Anti-Money Laundering Solution
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H 2 O.ai Machine Intelligence
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Transcript of Anti-Money Laundering Solution
H2O.aiMachine Intelligence
H2O.aiMachine Intelligence
Rule-based Model
Feature-based Model
Pure Data Driven Model
H2O.aiMachine Intelligence
Alerts from rule-based system
Analytical Inputs:1. LexisNexis2. Accounts Database3. Transaction Database4. Card Database
Alert Decision: Suspicious
Alert Decision: Not Suspicious
H2O.aiMachine Intelligence
1. Manual analysis by an investigator
2. Dispersed datasets
3. Subjective and inconsistent
4. Time consuming
5. High false positive rate
H2O.aiMachine Intelligence
Rule-based Model
Feature-based Model
Pure Data Driven Model
H2O.aiMachine Intelligence
1. Features are meta data (Extracted from the data)
2. They help algorithms capture information from the data.
3. Feature engineering is a form of language translation: Between raw data
and the algorithm.
H2O.aiMachine Intelligence
1. Transactions - or payments databases
2. Account Information - customer focused database
3. Alerts - AML alerts database.
H2O.aiMachine Intelligence
average balance of last 7 days
7 Days
H2O.aiMachine Intelligence
1. Designed Features Highlight Transactional Behaviour
2. Features Continuously Track Transactional Behaviour of an account
3. Rules Variables can only Identify Threshold Changes
H2O.aiMachine Intelligence
Alerts from rule-based system
Alert Decision: Not Suspicious
H2O Machine Learning Algorithm
Alert Decision: Suspicious
Analytical Inputs:1. Transaction Data2. Account Data3. Card Data etc.
H2O.aiMachine Intelligence
1. Uses AI - artificial intelligence
2. AI with features uses a consistent and objective approach
3. Quick classification
4. Low false positive rate - tweaked based on risk appetite.
H2O.aiMachine Intelligence
Alerts from rule-based system
Alert Decision: Not Suspicious
H2O Machine Learning Algorithm
Alert Decision: Suspicious
Analytical Inputs:1. Transaction Data2. Account Data3. Card Data etc.
AML Analyst
Alert decision sampling by the analyst
Algorithm tuning by analyst after alert decision sampling
H2O.aiMachine Intelligence
1. AI model will learn and improve from the analyst’s feedback
2. The analyst has one single interface
3. Unified interface for dispersed datasets
H2O.aiMachine Intelligence
Rule-based Model
Feature-based Model
Pure Data Driven Model
H2O.aiMachine Intelligence
Not a suspicious transaction
H2O Machine Learning - Deep
Learning Algorithm
Suspicious Transaction
Transaction Data
Alert Data
Card Data
Account Data
H2O.aiMachine Intelligence
1. The algorithm understands malicious behaviour through data
2. Algorithm is smart to work without features - metadata
3. Does not need alerts for training
4. Helps in identifying any kind of anomalous behaviour
5. Deeper insights about customer
H2O.aiMachine Intelligence
