03_Discai_Dagmar Schouterden. .pdf

1. AI as gamechanger
in Anti-Money
Laundering (AML)

2. Estimated ML globally
(2-5% of global GDP)
- source: UNODC
1-2 TLN
Detected ML
(10% of est. ML)
100-200 BLN
Money Laundering, a global issue
2
575 BLN
Projected GDP Belgium 2023
- source: IMF
Money Laundering (ML) in numbers (EUR)
© Discai nv, a Belgian entity and subsidiary of KBC Group, a financial institution listed on Euronext Brussels – All rights reserved
Recovered ML money
(1% of detected ML)
1-2 BLN

3. Estimated ML Belgium
(4,4% of GDP Belgium)
25,5 BLN
27 BLN
Projected deficit Belgium 2023
(4,7% of GDP Belgium)
- source: NBB
Money Laundering, a national concern
3
Detected ML
(10% of est. ML Belgium)
2 BLN
© Discai nv, a Belgian entity and subsidiary of KBC Group, a financial institution listed on Euronext Brussels – All rights reserved
Money Laundering (ML) in numbers (EUR)

4. Rule based vs AI based
Knowledge of investigators + bank data go into AI engine which generates
a multitude of rules
Compliance
investigators
Rules

5. Rule based
Rule X
Rule Y
Rule Z
Implementation scenarios of AI
Disadvantages of rules only set up

6. 35%
40%
AI based
Implementation scenarios of AI
Advantages of AI in AML

7. Implementation scenarios of AI
Intelligently combining AI with rules
35%
40%
Augmented
Rule X
Rule Y
Rule Z

8. Feedback loop
Automated and explainable 360° client scoring
KYC data
Transactions
history
Financial
transactions
Law
enforcement
Bank
employee
Other
client data
KYC &
KYT data
Other fin
crime
models
Alert generation Alert management Alert processing Alert closing
Discai
engine
The bulk
of alerts is
scored low
0.0
1.0 Spending most
time on most
relevant cases
SAR filing
Close case
Law enforcement
Branch alerts
Foundation model
Focused model
Network model
Anomaly model
Blending
model
Rules engine

9. For further info
contact:
Dagmar Schouterden
Relationship Management
dagmar.schouterden@discai.com
Christophe Himpens
Business Development
christophe.himpens@discai.com