anti money laundering solutions

H2O.aiMachine Intelligence

Ashrith Barthur, PhDSecurity Scientist at H2O.ai

Anti Money Laundering Solutions


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.


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.


Solutions do exist. Right?

1. Yes.

2. But are limited

3. Limited due to a rule-based, stateless approach


So what do we do?

1. Up the game

2. Make the detecting systems smarter


One Solution is to use Machine Learning - Artificial Intelligence


Why Machine Learning/Artificial Intelligence?

1. Machines (programs) learn from data.

2. More probability of the occurrence of an event = more

probability of the machine learning and being able to

predict the event.


More Data = Better AI. Right?

1. At a fundamental level, “Yes”

2. But the machines need great features (variables) to learn

the inherent behaviour


Let us see that with an example use-case.


Example use-case

Quick or excessive cash withdrawals using ATMs.


ATM Cash Withdrawals

1. Used to laundering money quickly

2. Mostly done through unsuspecting account holder -

“Money Mules”

3. Money movement done by promising a small percentage

of the deposited money

4. Sometimes with fake and/or new accounts


ATM Cash Withdrawals

1. Difficult to identify using rule-based systems

2. Rule based systems capture only trips

3. Below threshold laundering is missed by rule-based

systems

4. Rule based systems are fairly memoryless (stateless)


Detecting Cash Withdrawals with ML/AI

1. Machine Learning/AI system do not depend on trips and

thresholds

2. They learn from numerous features that are designed by

engineers

3. The learn from feedback - money laundering experts, what

is money laundering and what is not.


Feature examples

1. Age of the account

2. ATM withdrawal / All withdrawal per week - tracked for 6

months (rolling window)

3. Average volume of withdrawal per week - tracked for 6

months (rolling window)


Age of the Account

1. Investigators / AML Experts are routinely wary of new

accounts

2. New accounts tend to launder money more than old

accounts

3. Our tests corroborate that.


ATM Withdrawal/ All Withdrawal

1. Feature tracked for 6 months

2. The idea is that historically tracking a change or no change

helps the AI algorithm learn the behaviour

3. Actual money laundering will show spikes


Average volume of withdrawal

1. Another feature tracked for 6 months

2. Changes in the volume of withdrawal is indicator of a

possible money laundering behaviour


Aggregate of Features

1. No Individual feature can identify a money laundering

situation positively

2. The composition of all features yields a probability score

for a given event.

3. A high enough event probability score can confirm money

laundering.


Thank YouQuestions

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