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AI in the Enterprise: Past, Present & Future
Paul Huibers, Think Big Analytics
2
“
By 2020 AI will be a top five
investment priority for more
than 30% of CIOs.
—Gartner BI Summit,
February, 2017
“The Resurgence of AI
By 2019, deep learning will provide best-
in-class performance for demand, fraud,
and failure prediction. - Gartner
3
AI First
4
• Introduction to AI/DL
• AI in Industry
• Case Study: Financial Fraud
• Pilot to Production
Agenda
5
What is AI?
Artificial Intelligence is usually defined as the
science of making computers do things that
require intelligence when done by humans.
6
AI: A Brief History… …and now, Deep Learning!
• 1940’s – early concepts developed
• 1980’s – more concepts
– copy the brain, neurons, perceptron
– backpropagation for training
• 1990’s – LeCun handwriting reader
• AI winter
• 2009 – Netflix Prize $1M
• 2010 – first ImageNet competition
• 2012 - AI/deep learning comes of age
• ImageNet classification error:
– 2011: 25% using traditional methods
– 2012: 16% achieved by a ConvNet
– 2013: 11%
– 2014: 6.7%
– 2015: 3.6%
– 2016: < 3%
• 3% ~ human error rate (expert group)
• 0.3% mislabeling
• (1000 categories of images)
• What changed since the 1990s?
– 10,000X computing power, GPUs
– massive labeled datasets
7
Deep Learning Innovation in Computer Vision
Recent ImageNet Results
8
ImageNet
1.2 million images 1000 categories lots of animals…
a jungle of viewpoints,
lighting conditions, and variations of all
imaginable types.
…a jungle of viewpoints, lighting conditions, and variations of all imaginable types. – Karpathy
9
What is Deep Learning?
• A machine learning method that involves learning data representations
rather than task-specific algorithms
• Deep Neural Networks – an artificial neural network with multiple hidden
layers of “neurons” between the input and the output
• Artificial Neural Networks – computing systems inspired by biological
neural networks, involving a collection of connected units, with learned
weights and activation functions between the units
How is it achieved?
10
Deep Neural Networks
How are they different?
• Multiple hidden layers in neural network with intermediate data representations
to facilitate dimensional reduction
• Interpret non-linear relationships in the data through activation functions
• Derive patterns from data with very high dimensionality
Why do we care?
• Ability to create value with little
or no domain knowledge
required
• Ability to incorporate data from
across multiple, seemingly
unrelated sources
• Ability to tolerate very noisy data
11
Data Quantity Drives Deep Learning Performance
Andrew Ng
Amount of Labeled Data
ModelPerformance
1990’s
Small Training Sets
Traditional ML
Small NN
Medium NN
Large NN
12
Deep Learning Architectures
Convolutional Neural Network (ConvNet or CNN)
• CNN = Convolution + Pooling + ReLu + Fully Connected
• Convolution Layers are composable so can be chained
• Primary use: any problem that has a high
dimensional input (ex.: Image Labeling)
13
Specialized APIs General Purpose Frameworks
AI Framework Landscape
Vision
Language
Speech
Keras
• Pretrained (fast)
• Public
• Google/Microsoft/Amazon
• Need to be trained (expensive)
• Private
• Fully customizable
14
Touched by AI…
• Cognitive successes
• Siri, Alexa, OK Google!
– Understanding words
– Understanding context
– Language translation
• Face detection in images
• Recommender systems
• How about some practical
examples from industry?
15
• Introduction to AI/DL
• AI in Industry
• Case Study: Financial Fraud
• Pilot to Production
Agenda
16
Proven applications of Deep Learning
ANOMALY
DETECTION
Enables real-time
detection of
abnormal patterns of
data, usually time-
series events.
PREDICTIVE
MAINTENANCE
Improves preventative
measures &
performance with
greater accuracy at
the asset &
component level
RECOMMENDER
SYSTEMS
Enable more effective
search rankings based
on context, in
accordance with a
particular objective
such as purchase or
click-through
SPEECH
RECOGNITION
Enable capture of
voice to text with
higher fidelity of
speech transcription
and improved
precision of speaker
identification
COMPUTER
VISION
Enables dramatically
more accurate visual
recognition tasks
that include image
classification,
detection and
localization
DOCUMENT
AUTOMATION
Enables automation
of manual, paper-
based processes
that are human-
intensive with higher
speed, accuracy
and fidelity
17
Industry Specific Use Cases
High-Dimensional Data
Image
Video
Audio
Time Series
Text
• Many already have working solutions using non-DL Machine Learning Techniques
• Deep Learning is delivering improvement in performance on complex problems
Automotive Retail
• Navigation, Guidance, Assistance
• Predictive Maintenance
• Visual Search
• Recommendation
• Text Analytics
• Assistants
• Brand Analytics
Manufacturing & High-Tech Health Care
• Image/Audio/Video
• Reinforcement Learning – Systems
Optimization
• Plant Operations Optimization
• Image-based Analysis
• Drug Discovery
Financial Services & Insurance Cross-Industry
• Anti-Fraud
• Portfolio Optimization
• Damage Assessment
• Cyber Security
• Call Center Audio
18
Large European Logistics Provider
• Increasing use of plastic bags in
shipping
• Challenges with existing package
sorting and identification system
• Use Deep Learning Image
Analytics to improve identification
and sorting
• Tools: TensorFlow, Hadoop
• Techniques:
– Deep Learning: Convolutional
Neural Network
19
• Road objects, traffic and accident
events are manually reported or not
at all
• Automated object detection and
scene labeling system from car
camera feed to improve navigation
and traffic
• Tools: Darknet, Caffe, TensorFlow
• Techniques:
– Object Detection: Single Shot MultiBox
Detector (SSD), You Only Look Once
(YOLO)
– Scene Labeling: Convolutional Neural
Network
Large Auto Parts Manufacturer Use Case
Real-Time
Streaming
Streaming
Results
Traffic Data Service
Navigation Update
Darknet/Darkflow –
Object Detection
TensorFlow – Scene
Labeling
Cloud GPU
Based Training
TF Serving
Cloud GPU
Based Inference
Model
Updates
20
• Handwritten check volume is
decreasing however processing
checks has many fixed costs
• Handwriting recognition to reduce
manual processing and fraud
examination resulting in cost savings
• Tools: Spark, Hadoop, TensorFlow
• Techniques:
– Convolutional Neural Network
– Image Processing
Large US Multinational Bank
Check Images
To Hadoop
ImageMagick
Processing
Handwriting
Recognition
Fraud
Detection
21
• Predict failure of pistons on large
container ships to reduce unplanned
and costly maintenance
• Utilized sensor data to predict piston
wear between 70-80%
• Failures are extremely infrequent so there
is a risk of overfitting
• Tools: R, Hadoop, Spark, AWS
• Techniques:
– ROC curve
– Internet of Things data
– Methods to prevent overfitting
Large Container Shipping Company Container Ship Sensors
Predict
Failures
1 month (December)
High
Low
Abnormalcylinderbehaviour*
Lead time
Port stays
PROB1
0.0
0.2
0.4
0.6
0.8
2015-12-06 2015-12-13 2015-12-20 2015-12-27
Piston ring change
Cylinders
Piston ring(s) changed Threshold
Abnormal behavior:
Everything above
the threshold
triggers an alarm
Other cylinders
below threshold
Worn piston ring was
changed
Each point is a
combination of
selected sensor data
for a specific cylinder
22
Large European Railway
• Detecting rail switch failures
• Allows for switches to be fixed
ahead of time thus not delaying
trains
• Tools: R (Shiny and Studio),
Hadoop, Spark
• Techniques:
– Survival Analysis
– Machine Learning
– Internet of Things data
Railway Switch Sensor Data
Visualize
Failures
and Act
23
• Fraud detection across products
• Trends
– Mobile payments exploding
– Fraud evolving rapidly, increased
sophistication
• Significant improvements over
traditional rules-based techniques
• Tools: Spark, Hadoop, TensorFlow
• Techniques:
– Boosted Decision Trees
– Convolutional Neural Network
Large European Bank
24
State of AI in Industry
Successes
• Computer vision (e.g., ImageNet)
• Speech & NLP
• Simplification of general-purpose
ML (e.g., recommendation)
• Rapid advance of state of art,
growth of expertise & applications
• Major investment programs in
industry
Challenges
• Research-driven, fundamentals
change
• Mostly empirical, little theory
• Complexity in solution design
• Limited access to talent
• AI/DL still requires governed data,
and Analytics Ops integration
• Gaps in enterprise deployment
beyond lock-in clouds
25
• Introduction to AI/DL
• AI in Industry
• Case Study: Financial Fraud
• Pilot to Production
Agenda
26
Fighting Financial Fraud
with Artificial Intelligence
at Danske Bank
27
Data Driven Approach to Fight Fraud
Fast evolving fraud sophistication
Ambitions for Fraud Project
Danske Bank advanced
analytics blueprint
Data driven approach to
real time scoring of
transactions
Reduce false-positives &
Enhance fraud detection rate
ONLY ~40%
of fraud cases are detected using rules
Low Detection Rate
99.5%
of cases are not
fraud related
Many false positives
Challenges for Fraud Detection
Tens of Millions
€ lost each month
High Fraud Loss
© 2017 Teradata
28
Modeling
Challenges
© 2017 Teradata
• Class imbalance
(100,000:1 non-fraud vs. fraud)
• Assigning fraud labels from
historic data
• Fraud is ambiguous
• Not all features available in
real-time (balance, etc.)
• Most machine learning sees
transactions atomically
29
Current models can
only catch ~70% of
all fraud cases
Deep Learning Opportunity
Traditional ML models
view transactions
atomically
Often missed fraud
transactions are part
of a series
Capturing
correlation across
many features
© 2017 Teradata
30
Machine Learning Results
(Live System: 60 transactions/sec.)
Ensemble of boosted decision trees
and logistic regression.
From online validation of the model:
● 25-30% false positive reduction, with
over 35% increase in detection rate
● Opportunity to expand model with
additional features, retrain on recent
data and add additional models to
the ensemble.
● Models can be expanded to
additional channels
Rule Engine on
validation set
© 2017 Teradata
31
Three Deep Learning Architectures to Deliver Value
• Designed for spatial
correlated features,
but by transforming
transactions into a
2D image, we can
learn temporal
correlated features.
• Deeper ConvNet
allows learning more
complex & general
features.
Goal: Learn kernels from
temporal & static
features to gain insight
into the characteristics of
fraud.
• Learn temporal
information and
classify if the
sequence of
transactions
contains fraud.
• Shares knowledge
across learning time.
Goal: Learn transaction
patterns within a window.
Two solutions can be
tested: flag fraud or
predict next transaction
and define an error.
• Learn how to
generate normal
transactions,
potentially large
volumes of non-fraud
data.
• AE provide a low level
representation of the
data.
Goal: Build a model that
learns how to generate
non-fraud data. To
detect fraud, define a
reconstruction error rate
for the fraud cases
Auto-Encoders
LSTM
ConvNet
© 2017 Teradata
32
How Can We Create an Image From Bank Transactions?
t0 X_0, X_1, ... X_n
dt
t1 X_0, X_1, ... X_n
t2 X_0, X_1, ... X_n
ts X_0, X_1, ... X_n
...
Top k Features Correlation
...
X_0
X_41 X_5
X_30
X_29X_31X_10
X_37
X_3
X_1
X_42 X_40
X_32
X_15X_35X_2
X_16
X_31
X_2
X_3 X_15
X_4
X_1X_28X_40
X_31
X_49
X_n
X_26 X_9
X_40
X_35X_28X_2
X_17
X_1
...
X_0
X_41 X_5
X_30
X_29X_31X_10
X_37
X_3
X_1
X_42 X_40
X_32
X_15X_35X_2
X_16
X_31
X_2
X_3 X_15
X_4
X_1X_28X_40
X_31
X_49
X_n
X_26 X_9
X_40
X_35X_28X_2
X_17
X_1
...
...
...
...
...
X_0
X_41 X_5
X_30
X_29X_31X_10
X_37
X_3
X_1
X_42 X_40
X_32
X_15X_35X_2
X_16
X_31
X_2
X_3 X_15
X_4
X_1X_28X_40
X_31
X_49
X_n
X_26 X_9
X_40
X_35X_28X_2
X_17
X_1
N
dt
t0
t1
ts
Input Output
Raw Features
Add correlated features
in a clock-wise manner
© 2017 Teradata
Image size is:
[10 x 3, 50 x 3, 1]
Time
Strides of 3
33
2D Transaction Image Example
© 2017 Teradata
Non-fraud Transaction Image
Non-fraud
Fraud Transaction Image
X-axis: features, Y-axis: time
Fraud
Non-fraud
34
Network Architecture for CNNs
Fraud
Normal
50
30
25
15
13
8CNN
Fraud
Normal50
30
25
15
25
15
25
15
25
15
© 2017 Teradata
35
Inside the ResNet model
64 Filters
Activations
After the CNN
Residual Blocks
FraudNon-fraud
© 2017 Teradata
36
Dramatically Improve Fraud Detection over Traditional Rule Engine
© 2017 Teradata
Our Deep
Learning
Rules
Engine
False Positive Rates
Our Deep
Learning
vs
Rule
Engine at
9% FP
40% Gain
TruePositiveRates
Classic
Machine
Learning
Our Deep
Learning vs
Machine
Learning at
1% FP
44% Gain
37
Lessons Learned: Take-Aways From Danske Bank
Deep
learning
adoption
from
pictures to
financial
transactions
Enhancement
of data quality
& cluster
capabilities
with data
ingestion
Building
Analytics
Ops
capabilities
to support
business
units
Leveraging
experience
from Fraud
advanced
analytics to
deliver extra
use cases
© 2017 Teradata
38
• Introduction to AI/DL
• AI in Industry
• Case Study: Financial Fraud
• Pilot to Production
Agenda
39
Operationalization is Hard
“We evaluated some of the new methods offline
but the additional accuracy gains that we
measured did not seem to justify the engineering
effort needed to bring them into a production
environment.”
- Netflix, 2012
40
Focus First on Pilot into Production
Sets up Phase Two: Scale COE, Standardize Capabilities
Investigate
Test
Engineer
SimulateIntegration
Analyze
Data
Go Live
Handover
Validate
Activities: Define business
opportunity, understand data
available, test model
approaches, potentially
generate data
Outcome: Proposed solution
approach
Discovery/Insights
Activities: Architecture
selection, software engineering
of model and simulation
Outcome: Predicted impact of
model
Live Test
Activities: Integration into
live business process
(Champion/Challenger),
analysis, iteration
Outcome: Benefit
measurement, live learnings,
improvement
Production
Activities: Go Live, Analytics
Ops integration, Hand Over
Outcome: System scaled,
application teams and ops
trained and operating
Assessment
Insights
Production
Live Test
Cross-Functional
Teams
Cross-Functional Teams
4141
For more information, please contact:
Paul.Huibers@ThinkBigAnalytics.com
603-395-6567
Thank You StampedeCon!
stampedecon.com/ai-summit-2017-st-louis/
42
The Future… and more…
Architectural innovations: RNN, LSTM, GAN and more
Better training through new optimization, new activation functions and more
Transfer learning, pre-training and more
Theory catching up with practice (Tishby) – relevant information, bottlenecks
10,000X speed improvement would make many things possible – Moore’s Law
Unsupervised learning
Learning with few samples
AGI – artificial general intelligence
Singularity

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AI in the Enterprise: Past, Present & Future - StampedeCon AI Summit 2017

  • 1. AI in the Enterprise: Past, Present & Future Paul Huibers, Think Big Analytics
  • 2. 2 “ By 2020 AI will be a top five investment priority for more than 30% of CIOs. —Gartner BI Summit, February, 2017 “The Resurgence of AI By 2019, deep learning will provide best- in-class performance for demand, fraud, and failure prediction. - Gartner
  • 4. 4 • Introduction to AI/DL • AI in Industry • Case Study: Financial Fraud • Pilot to Production Agenda
  • 5. 5 What is AI? Artificial Intelligence is usually defined as the science of making computers do things that require intelligence when done by humans.
  • 6. 6 AI: A Brief History… …and now, Deep Learning! • 1940’s – early concepts developed • 1980’s – more concepts – copy the brain, neurons, perceptron – backpropagation for training • 1990’s – LeCun handwriting reader • AI winter • 2009 – Netflix Prize $1M • 2010 – first ImageNet competition • 2012 - AI/deep learning comes of age • ImageNet classification error: – 2011: 25% using traditional methods – 2012: 16% achieved by a ConvNet – 2013: 11% – 2014: 6.7% – 2015: 3.6% – 2016: < 3% • 3% ~ human error rate (expert group) • 0.3% mislabeling • (1000 categories of images) • What changed since the 1990s? – 10,000X computing power, GPUs – massive labeled datasets
  • 7. 7 Deep Learning Innovation in Computer Vision Recent ImageNet Results
  • 8. 8 ImageNet 1.2 million images 1000 categories lots of animals… a jungle of viewpoints, lighting conditions, and variations of all imaginable types. …a jungle of viewpoints, lighting conditions, and variations of all imaginable types. – Karpathy
  • 9. 9 What is Deep Learning? • A machine learning method that involves learning data representations rather than task-specific algorithms • Deep Neural Networks – an artificial neural network with multiple hidden layers of “neurons” between the input and the output • Artificial Neural Networks – computing systems inspired by biological neural networks, involving a collection of connected units, with learned weights and activation functions between the units How is it achieved?
  • 10. 10 Deep Neural Networks How are they different? • Multiple hidden layers in neural network with intermediate data representations to facilitate dimensional reduction • Interpret non-linear relationships in the data through activation functions • Derive patterns from data with very high dimensionality Why do we care? • Ability to create value with little or no domain knowledge required • Ability to incorporate data from across multiple, seemingly unrelated sources • Ability to tolerate very noisy data
  • 11. 11 Data Quantity Drives Deep Learning Performance Andrew Ng Amount of Labeled Data ModelPerformance 1990’s Small Training Sets Traditional ML Small NN Medium NN Large NN
  • 12. 12 Deep Learning Architectures Convolutional Neural Network (ConvNet or CNN) • CNN = Convolution + Pooling + ReLu + Fully Connected • Convolution Layers are composable so can be chained • Primary use: any problem that has a high dimensional input (ex.: Image Labeling)
  • 13. 13 Specialized APIs General Purpose Frameworks AI Framework Landscape Vision Language Speech Keras • Pretrained (fast) • Public • Google/Microsoft/Amazon • Need to be trained (expensive) • Private • Fully customizable
  • 14. 14 Touched by AI… • Cognitive successes • Siri, Alexa, OK Google! – Understanding words – Understanding context – Language translation • Face detection in images • Recommender systems • How about some practical examples from industry?
  • 15. 15 • Introduction to AI/DL • AI in Industry • Case Study: Financial Fraud • Pilot to Production Agenda
  • 16. 16 Proven applications of Deep Learning ANOMALY DETECTION Enables real-time detection of abnormal patterns of data, usually time- series events. PREDICTIVE MAINTENANCE Improves preventative measures & performance with greater accuracy at the asset & component level RECOMMENDER SYSTEMS Enable more effective search rankings based on context, in accordance with a particular objective such as purchase or click-through SPEECH RECOGNITION Enable capture of voice to text with higher fidelity of speech transcription and improved precision of speaker identification COMPUTER VISION Enables dramatically more accurate visual recognition tasks that include image classification, detection and localization DOCUMENT AUTOMATION Enables automation of manual, paper- based processes that are human- intensive with higher speed, accuracy and fidelity
  • 17. 17 Industry Specific Use Cases High-Dimensional Data Image Video Audio Time Series Text • Many already have working solutions using non-DL Machine Learning Techniques • Deep Learning is delivering improvement in performance on complex problems Automotive Retail • Navigation, Guidance, Assistance • Predictive Maintenance • Visual Search • Recommendation • Text Analytics • Assistants • Brand Analytics Manufacturing & High-Tech Health Care • Image/Audio/Video • Reinforcement Learning – Systems Optimization • Plant Operations Optimization • Image-based Analysis • Drug Discovery Financial Services & Insurance Cross-Industry • Anti-Fraud • Portfolio Optimization • Damage Assessment • Cyber Security • Call Center Audio
  • 18. 18 Large European Logistics Provider • Increasing use of plastic bags in shipping • Challenges with existing package sorting and identification system • Use Deep Learning Image Analytics to improve identification and sorting • Tools: TensorFlow, Hadoop • Techniques: – Deep Learning: Convolutional Neural Network
  • 19. 19 • Road objects, traffic and accident events are manually reported or not at all • Automated object detection and scene labeling system from car camera feed to improve navigation and traffic • Tools: Darknet, Caffe, TensorFlow • Techniques: – Object Detection: Single Shot MultiBox Detector (SSD), You Only Look Once (YOLO) – Scene Labeling: Convolutional Neural Network Large Auto Parts Manufacturer Use Case Real-Time Streaming Streaming Results Traffic Data Service Navigation Update Darknet/Darkflow – Object Detection TensorFlow – Scene Labeling Cloud GPU Based Training TF Serving Cloud GPU Based Inference Model Updates
  • 20. 20 • Handwritten check volume is decreasing however processing checks has many fixed costs • Handwriting recognition to reduce manual processing and fraud examination resulting in cost savings • Tools: Spark, Hadoop, TensorFlow • Techniques: – Convolutional Neural Network – Image Processing Large US Multinational Bank Check Images To Hadoop ImageMagick Processing Handwriting Recognition Fraud Detection
  • 21. 21 • Predict failure of pistons on large container ships to reduce unplanned and costly maintenance • Utilized sensor data to predict piston wear between 70-80% • Failures are extremely infrequent so there is a risk of overfitting • Tools: R, Hadoop, Spark, AWS • Techniques: – ROC curve – Internet of Things data – Methods to prevent overfitting Large Container Shipping Company Container Ship Sensors Predict Failures 1 month (December) High Low Abnormalcylinderbehaviour* Lead time Port stays PROB1 0.0 0.2 0.4 0.6 0.8 2015-12-06 2015-12-13 2015-12-20 2015-12-27 Piston ring change Cylinders Piston ring(s) changed Threshold Abnormal behavior: Everything above the threshold triggers an alarm Other cylinders below threshold Worn piston ring was changed Each point is a combination of selected sensor data for a specific cylinder
  • 22. 22 Large European Railway • Detecting rail switch failures • Allows for switches to be fixed ahead of time thus not delaying trains • Tools: R (Shiny and Studio), Hadoop, Spark • Techniques: – Survival Analysis – Machine Learning – Internet of Things data Railway Switch Sensor Data Visualize Failures and Act
  • 23. 23 • Fraud detection across products • Trends – Mobile payments exploding – Fraud evolving rapidly, increased sophistication • Significant improvements over traditional rules-based techniques • Tools: Spark, Hadoop, TensorFlow • Techniques: – Boosted Decision Trees – Convolutional Neural Network Large European Bank
  • 24. 24 State of AI in Industry Successes • Computer vision (e.g., ImageNet) • Speech & NLP • Simplification of general-purpose ML (e.g., recommendation) • Rapid advance of state of art, growth of expertise & applications • Major investment programs in industry Challenges • Research-driven, fundamentals change • Mostly empirical, little theory • Complexity in solution design • Limited access to talent • AI/DL still requires governed data, and Analytics Ops integration • Gaps in enterprise deployment beyond lock-in clouds
  • 25. 25 • Introduction to AI/DL • AI in Industry • Case Study: Financial Fraud • Pilot to Production Agenda
  • 26. 26 Fighting Financial Fraud with Artificial Intelligence at Danske Bank
  • 27. 27 Data Driven Approach to Fight Fraud Fast evolving fraud sophistication Ambitions for Fraud Project Danske Bank advanced analytics blueprint Data driven approach to real time scoring of transactions Reduce false-positives & Enhance fraud detection rate ONLY ~40% of fraud cases are detected using rules Low Detection Rate 99.5% of cases are not fraud related Many false positives Challenges for Fraud Detection Tens of Millions € lost each month High Fraud Loss © 2017 Teradata
  • 28. 28 Modeling Challenges © 2017 Teradata • Class imbalance (100,000:1 non-fraud vs. fraud) • Assigning fraud labels from historic data • Fraud is ambiguous • Not all features available in real-time (balance, etc.) • Most machine learning sees transactions atomically
  • 29. 29 Current models can only catch ~70% of all fraud cases Deep Learning Opportunity Traditional ML models view transactions atomically Often missed fraud transactions are part of a series Capturing correlation across many features © 2017 Teradata
  • 30. 30 Machine Learning Results (Live System: 60 transactions/sec.) Ensemble of boosted decision trees and logistic regression. From online validation of the model: ● 25-30% false positive reduction, with over 35% increase in detection rate ● Opportunity to expand model with additional features, retrain on recent data and add additional models to the ensemble. ● Models can be expanded to additional channels Rule Engine on validation set © 2017 Teradata
  • 31. 31 Three Deep Learning Architectures to Deliver Value • Designed for spatial correlated features, but by transforming transactions into a 2D image, we can learn temporal correlated features. • Deeper ConvNet allows learning more complex & general features. Goal: Learn kernels from temporal & static features to gain insight into the characteristics of fraud. • Learn temporal information and classify if the sequence of transactions contains fraud. • Shares knowledge across learning time. Goal: Learn transaction patterns within a window. Two solutions can be tested: flag fraud or predict next transaction and define an error. • Learn how to generate normal transactions, potentially large volumes of non-fraud data. • AE provide a low level representation of the data. Goal: Build a model that learns how to generate non-fraud data. To detect fraud, define a reconstruction error rate for the fraud cases Auto-Encoders LSTM ConvNet © 2017 Teradata
  • 32. 32 How Can We Create an Image From Bank Transactions? t0 X_0, X_1, ... X_n dt t1 X_0, X_1, ... X_n t2 X_0, X_1, ... X_n ts X_0, X_1, ... X_n ... Top k Features Correlation ... X_0 X_41 X_5 X_30 X_29X_31X_10 X_37 X_3 X_1 X_42 X_40 X_32 X_15X_35X_2 X_16 X_31 X_2 X_3 X_15 X_4 X_1X_28X_40 X_31 X_49 X_n X_26 X_9 X_40 X_35X_28X_2 X_17 X_1 ... X_0 X_41 X_5 X_30 X_29X_31X_10 X_37 X_3 X_1 X_42 X_40 X_32 X_15X_35X_2 X_16 X_31 X_2 X_3 X_15 X_4 X_1X_28X_40 X_31 X_49 X_n X_26 X_9 X_40 X_35X_28X_2 X_17 X_1 ... ... ... ... ... X_0 X_41 X_5 X_30 X_29X_31X_10 X_37 X_3 X_1 X_42 X_40 X_32 X_15X_35X_2 X_16 X_31 X_2 X_3 X_15 X_4 X_1X_28X_40 X_31 X_49 X_n X_26 X_9 X_40 X_35X_28X_2 X_17 X_1 N dt t0 t1 ts Input Output Raw Features Add correlated features in a clock-wise manner © 2017 Teradata Image size is: [10 x 3, 50 x 3, 1] Time Strides of 3
  • 33. 33 2D Transaction Image Example © 2017 Teradata Non-fraud Transaction Image Non-fraud Fraud Transaction Image X-axis: features, Y-axis: time Fraud Non-fraud
  • 34. 34 Network Architecture for CNNs Fraud Normal 50 30 25 15 13 8CNN Fraud Normal50 30 25 15 25 15 25 15 25 15 © 2017 Teradata
  • 35. 35 Inside the ResNet model 64 Filters Activations After the CNN Residual Blocks FraudNon-fraud © 2017 Teradata
  • 36. 36 Dramatically Improve Fraud Detection over Traditional Rule Engine © 2017 Teradata Our Deep Learning Rules Engine False Positive Rates Our Deep Learning vs Rule Engine at 9% FP 40% Gain TruePositiveRates Classic Machine Learning Our Deep Learning vs Machine Learning at 1% FP 44% Gain
  • 37. 37 Lessons Learned: Take-Aways From Danske Bank Deep learning adoption from pictures to financial transactions Enhancement of data quality & cluster capabilities with data ingestion Building Analytics Ops capabilities to support business units Leveraging experience from Fraud advanced analytics to deliver extra use cases © 2017 Teradata
  • 38. 38 • Introduction to AI/DL • AI in Industry • Case Study: Financial Fraud • Pilot to Production Agenda
  • 39. 39 Operationalization is Hard “We evaluated some of the new methods offline but the additional accuracy gains that we measured did not seem to justify the engineering effort needed to bring them into a production environment.” - Netflix, 2012
  • 40. 40 Focus First on Pilot into Production Sets up Phase Two: Scale COE, Standardize Capabilities Investigate Test Engineer SimulateIntegration Analyze Data Go Live Handover Validate Activities: Define business opportunity, understand data available, test model approaches, potentially generate data Outcome: Proposed solution approach Discovery/Insights Activities: Architecture selection, software engineering of model and simulation Outcome: Predicted impact of model Live Test Activities: Integration into live business process (Champion/Challenger), analysis, iteration Outcome: Benefit measurement, live learnings, improvement Production Activities: Go Live, Analytics Ops integration, Hand Over Outcome: System scaled, application teams and ops trained and operating Assessment Insights Production Live Test Cross-Functional Teams Cross-Functional Teams
  • 41. 4141 For more information, please contact: Paul.Huibers@ThinkBigAnalytics.com 603-395-6567 Thank You StampedeCon! stampedecon.com/ai-summit-2017-st-louis/
  • 42. 42 The Future… and more… Architectural innovations: RNN, LSTM, GAN and more Better training through new optimization, new activation functions and more Transfer learning, pre-training and more Theory catching up with practice (Tishby) – relevant information, bottlenecks 10,000X speed improvement would make many things possible – Moore’s Law Unsupervised learning Learning with few samples AGI – artificial general intelligence Singularity