The document outlines the use of AI and machine learning in analyzing transaction data to identify suspicious activities through various modeling approaches, including rule-based, feature-based, and pure data-driven models. It highlights the advantages of using AI, such as quicker classification and lower false positive rates, compared to traditional rule-based systems which rely on manual analysis and can be inconsistent. Additionally, it emphasizes the importance of feature engineering and the algorithm's ability to learn from analyst feedback for improved accuracy.

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Machine Intelligence

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Machine Intelligence
Rule-based
Model
Feature-based
Model
Pure Data Driven
Model

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Machine Intelligence
Alerts from
rule-based
system
Analytical Inputs:
1. LexisNexis
2. Accounts Database
3. Transaction Database
4. Card Database
Alert Decision:
Suspicious
Alert Decision:
Not Suspicious

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Machine Intelligence
1. Manual analysis by an investigator
2. Dispersed datasets
3. Subjective and inconsistent
4. Time consuming
5. High false positive rate

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Machine Intelligence
Rule-based
Model
Feature-based
Model
Pure Data Driven
Model

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Machine 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.

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Machine Intelligence
1. Transactions - or payments databases
2. Account Information - customer focused database
3. Alerts - AML alerts database.

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average balance of last 7 days
7 Days

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1. Designed Features Highlight Transactional Behaviour
2. Features Continuously Track Transactional Behaviour of an account
3. Rules Variables can only Identify Threshold Changes

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Machine Intelligence
Alerts from rule-based
system
Alert Decision:
Not Suspicious
H2O Machine
Learning Algorithm
Alert Decision:
Suspicious
Analytical Inputs:
1. Transaction Data
2. Account Data
3. Card Data etc.

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Machine 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.

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O.ai
Machine Intelligence
Alerts from rule-based
system
Alert Decision:
Not Suspicious
H2O Machine Learning
Algorithm
Alert Decision:
Suspicious
Analytical Inputs:
1. Transaction Data
2. Account Data
3. Card Data etc.
AML Analyst
Alert decision sampling by the analyst
Algorithm tuning by analyst after alert
decision sampling

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Machine 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

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Machine Intelligence
Rule-based
Model
Feature-based
Model
Pure Data Driven
Model

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Machine Intelligence
Not a suspicious
transaction
H2O Machine
Learning - Deep
Learning Algorithm
Suspicious
Transaction
Transaction Data
Alert Data
Card Data
Account Data

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Machine 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

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O.ai
Machine Intelligence