Ashrith Barthur is a Security Scientist at H2O currently working on algorithms that detect anomalous behaviour in user activities, network traffic, attacks, financial fraud and global money movement. He has a PhD from Purdue University in the field of information security, specialized in Anomalous behaviour in DNS protocol. Abstract summary ML(Machine Learning) in AML (Anti Money Laundering): AML or anti money laundering has been a consistent bane of multiple governments and banks. A strong influences by countries to curb illegal money movement has resulted in a significant yet extremely small aspect of money laundering being identified – a success rate of about 2% average. A more global foot print the bank has the lesser is the accuracy of money laundering investigations. In its current mechanism, investigators analyse each money laundering alert and provide their subjective opinion towards a case. Unfortunately this takes time, and still has a return rate of about 2% at average and 10% at the highest. What we design are AI algorithms that work upon features that track monetary behaviour of every account. These features are essentially time-bound making them a fundamental aspect of algorithm design. The algorithms have a capability to improve the identification close to 70%, and we a certain exclusive features that are a function of time and improve much further.

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Machine Intelligence
Anti-Money Laundering Solution

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What is Money Laundering?
1. “Washing” ill-gotten money with legitimate money to hide the
source
2. Illegal drug sales, human trafficking, online gambling, insider
trading, etc.

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What is the problem with Money
Laundering?
1. Illegal trade and markets grow
2. Negatively impacts the society
3. Governments lose out on taxes
4. In some countries, alternative centers of power come into
existence.

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Solutions do exist. Right?
1. Yes.
2. But are limited
3. Limited due to current rule-based, stateless approach

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So what do we do?
1. Up the game
2. Make the detecting systems smarter

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One Solution is to use Machine Learning
- Artificial Intelligence

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Machine Intelligence
AML Solution Evolution
Rule-based
Model
Feature-based
Model
Pure Data Driven
Model

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Machine Intelligence
Rule-based Model
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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Rule-based Model: Limitations
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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O.ai
Machine Intelligence
AML Solution Evolution
Rule-based
Model
Feature-based
Model
Pure Data Driven
Model

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Machine Intelligence
Features - (Used in Feature-based Model)
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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Source of Features
1. Transactions - or payments databases
2. Account Information - customer focused database
3. Alerts - AML alerts database.

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Features - Example
average balance of last 7 days
7 Days

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Features: Advantages
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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Feature-based Model
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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Feature-based Model: Advantages
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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Feature-based Model Workflow
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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O.ai
Machine Intelligence
AML Solution Evolution
Rule-based
Model
Feature-based
Model
Pure Data Driven
Model

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Machine Intelligence
Pure Data-driven Model
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
Pure Data-driven Model: Advantages
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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Thank You
Questions?