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David Talby
@davidtalby
SVP Engineering, Atigeo
HUNTING CRIMINALS WITH HYBRID ANALYTICS,
SEMI-SUPERVISED LEARNING, AND AGENT FEEDBACK
Claudiu Branzan
@melcutz
Principal Data Science Lead, Atigeo
2
3
ITโ€™S NOT (JUST) ABOUT MONEY
3
3
4
WHAT WEโ€™RE UP AGAINST
4
4
50+Schemes
(and counting)
99.9999%โ€˜Goodโ€™ messages
6+Months
per case
Needle in a haystack
Hybrid analytics
No training data
Semi-supervised learning
Adversarial learning
Online feedback
5
WHY HYBRID ANALYTICS?
5
5
Ignore
more rules
Unusual
timing of
events
Unusual
personal
network
Teamwork
& scale
Think & talk
differently
6
(BITS OF) THE TOOLBOX
6
6
Rule
Inference
Time
Series
AnalysisLink
Analysis
Ensemble
Learning
Natural
Language
7
CAN WE SEE SOME CODE PLEASE?
7 7
Freely available IPython notebooks
Open source libraries & open data
Jump-start via AWS Marketplace
8
STREAM PROCESSING
8
8
Kafka
Email Stream
Account transactions
Stream
Email NLP
Features
People graph
Transactions time series
9
SAMPLE EMAIL PATTERNS
1 0
SAMPLE NATURAL LANGUAGE ANNOTATORS
Understand vocabulary
โ€“ Jargon
โ€“ Code words
โ€“ Multi-lingual
Understand grammar
โ€“ Who are we talking about?
โ€“ Past, present or future?
โ€“ Compound sentences
Understand context
โ€“ Email: Re:, Fwd:, attachments
โ€“ SMS & IM have their own grammar
1 1
SAMPLE GRAPH FEATURES
Standard algorithms like KMeans donโ€™t work on โ€œhaystacksโ€
1 2
SAMPLE GRAPH FEATURES
Bregman Bubble Clustering
1 3
USER ANALYSIS ITERATION
Email NLP
Features
User graph
Transactions
time series
Graph Features
Time Series
Features
NLP Features
Agent Feedback
Train/TestClassifier
1 4
Really
โ€ข Makes the world a better place โ€ข Needle in a very large haystack
โ€“ Actually needs a petabyte-scale platform
โ€ข Multi-modal: no single trick works
โ€“ Hybrid analytics
โ€ข No labeled data
โ€“ Semi-supervised learning
โ€“ Cold start problem
โ€ข Sparse & high-dimensional
โ€“ Graph based features & change over time
โ€ข Adversarial
โ€“ Feedback & online learning
Technically
SUMMARY: WHY HUNTING CRIMINALS IS COOL
14
14
1 5
15
@Atigeo
@melcutz
@davidtalby
ยฉ 2015 Atigeo, Corporation. All rights reserved. Atigeo and the xPatterns logo are trademarks of Atigeo. The information herein is for informational purposes only and represents the current view of Atigeo as of the date of this presentation. Because Atigeo
must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Atigeo, and Atigeo cannot guarantee the accuracy of any information provided after the date of this presentation. ATIGEO MAKES NO WARRANTIES,
EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.
APPENDIX
In case the live demo gets cold feet on stage
1 7
1 8
1 9
2 0
2 1
2 2
2 3
2 4
2 5
2 6

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Hunting criminals with hybrid analytics -- October 2015

  • 1. David Talby @davidtalby SVP Engineering, Atigeo HUNTING CRIMINALS WITH HYBRID ANALYTICS, SEMI-SUPERVISED LEARNING, AND AGENT FEEDBACK Claudiu Branzan @melcutz Principal Data Science Lead, Atigeo
  • 2. 2
  • 3. 3 ITโ€™S NOT (JUST) ABOUT MONEY 3 3
  • 4. 4 WHAT WEโ€™RE UP AGAINST 4 4 50+Schemes (and counting) 99.9999%โ€˜Goodโ€™ messages 6+Months per case Needle in a haystack Hybrid analytics No training data Semi-supervised learning Adversarial learning Online feedback
  • 5. 5 WHY HYBRID ANALYTICS? 5 5 Ignore more rules Unusual timing of events Unusual personal network Teamwork & scale Think & talk differently
  • 6. 6 (BITS OF) THE TOOLBOX 6 6 Rule Inference Time Series AnalysisLink Analysis Ensemble Learning Natural Language
  • 7. 7 CAN WE SEE SOME CODE PLEASE? 7 7 Freely available IPython notebooks Open source libraries & open data Jump-start via AWS Marketplace
  • 8. 8 STREAM PROCESSING 8 8 Kafka Email Stream Account transactions Stream Email NLP Features People graph Transactions time series
  • 10. 1 0 SAMPLE NATURAL LANGUAGE ANNOTATORS Understand vocabulary โ€“ Jargon โ€“ Code words โ€“ Multi-lingual Understand grammar โ€“ Who are we talking about? โ€“ Past, present or future? โ€“ Compound sentences Understand context โ€“ Email: Re:, Fwd:, attachments โ€“ SMS & IM have their own grammar
  • 11. 1 1 SAMPLE GRAPH FEATURES Standard algorithms like KMeans donโ€™t work on โ€œhaystacksโ€
  • 12. 1 2 SAMPLE GRAPH FEATURES Bregman Bubble Clustering
  • 13. 1 3 USER ANALYSIS ITERATION Email NLP Features User graph Transactions time series Graph Features Time Series Features NLP Features Agent Feedback Train/TestClassifier
  • 14. 1 4 Really โ€ข Makes the world a better place โ€ข Needle in a very large haystack โ€“ Actually needs a petabyte-scale platform โ€ข Multi-modal: no single trick works โ€“ Hybrid analytics โ€ข No labeled data โ€“ Semi-supervised learning โ€“ Cold start problem โ€ข Sparse & high-dimensional โ€“ Graph based features & change over time โ€ข Adversarial โ€“ Feedback & online learning Technically SUMMARY: WHY HUNTING CRIMINALS IS COOL 14 14
  • 16. ยฉ 2015 Atigeo, Corporation. All rights reserved. Atigeo and the xPatterns logo are trademarks of Atigeo. The information herein is for informational purposes only and represents the current view of Atigeo as of the date of this presentation. Because Atigeo must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Atigeo, and Atigeo cannot guarantee the accuracy of any information provided after the date of this presentation. ATIGEO MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.
  • 17. APPENDIX In case the live demo gets cold feet on stage 1 7
  • 18. 1 8
  • 19. 1 9
  • 20. 2 0
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  • 26. 2 6