Real-World Data Science (Fraud Detection, Customer Churn & Predicti...

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Real-World Data Science
Fraud Detection, Customer Churn & Predictive Maintenance
Dr. Shirin Glander

About me

Fraud Detection

What is fraud and why is it interesting for Data
Science?
“The crime of getting money by deceiving people” (Cambridge Dict...

What is fraud and why is it interesting for Data
Science?
“The crime of getting money by deceiving people” (Cambridge Dict...

Leveraging Big Data and Machine Learning in
modern Fraud Detection
Collect information about customers & transactions
Lear...

Leveraging Big Data and Machine Learning in
modern Fraud Detection
Collect information about customers & transactions
Lear...

Unsupervised
Data without labels or class
information
Aim
Finding similar instances or
patterns
Ranking instances
Finding ...

Unsupervised
Data without labels or class
information
Aim
Finding similar instances or
patterns
Ranking instances
Finding ...

What can Machine Learning do?

Exploring a fraud example dataset
Synthetic financial dataset for fraud detection from Kaggle1
6,362,620 transactions over...

Exploring a fraud example dataset
Synthetic financial dataset for fraud detection from Kaggle1
6,362,620 transactions over...

We can already learn a lot just by looking at the
data

Fraud Detection with unsupervised
dimensionality reduction
Identify hidden patterns

Principal Component
Analysis (PCA)
Linear relationships between
features
Reflects the majority of
variation in our data
Sh...

t-distributed Stochastic
Neighbor Embedding (t-
SNE)
Complex relationships between
features
Shows also similarity by
clust...

Fraud Detection with supervised Machine
Learning
Train a classification model on the class label e.g. with Gradient Boosti...

Why Fraud Detection is particularly tricky
Fraud is rare => highly unbalanced class distribution
This makes using normal a...

Why Fraud Detection is particularly tricky
Fraud is rare => highly unbalanced class distribution
This makes using normal a...

Fraud Detection with unsupervised Anomaly
Detection
If we assume that fraud is sufficiently different from regular transac...

Fraud Detection with unsupervised Anomaly
Detection
If we assume that fraud is sufficiently different from regular transac...

What are Neural Nets
A particular type of algorithm for Machine Learning
Used in supervised and unsupervised learning

What are Neural Nets
A particular type of algorithm for Machine Learning
Used in supervised and unsupervised learning
Mode...

How Neural Nets learn classification tasks
Input: instances + features (data)
Output: class label
Neural Nets learn by opt...

How Neural Nets learn classification tasks
Input: instances + features (data)
Output: class label
Neural Nets learn by opt...

The most important things to know about Deep
Learning

Deep Learning vs. Machine Learning

Deep Learning autoencoders
Anomaly detection
Semi-supervised: input = output
Minimizing reconstruction error or loss
Outli...

Deep Learning autoencoders
Anomaly detection
Semi-supervised: input = output
Minimizing reconstruction error or loss
Outli...

Pre-training supervised models with
autoencoders
Using the weights from the autoencoder model for initialising the trainin...

A few words on performance evaluation
Accuracy is easy to understand but not necessarily correct!

A few words on performance evaluation
Accuracy is easy to understand but not necessarily correct!
Performance is evaluated...

A few words on performance evaluation
Accuracy is easy to understand but not necessarily correct!
Performance is evaluated...

Precision
How many predicted fraud cases in
the test data were correctly
classified?
So, what's a better way to measure
pe...

Precision
How many predicted fraud cases in
the test data were correctly
classified?
Sensitivity or recall
How many fraud ...

Precision
How many predicted fraud cases in
the test data were correctly
classified?
Sensitivity or recall
How many fraud ...

A Shiny demo
https://shiring.shinyapps.io/fraud_example_dashboard/

Customer Churn

What is Customer Churn?
Loss of revenue due to customers cancelling contract, not returning, etc.
Affects e-commerce, tele...

What is Customer Churn?
Loss of revenue due to customers cancelling contract, not returning, etc.
Affects e-commerce, tele...

What is Customer Churn?
Loss of revenue due to customers cancelling contract, not returning, etc.
Affects e-commerce, tele...

How can Machine Learning help prevent churn?
Learn from historical data about customer behavior

How can Machine Learning help prevent churn?
Learn from historical data about customer behavior
Generate and use predictiv...

How can Machine Learning help prevent churn?
Learn from historical data about customer behavior
Generate and use predictiv...

How can Machine Learning help prevent churn?
Learn from historical data about customer behavior
Generate and use predictiv...

Predicting customer churn
Traditional statistical methods, like logistic regression
Machine Learning approaches, e.g. with...

Predicting customer churn
Traditional statistical methods, like logistic regression
Machine Learning approaches, e.g. with...

A customer churn case-study1
Telco Customer Churn dataset from IBM Watson2
[1] https://shirinsplayground.netlify.com/2018/...

A customer churn case-study1
Telco Customer Churn dataset from IBM Watson2
7,043 customers
19 feature
1 response variable:...

A customer churn case-study1
Telco Customer Churn dataset from IBM Watson2
7,043 customers
19 feature
1 response variable:...

Modeling customer churn with Keras &
TensorFlow
Open-Source Deep Learning API
Train and prototype neural nets fast & easy
...

Training with Keras & TensorFlow
1. Initialising sequential model
2. Adding layers
3. Compiling model
4. Fitting model

Training with Keras & TensorFlow
1. Initialising sequential model
2. Adding layers
3. Compiling model
4. Fitting model

H2O.ai
Open-Source Machine Learning
API
Optimized for distributed and
parallel training
Stacked Ensembles mit H2O

H2O.ai
Open-Source Machine Learning
API
Optimized for distributed and
parallel training
Stacked Ensembles mit H2O
Stacked ...

Precision
How many predicted fraud cases in
the test data were correctly
classified?
Sensitivity or recall
How many fraud ...

Evaluation of customer churn models
Area under the ROC curve
Receiver Operator Characteristic
Proportion false positives v...

Thresholds influence prediction accuracy
In binary classification: default = 0.5
Best threshold depends on task and costs ...

Thresholds influence prediction accuracy
In binary classification: default = 0.5
Best threshold depends on task and costs ...

Thresholds influence prediction accuracy

Confusion matrices
Table of wrong and correct predictions
actual predict_0 predict_1 error rate
0 667 107 0.14 107/774
1 8...

Confusion matrices
Table of wrong and correct predictions
actual predict_0 predict_1 error rate
0 667 107 0.14 107/774
1 8...

Cost/revenue calculation
Let’s assume that

Cost/revenue calculation
Let’s assume that
1. A marketing campaign + employee time will cost the company 1000€ per year
f...

Cost/revenue calculation
Let’s assume that
1. A marketing campaign + employee time will cost the company 1000€ per year
f...

Cost/revenue calculation
Let’s assume that
1. A marketing campaign + employee time will cost the company 1000€ per year
f...

Cost/revenue calculation
Let’s assume that
1. A marketing campaign + employee time will cost the company 1000€ per year
f...

Cost/revenue calculation
Let’s assume that
1. A marketing campaign + employee time will cost the company 1000€ per year
f...

Cost/revenue calculation
This would mean that compared to no intervention we would have

Cost/revenue calculation
This would mean that compared to no intervention we would have
prop_p0_true == customers who were...

Cost/revenue calculation
This would mean that compared to no intervention we would have
prop_p0_true == customers who were...

Cost/revenue calculation
This would mean that compared to no intervention we would have
prop_p0_true == customers who were...

Cost/revenue calculation
This would mean that compared to no intervention we would have
prop_p0_true == customers who were...

Cost/revenue calculation
How much revenue can we gain from predicting customer churn (assuming
conversion rate of 0.7)?

Explaining model predictions
https://shirinsplayground.netlify.com/2018/12/customer_churn_code/

Predictive Maintenance

Predictive Maintenance
Industry 4.0 & IoT
Uses advanced analytics and Machine Learning to optimize machine costs
and outpu...

Goal: Predict machine failure
before it happens to
Save costs
Reduce downtime
Optimize asset availability,
productivity an...

Goal: Predict machine failure
before it happens to
Save costs
Reduce downtime
Optimize asset availability,
productivity an...

Wind turbines
Motor Engines
Cars
Planes
Industrial machinery
Examples for Predictive Maintenance

Predictive Maintenance and Big Data
Relies on large amounts of data collected over long periods of time

Predictive Maintenance and Big Data
Relies on large amounts of data collected over long periods of time
Databases
Collect ...

Predictive Maintenance and Big Data
Relies on large amounts of data collected over long periods of time
Databases
Collect ...

Predictive Maintenance and Big Data
Relies on large amounts of data collected over long periods of time
Databases
Collect ...

Why can Predictive Maintenance be tricky?
Data quality
Raw data usually needs preprocessing & feature extraction

Why can Predictive Maintenance be tricky?
Data quality
Raw data usually needs preprocessing & feature extraction
We don't ...

Why can Predictive Maintenance be tricky?
Data quality
Raw data usually needs preprocessing & feature extraction
We don't ...

Why can Predictive Maintenance be tricky?
Data quality
Raw data usually needs preprocessing & feature extraction
We don't ...

Why can Predictive Maintenance be tricky?
Data quality
Raw data usually needs preprocessing & feature extraction
We don't ...

Why can Predictive Maintenance be tricky?
Data quality
Raw data usually needs preprocessing & feature extraction
We don't ...

Why can Predictive Maintenance be tricky?
Data quality
Raw data usually needs preprocessing & feature extraction
We don't ...

Predicting machine failure
An example analysis
Data set for predictive maintenance
Machine failure of 1000 instances
Label...

Time-series forecasting
Classification
Regression
Anomaly detection
Survival Analysis
Predicting machine failure
An exampl...

Time-series forecasting
Classification
Regression
Anomaly detection
Survival Analysis
Predicting machine failure
An exampl...

Same techniques as used before
in fraud detection & customer
churn
Classification: Predict whether
machine will likely bre...

Same techniques as used before
in fraud detection & customer
churn
Classification: Predict whether
machine will likely bre...

Predictive Maintenance with Survival Analysis
Cox Proportional Hazards regression model

Predictive Maintenance with Time-Series
Forecasting
Facebook's prophet algorithm1
logistic growth curve trend
yearly & wee...

Predictive Maintenance with Time-Series
Forecasting
Facebook's prophet algorithm1
logistic growth curve trend
yearly & wee...

Predictive Maintenance with Time-Series
Forecasting
Facebook's prophet algorithm1
logistic growth curve trend
yearly & wee...

Evaluating Predictive Maintenance
Cost/benefit calculation
Similar to the examples before:
Probability of detection = reca...

ROC curve:
Evaluating Predictive Maintenance
Cost/benefit calculation
Similar to the examples before:
Probability of detec...

ROC curve:
Optimize model to
=> avoid false alarms but maximize
true alarms
iterative Monte-Carlo-
Simulation to compare
c...

A Shiny demo
https://shiring.shinyapps.io/demo_dashboard_pred_maint_fast/

Thank you and keep in touch :-)
shirin.glander@gmail.com
shirin-glander.de
Twitter: @ShirinGlander
Github: @ShirinG...

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