This document provides an overview of approaches for credit card fraud detection. It discusses using classification models for labeled datasets and autoencoders or isolation forests for unlabeled datasets. It also describes the credit card transaction dataset and features. Finally, it discusses building workflows in KNIME for fraud detection and deploying them via REST on KNIME Server.

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Tutorial on Credit Card Fraud Detection
Maarit Widmann
maarit.widmann@knime.com
Kathrin Melcher
kathrin.melcher@knime.com

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Approaches for a labeled vs. unlabeled dataset
• Situation 1: The dataset has enough fraud examples
– Train a classification model
• Situation 2: The dataset has no (or just a negligible
number of) fraud examples
– Use a neural autoencoder
– Use an outlier detection technique, e.g. isolation forest
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The data
• Kaggle dataset https://www.kaggle.com/mlg-ulb/creditcardfraud
• 284 807 credit card transactions performed in September 2013 by
European cardholders
• 492 (0.2 %) transactions in the dataset are fraudulent
• Features:
– 28 principal components
– Time from the first transaction
– Amount of money
• Class column with 2 labels
– fraudulent transactions = 1
– legitimate transactions = 0
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KNIME Analytics Platform
• A tool for data analysis, manipulation, visualization, and reporting
• Based on the graphical programming paradigm
• Provides a diverse array of extensions:
– Text Mining
– Network Mining
– Cheminformatics
– Many integrations,
such as Java, R, Python,
Weka, Keras, Plotly, H2O, etc.
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Situation 1: The dataset has enough fraud examples

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Model training with labeled data

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Model training with labeled data

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Situation 2: The dataset has no fraud examples

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Idea of an autoencoder
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Decoder
Training with numbers:
Input Compressed
representation
Reconstructed
input
− −= small = big
Encoder Decoder
Appling the trained autoencoder:
Encoder Decoder
Encoder

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Fraud detection using an autoencoder
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Input Layer Hidden Layers Output Layer
Input 𝒙 Output 𝒙‘
𝒙 𝒏𝒆𝒘 − 𝒙 𝒏𝒆𝒘
′ 2 > 𝛿 ⇒ anomaly
Each layer:
𝒉𝑖(𝒙𝑖−1) = 𝑓(𝑔 𝒙𝑖−1 ) = 𝑓(𝑾𝑖−1 𝒙𝑖−1))
f is a non-linear activation function,
e.g. tanh, relu.
min
𝑾 𝑖 𝑖∈(1,2,3,4)
𝐽(𝒙, 𝒙′
) with
𝐽 𝒙, 𝒙’ =
1
𝑛
෍
𝑗=1
𝑛
𝒙 − 𝒙′ 2
Execution of the Network:
The network structure on the left:
𝒙 ∈ ℝ5
ℎ1 𝒙 = 𝑓1 𝑾1 𝒙 ∈ ℝ3
ℎ2 𝒙 = 𝑓2(𝑾2 𝒉1) ∈ ℝ2
ℎ3 𝒙 = 𝑓3 𝑾3 𝒉2 ∈ ℝ3
𝒙′ = 𝑓4 𝑾4 𝒉3 ∈ ℝ5
Training of the network:

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Fraud detection using an autoencoder
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Isolation forest algorithm
Idea: Outlier can be isolated with less random splits
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𝑥1
𝑥2
𝑥2 𝑥2 𝑥2 𝑥2 𝑥2 𝑥2 𝑥2 𝑥2
𝑥1 𝑥1 𝑥1 𝑥1 𝑥1 𝑥1 𝑥1
𝑥2 𝑥2 𝑥2 𝑥2 𝑥2 𝑥2
𝑥1 𝑥1 𝑥1 𝑥1 𝑥1
𝑥2 𝑥2 𝑥2 𝑥2
𝑥2 𝑥2 𝑥2
𝑥1 𝑥1
𝑥2
𝑥2 𝑥2 𝑥2 𝑥2 𝑥2 𝑥2 𝑥2 𝑥2
𝑥1 𝑥1 𝑥1 𝑥1 𝑥1 𝑥1 𝑥1
𝑥2 𝑥2 𝑥2 𝑥2 𝑥2 𝑥2
𝑥1 𝑥1 𝑥1 𝑥1 𝑥1
𝑥2 𝑥2 𝑥2 𝑥2
𝑥2 𝑥2 𝑥2
𝑥1 𝑥1
𝑥1
𝑥2 𝑥2 𝑥2 𝑥2 𝑥2 𝑥2 𝑥2 𝑥2
𝑥1 𝑥1 𝑥1 𝑥1 𝑥1 𝑥1 𝑥1
𝑥2 𝑥2 𝑥2 𝑥2 𝑥2 𝑥2
𝑥1 𝑥1 𝑥1 𝑥1 𝑥1
𝑥2 𝑥2 𝑥2 𝑥2
𝑥2 𝑥2 𝑥2
𝑥2 𝑥2
𝑥1
𝑥1 𝑥1 𝑥1 𝑥1 𝑥1 𝑥1 𝑥1
𝑥2 𝑥2 𝑥2 𝑥2 𝑥2 𝑥2
𝑥1 𝑥1 𝑥1 𝑥1 𝑥1
𝑥2 𝑥2 𝑥2 𝑥2
𝑥2 𝑥2 𝑥2
𝑥1 𝑥1
𝑥2
=> shorter mean length

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Fraud detection using isolation forest
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Today’s project
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https://tinyurl.com/ydov2gvw

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The KNIME Hub
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https://hub.knime.com

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Deployment options

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A second workflow for deployment
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Deployment via REST on KNIME Server
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Workflow deployed as (REST) web service on KNIME Server
Workflow calling another workflow on KNIME Server

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The KNIME® trademark and logo and OPEN FOR INNOVATION®trademark are used by
KNIME AG under license from KNIME GmbH, and are registered in the United States.
KNIME® is also registered in Germany.
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Thank You
© 2019 KNIME AG. All Rights Reserved.
The KNIME® trademark and logo and OPEN FOR INNOVATION® trademark are used by
KNIME AG under license from KNIME GmbH, and are registered in the United States.
KNIME® is also registered in Germany.
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Thank You