR = False 9,638 instances
isSAR = True 501 instances
AMLSim dataset
(www.kaggle.com)
with 20,277 accounts
and 337,738 transactions
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9. Account
Transactions
Amount
Incoming Outgoing
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12. A1 A2
A3 A4
TRAINING SET
cond1
cond2
A4
cond2
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ANOMALY DETECTION
// ISOLATION FOREST

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14. Min. class
Maj. class
Train
Test
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YES
NO
ACTUAL
RESPONSE
LABEL
YES
PREDICTED
PROBABILITY
OF RESPONSE
PREDICTED
CLASS
LABEL
NO
NO
YES TRUE POSITVES
TRUE NEGATIVES
FALSE NEGATIVES
FALSE POSITVES
MODEL
PERFORMANCE
TRAINING
DATA
DATA INPUT MODEL OUTPUT

16. TN
CORRECT
UNSUSPICIOUS
TP
CORRECT
ALERT
FP
FALSE
ALERT
→ BACKLOG
FN
MISSED
ALERT
→ PENALTY
ACTUAL OUTCOME
NO SAR SAR
RESPONSE
PREDICTION
→ ACTION
NO SAR
→ PASS
SAR
→ ALERT
CORRECT ALERT RATE
= TP / (TP+FP)
SAR DETECTION RATE
= TP / (TP+FN)
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17. CORRECT ALERT
RATE
Abbreviations:
U = Unsuspicious
P = Penalty
S = Suspicious
B = Backlog
SAR DETECTION RATE
U
S
B
P
U
S
P
U
S
B
U
S
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19. Pre-
dicted
Proba-
bility
SAR
Pre-
dicted
Proba-
bility
NO
SAR
153
549
9,437
ML Response Prediction Response = “LOW”
Response = “MID”
Response = “HIGH”
Operating Threshold: 76%
Operating Threshold: 96%
10,139
Accounts in
TEST SET
Level 2.1 Analyst
confirms FP
Level 2.3 Analyst
approves SAR
Level 2.2 Analyst
checks alert
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MACHINE LEARNING FOR
AML COMPLIANCE // A set
of AI tools to detect risks and
criminal connections at
financial institutions:-
• Graph Analytics
• AML Alerts Triage
• Hybrid AI
ANTI MONEY LAUNDERING
(AML) // A set of legally
obligated controls to monitor
suspicious activity at financial
institutions:-
• Know Your Customer
• Record Management
• Software Filtering
• New Technology