Ashirth Barthur, Security Scientist, H2O, at MLconf Seattle 2017

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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.

Technology

H2
O.ai
Machine Intelligence
Anti-Money Laundering Solution

H2
O.ai
Machine Intelligence
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.

H2
O.ai
Machine Intelligence
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.

H2
O.ai
Machine Intelligence
Solutions do exist. Right?
1. Yes.
2. But are limited
3. Limited due to current rule-based, stateless approach

H2
O.ai
Machine Intelligence
So what do we do?
1. Up the game
2. Make the detecting systems smarter

H2
O.ai
Machine Intelligence
One Solution is to use Machine Learning
- Artificial Intelligence

H2
O.ai
Machine Intelligence
AML Solution Evolution
Rule-based
Model
Feature-based
Model
Pure Data Driven
Model

H2
O.ai
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

H2
O.ai
Machine Intelligence
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

H2
O.ai
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.

H2
O.ai
Machine Intelligence
Source of Features
1. Transactions - or payments databases
2. Account Information - customer focused database
3. Alerts - AML alerts database.

H2
O.ai
Machine Intelligence
Features - Example
average balance of last 7 days
7 Days

H2
O.ai
Machine Intelligence
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

H2
O.ai
Machine Intelligence
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.

H2
O.ai
Machine Intelligence
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.

H2
O.ai
Machine Intelligence
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

H2
O.ai
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

H2
O.ai
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

H2
O.ai
Machine Intelligence
Thank You
Questions?

What's hot

With the increasingly connected world revolving around the revolution of internet and new technologies like mobiles, smartphones, and tablets, and with the wide usage of wireless technologies, the information security risks have increased. Both individuals and organizations are under regular attacks for commercial or non-commercial gains. The objectives of such attacks may be to take revenge, malign the reputation of a competitor organization, understand the strategies and sensitive information about the competitor, simply have fun of exploiting the vulnerabilities. Hence, the need to protect information assets and ensure information security receives adequate attention. In this session, I will discuss how AI and Machine Learning can be applied in detecting, predicting and preventing cyber security/information security vulnerabilities and what are the benefits of using Machine Learning and AI. We also touch upon some of the tools available to perform the same.

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Our architecture migration from a monolithic to a distributed system at dmTECH entailed a lot of challenges. One of the main topics was observability. An increasing distribution of the systems demands for centralized logging to correlate events and analyze business processes. Dependencies of services can be tracked by request based tracing. Metrics and monitoring are prerequisites to capture the reliability of applications and to take action on anomalies. Establishing a devops culture the product team is forced to face the challenges as every member of the team has holistic responsibility of their product. In our talk we present some tools to handle the aforementioned challenges, especially those which fit best for our Spring-Boot-Stack.

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Every single security company is talking about how they are using machine learning—as a security company you have to claim artificial intelligence to be even part of the conversation. However, this approach can be dangerous when we blindly rely on algorithms to do the right thing. Rather than building systems with actual security knowledge, companies are using algorithms that nobody understands and, in turn, discovering wrong insights. In this session, we will discuss: • Limitations of machine learning and issues of explainability • Where deep learning should never be applied • Examples of how the blind application of algorithms can lead to wrong results

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Fraud detection is a topic which is applicable to many industries including banking and financial sectors, insurances, government agencies, and low enforcement and more.Through the use of sophisticeted use of data mining tools, millions of transactions can be searched to spot patterns and detect fraudulent transactions. Its a process of identifying fraudulent transaction. This technique used to recognize fraudulent creddit card transactions so that customers are not charged for items that they did not purchases

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1. H2 O.ai Machine Intelligence Anti-Money Laundering Solution

2. H2 O.ai Machine Intelligence 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.

3. H2 O.ai Machine Intelligence 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.

4. H2 O.ai Machine Intelligence Solutions do exist. Right? 1. Yes. 2. But are limited 3. Limited due to current rule-based, stateless approach

5. H2 O.ai Machine Intelligence So what do we do? 1. Up the game 2. Make the detecting systems smarter

6. H2 O.ai Machine Intelligence One Solution is to use Machine Learning - Artificial Intelligence

7. H2 O.ai Machine Intelligence AML Solution Evolution Rule-based Model Feature-based Model Pure Data Driven Model

8. H2 O.ai 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

9. H2 O.ai Machine Intelligence 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

10. H2 O.ai Machine Intelligence AML Solution Evolution Rule-based Model Feature-based Model Pure Data Driven Model

11. H2 O.ai 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.

12. H2 O.ai Machine Intelligence Source of Features 1. Transactions - or payments databases 2. Account Information - customer focused database 3. Alerts - AML alerts database.

13. H2 O.ai Machine Intelligence Features - Example average balance of last 7 days 7 Days

14. H2 O.ai Machine Intelligence 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

15. H2 O.ai Machine Intelligence 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.

16. H2 O.ai Machine Intelligence 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.

17. H2 O.ai Machine Intelligence 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

18. H2 O.ai Machine Intelligence AML Solution Evolution Rule-based Model Feature-based Model Pure Data Driven Model

19. H2 O.ai 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

20. H2 O.ai 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

21. H2 O.ai Machine Intelligence Thank You Questions?

Ashirth Barthur, Security Scientist, H2O, at MLconf Seattle 2017

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Ashirth Barthur, Security Scientist, H2O, at MLconf Seattle 2017