This document discusses approaches for fraud detection using labeled and unlabeled datasets. For labeled data, a classification model can be trained to detect fraud. For unlabeled data, an autoencoder or isolation forest can be used. An autoencoder learns a compressed representation of normal transactions and flags outliers based on reconstruction error. Isolation forest detects outliers by measuring the number of splits needed to isolate observations. The document provides examples of implementing these techniques in KNIME workflows and highlights their use in fraud detection applications.

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Automating Inferences out of Financial Data
Based on the example of credit card fraud detection
Maarit Widmann maarit.widmann@knime.com
Mathilde Humeau mathilde.humeau@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
2

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Situation 1: The dataset has enough fraud examples

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Fraud detection using a labeled dataset
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Transactions
• Trx 1
• Trx 2
• Trx 3
• Trx 4
• Trx 5
• Trx 6
• …
Model

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KNIME Analytics Platform
• An open source 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.
5

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Model training with labeled data
Workflow on the KNIME Hub:
https://kni.me/w/gwBpbUtj0awOERjg

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The Final Goal of a Classification Model
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Contact customers for no reason
vs. accept a higher amount of fraud

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Model training with labeled data
Classification based on the predicted
positive class score
Optimize on Cohen’s
kappa

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Classifying Imbalanced Data

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Classifying Imbalanced Data
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Accuracy = 99.9 %
Accuracy = 95.4 %
x
Fraudulent
Legitimate
% Correctly classified
x
Fraudulent
Legitimate
• Some accuracy metrics are not informative when the target class is imbalanced
y
y
y
99 %
51 %
% Correctly classified
y
98 %
93 %

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• Resample data in order to make the target class distribution balanced
Handling Imbalanced Data
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x
Fraudulent
Legitimate
y
x
Fraudulent
Legitimate
y

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SMOTE
• Generate events into the
minority class
Undersampling
• Remove a random
sample of the majority
class events
Oversampling
• Duplicate a random
sample of the minority
class events
Resampling Techniques
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Unbalanced data
x
Fraudulent
Legitimate
y
x
Fraudulent
Legitimate
y
x
Fraudulent
Legitimate
y
x
Fraudulent
Legitimate
y

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Situation 2: The dataset has no fraud examples

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Fraud detection using an unlabeled dataset
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Fault Detection
Fraud Detection
Predictive Maintenance
Intrusion
Medicine
Heart Beat
Sensor Data
AssemblingDetails
Transactions
Networks
Finance
IoT
Weather Information
Fraud Detection
System Health Monitoring

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What is an autoencoder?
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Input Layer Hidden Layers Output Layer
Input 𝒙 Output 𝒙‘
Feature vector of a
transaction (time,
amount, etc.) Linear transformation
of the feature vector
Reconstructed feature
vector of a transaction
(time, amount, etc.)
Distance between 𝒙 and 𝒙‘
→ fraudulent or legitimate

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Example 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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Workflow on the KNIME Hub:
https://kni.me/w/9qFNMrsuN4PH1hRg

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Fraud detection using isolation forest
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Workflow on the KNIME Hub:
https://kni.me/w/xSIWSAh_u-fwgi5B

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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,
i.e. less random splits

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Fraud Detection in Labeled and Non-Labeled Data
• Fraud Detection Using a Neural Autoencoder
as #13 most read article on
• Fraud Detection using Random Forest, Neural Autoencoder, and Isolation
Forest techniques tutorial on
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The KNIME Hub
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