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Online Predictive Modeling of
Fraud Schemes from Multiple
Live Streams
David Talby Claudiu Branzan
CTO, Atigeo Principal Lead, Atigeo
2
What we’re up against
3
50+
Schemes
(and counting)
99.9999%‘Good’ messages
6+Months
per case
Needle in a haystack
Hybrid analytics
No training data
Semi-supervised learning
Adversarial learning
Online feedback
Why hybrid analytics?
4
Ignore
more
rules
Unusual
timing of
events
Unusual
personal
network
Teamwork
& scale
Think &
talk
differently
(bits of) the toolbox
5
Rule
Inference
Time
Series
AnalysisLink
Analysis
Ensemble
Learning
Natural
Language
Can we see some code please?
6
Freely available IPython notebooks
Open source libraries & open data
Jump-start via AWS Marketplace
Stream processing
7
Kafka
Email Stream
Account transactions
Stream
Email NLP
Features
People graph
Transactions time series
Sample email patterns
Sample natural language annotators
Understand	vocabulary
– Jargon
– Code	words
– Multi-lingual
Understand	grammar
– Who	are	we	talking	about?
– Past,	present	or	future?
– Compound	sentences
Understand	context
– Email:	Re:,	Fwd:,	attachments
– SMS	&	IM	have	their	own	grammar
Sample email patterns
K-Means failing on “haystacks” Bregman Bubble Clustering
User analysis iteration
Email NLP
Features
User graph
Transactions
time series
Graph Features
Time Series
Features
NLP Features
Agent Feedback
Train/TestClassifier
Really
• Makes the world a better place • Needle in a very large haystack
– Actually needs a petabyte-scale platform
• Multi-modal: no single trick works
– Hybrid analytics
• No labeled data
– Semi-supervised learning
– Cold start problem
• Sparse & high-dimensional
– Graph based features & change over time
• Adversarial
– Feedback & online learning
Technically
Summary: why hunting criminals is cool
12
12
THANK YOU!
Get the notebooks: github.com/Atigeo/Atigeo/hunting_criminals_demo
Try it yourself: “xPatterns Connect” on AWS Marketplace
Ask us about it: @davidtalby , @melcutz
appendix
Appendix
In case the live demo gets cold feet on stage
14
Online Predictive Modeling of Fraud Schemes from Mulitple Live Streams by Claudiu Branzan and David Talby
Online Predictive Modeling of Fraud Schemes from Mulitple Live Streams by Claudiu Branzan and David Talby
Online Predictive Modeling of Fraud Schemes from Mulitple Live Streams by Claudiu Branzan and David Talby
Online Predictive Modeling of Fraud Schemes from Mulitple Live Streams by Claudiu Branzan and David Talby
Online Predictive Modeling of Fraud Schemes from Mulitple Live Streams by Claudiu Branzan and David Talby
Online Predictive Modeling of Fraud Schemes from Mulitple Live Streams by Claudiu Branzan and David Talby
Online Predictive Modeling of Fraud Schemes from Mulitple Live Streams by Claudiu Branzan and David Talby
Online Predictive Modeling of Fraud Schemes from Mulitple Live Streams by Claudiu Branzan and David Talby
Online Predictive Modeling of Fraud Schemes from Mulitple Live Streams by Claudiu Branzan and David Talby

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Online Predictive Modeling of Fraud Schemes from Mulitple Live Streams by Claudiu Branzan and David Talby

  • 1. Online Predictive Modeling of Fraud Schemes from Multiple Live Streams David Talby Claudiu Branzan CTO, Atigeo Principal Lead, Atigeo
  • 2. 2
  • 3. What we’re up against 3 50+ Schemes (and counting) 99.9999%‘Good’ messages 6+Months per case Needle in a haystack Hybrid analytics No training data Semi-supervised learning Adversarial learning Online feedback
  • 4. Why hybrid analytics? 4 Ignore more rules Unusual timing of events Unusual personal network Teamwork & scale Think & talk differently
  • 5. (bits of) the toolbox 5 Rule Inference Time Series AnalysisLink Analysis Ensemble Learning Natural Language
  • 6. Can we see some code please? 6 Freely available IPython notebooks Open source libraries & open data Jump-start via AWS Marketplace
  • 7. Stream processing 7 Kafka Email Stream Account transactions Stream Email NLP Features People graph Transactions time series
  • 9. Sample natural language annotators Understand vocabulary – Jargon – Code words – Multi-lingual Understand grammar – Who are we talking about? – Past, present or future? – Compound sentences Understand context – Email: Re:, Fwd:, attachments – SMS & IM have their own grammar
  • 10. Sample email patterns K-Means failing on “haystacks” Bregman Bubble Clustering
  • 11. User analysis iteration Email NLP Features User graph Transactions time series Graph Features Time Series Features NLP Features Agent Feedback Train/TestClassifier
  • 12. Really • Makes the world a better place • Needle in a very large haystack – Actually needs a petabyte-scale platform • Multi-modal: no single trick works – Hybrid analytics • No labeled data – Semi-supervised learning – Cold start problem • Sparse & high-dimensional – Graph based features & change over time • Adversarial – Feedback & online learning Technically Summary: why hunting criminals is cool 12 12
  • 13. THANK YOU! Get the notebooks: github.com/Atigeo/Atigeo/hunting_criminals_demo Try it yourself: “xPatterns Connect” on AWS Marketplace Ask us about it: @davidtalby , @melcutz
  • 14. appendix Appendix In case the live demo gets cold feet on stage 14