NASSCOM Presentation

1. How can you Identify Fraud in Fintech Lending using
Deep Learning
RATNAKAR PANDEY, HEAD OF INDIA ANALYTICS & DATA SCIENCE, KABBAGE
Disclaimer: The views expressed here are solely those of the presenter in his private capacity.
16th October 2018

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Legal Disclaimer
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3. FRAUD IS A BIG PROBLEM
ACROSS THE WORLD
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4. Outline
Demo of Multi Level Perceptron (MLP)
Classification Case Approach and Performance
Suggested Deep Learning Application Areas
Supervised Unsupervised
Need for Deep Learning
Existing Methods Why Deep Learning?
Frauds in Fintech Lending
Drivers Modus Operandi
Introduction
About Fintech About Kabbage
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5. Fintech is an Integral Part of Our Life Now
$24.7 B
Invested in 2016 in
global fintech companies
1076
Deals in 2016 in
global fintech companies
Sources: KPMG, The Pulse of Fintech Q4 2016 | Capgemini World Fintech Report 2017 | PwC Global Fintech Report 2017 | www.forbes.com
50.2%
Of global customers have
done business with fintech
20%
Expected ROI on
fintech projects
20+
Global fintech
Unicorns
10K+
Global fintech
companies
Types
of
Fintech
Alternative Lending- Kabbage, Lendingclub, Prosper, Zopa
Payment / Billing Tech - Stripe, Paytm, Adyen, Ant Financial,
Square
Personal Finance / Asset Management Creditkarma, Bankrate,
NerdWallet
Robo Advisory- Wealthfront, Betterment, NerdWallet
Blockchain- Abra, 21, coinbase, Ethereum
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6. Kabbage is Blazing a Trail in Big Data & Fintech
Kabbage is more than a lender for small businesses; our data and technology
platform is now being used as a fully branded product by other lenders, and
our products are expanding. We’ve received numerous awards & recognition,
including-
• CNBC Disruptors 50 list
• Inc. 500 list for three consecutive years
• The Forbes Most Promising Companies lists twice
• Glassdoor’s 2017 Best Places to Work list
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7. Fraud Drivers- Superfast Decision Making and Faceless Channels
Decisioning within few minutes
Application on web and Mobile
May have higher exposure to
thin file and new to credit
More prone to invisible window
applications
Unconventional and evolving
data sources
Note: Even with these challenges the fraud rate in the industry is typically less than 20 bps for more data savvy lenders 7

8. How a Lending Fraud can be Classified?
Who
Commits?
How?
Who is the
Victim?
Borrower
Someone known to the
borrower- lead
generator, friends, family
employees etc.
Someone unknown to
the borrower
First Payment Default,
Bust Out, Synthetic
Identity, Stacking etc.
Friendly Fraud-
someone misuses the
trust
Fraud rings, Identity
Theft, Account Takeover
Lender Borrower, Lender Borrower, Lender
First Party Second Party Third Party
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9. Sample Modus Operandi
• Stolen identity
• Synthetic identity
• May replicate best
customer (prime
and super prime)
• Falsified info
• No willingness to
pay
• Acquire multiple loans
in a short window (
invisible window)
• May provide all info
correctly
• More likely to be on
higher side in the risk
spectrum
• No or low willingness to
pay
• Mimic good payment
behavior for significant
time
• Bust out when gains
are highestCommon Fraud Related Terms- http://www.cpp.co.uk/helpful-info/fraud-glossary-of-terms
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10. Current Situation- Heuristics and Regression Driven Approaches
Intuitive
Heuristics
Statistical
• Manual Reviews
• Experts Driven
• Gut feeling
• Thumb rules
• Driven by past experience
• Quick decision making
• Control/ confidence limits
• Outlier detection/ deviation from norm
• Decision tree, regression, time series
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11. 10,000 +
Features
Unstructured
Transactional
Social
Device
&
IP
Third Parties
Bureau
Why go Deep? Explosion of Features and Data Sources
• Uncover hard to detect patterns
(using traditional techniques) when
the incidence rate is low
• Find latent features (super variables)
without significant manual feature
engineering
• Real time fraud detection and self
learning models using streaming data
(KAFKA, MapR)
• Ensure consistent customer
experience and regulatory
compliance
• Higher operational efficiency
• Big data and data exhaust handling
capabilities
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12. UNSUPERVISED DEEP LEARNING ALGORITHMS AND
USE CASES
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13. Find Anomalies- Autoencoder
• Traditional techniques based on density or
distance works better with linearly separable
data
• Stacked Autoencoders (SAE) and Deep Belief
Networks ( DBN) make no assumptions about
the distribution of data and work better on non
linearly separable data
• Unsupervised learning algorithms for feature
learning, feature reduction and outlier detection
• Input vectors are used as output vectors and
reconstruction error computed
• The data points with higher reconstruction error
( MSE) are more likely to be outliers
• Helps in detecting different modus operandi of
fraudsters
Use Case- Deployment of Autoencoder for Credit Card Fraud Detection
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14. Sequence Analysis- Recurrent Neural Network (LSTM)
• Recurrent Neural Network (RNN) are a special
type of feed-forward network used for
sequential data analysis where inputs are not
independent and are not of fixed length
• Rather in this case, inputs are dependent on
each other along the time dimension. In other
words, what happens in time ‘t’ may depend on
what happened in time ‘t-1’, ‘t-2’ and so on
• These are also called ‘memory’ networks as
previous inputs and states persist in the model
for doing a more optimal sequential analysis.
They can have both short term and long term
time dependence.
• Long Short Term Memory (LSTM) is one of the
most popular Deep Network used for sequential
data analysis.
• More on LSTM Here-
https://datafai.com/2018/03/08/recurrent-
neural-network-rnn-in-python/
Use Case- Use RNN (LSTM) to analyse web behaviour and logs to detect
fraudulent behavior
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15. Find Networks - Clique and Links Graphs
Detect
Fraudulent
Cases
Find
Commonalities
Form Network
• Use variety of attributes (on-us/ off-us) to build linkage between known bad
customers and other customers with unknown status
• Larger the size of network, easier the detection and vice versa
• Overlap networks using enumerative approaches and find commonalities
• Use graph transduction (t-SNE) to detect potential fraudulent cases by doing peer
group (archetype) analysis to separate routine behavior from suspicious behavior -
“birds of same feather flock together”
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16. SUPERVISED DEEP LEARNING ALGORITHMS AND USE
CASES
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17. Real Time Detection- Convolution Neural Network (CNN)
• Convolution Neural Network (CNN) are
particularly useful for spatial data analysis, image
recognition, computer vision, natural language
processing, signal processing and variety of
other different purposes. They are biologically
motivated by functioning of neurons in visual
cortex to a visual stimuli.
• What makes CNN much more powerful
compared to the other feedback forward
networks for image recognition is the fact that
they do not require as much human
intervention and parameters as some of the
other networks such as MLP do. This is primarily
driven by the fact that CNNs have neurons
arranged in three dimensions.
• More on CNN Here-
https://datafai.com/2018/02/25/deep-learning-
convolution-neural-network-cnn-in-python/
Use Case- CNN for real time classification
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18. Labeled Data- Multilayer Perceptron (MLP)
• These are the most basic networks and feed
forward the inputs to create output. They
consist of an input layer and an output layer
and many interconnected hidden layers and
neurons between the input and the output
layers.
• They can be used for any supervised regression
or classification problems
• Since they generally use some non linear
activation function such as Relu or Tanh to
compute the losses ( the difference between the
true output and computed output) such as
Mean Square Error ( MSE), Logloss, they are
more suitable for handling non linear problems.
• We will do a MLP Demo on credit card fraud
data
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19. MLP Demo- Case Details
• Anonymized credit card transactions data from European customers
• 30 features ( 28 anonymized, duration elapsed, amount of transactions)
• Label- fraud or normal transaction
• 17bps incidence rate for fraudulent transactions
• 284,807 total transaction in data
Sources: http://mlg.ulb.ac.be | https://www.kaggle.com/dalpozz/creditcardfraud
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20. MLP Demo- Tools and Techniques used
Python
2.7 or 3.6
Keras
2.0.2
TensorFlow
1.0.1
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21. MLP Demo- Traditional Modeling Techniques Process
Manual
Feature
Engineering
After variable
treatments
drop variables
with little or no
explaining
power- WOE,
IV, Distribution
Look at WOE
to create bins
etc.
WOEDensity Dist.
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22. MLP Demo- Network Training
Little or No Manual Feature Engineering
• No over or under sampling
• No variables dropped
• Only standardization of features done
• 75% training/ 25% validation
• No manual binning
Fitted Network
• Multi Layer Perceptron with three hidden layers.
o Activation function = Sigmoid
o # of neurons = 512 in the input layer
o Each consequent layer has half the neurons
o Cost function = logloss
o Optimizer = adam
o Epochs= 5
o Dropout rate = 30%
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23. MLP Demo- Performance Summary
Metric Value
Accuracy Score 99.9%
Logloss 0.003
Precision Score 77%
Recall Score 75%
Area Under the
Curve (AUC)
87.4%
FScore 76.5%
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24. MLP Demo- Hyperparameters Optimization
• Epochs = [5,10,15,20,25…]
• Batch Size = [5,10,20,30,40…]
• Optimizer= [‘SGD’, ’Adam’, ’RMSprop’…]
• Learning Rate = [0.01,0.05,0.1,0.2…]
• Momentum = [0.2,0.4,0.6,…]
• Weights Initiation= [‘Uniform’, ‘Normal’, …]
• Activation Function= [‘relu’,’sigmoid’, ‘tanh’, ‘softmax’,…]
• Drop-out rate= [0.0,0.2,0.4,0.5,…]
• Neurons= [5,10,20,30,40…]
Python scikit-learn gridsearch function, design of experiment( screening
design, fractional designs) needs to be combined with intutition and expertise
to come out with the best network!
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25. Thank You!
Christopher McDougall- “Every morning in Africa, a gazelle wakes up, it knows it must outrun the fastest lion
or it will be killed. Every morning in Africa, a lion wakes up. It knows it must run faster than the slowest
gazelle, or it will starve. It doesn't matter whether you're the lion or a gazelle-when the sun comes up, you'd
better be running.
Working in the fraud analytics is the same way.
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26. Next Webinar : Go-to-market strategy / Planning
Date : 2nd Nov 2018
Speaker: Ashok Munirathinam, Sr. Director, SAP Cloud Platform
SAP Asia Pacific & Japan
Queries: Ankita@nasscom.in
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