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Credit Card Fraud Detection Tutorial

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KNIMESlides
KNIMESlides

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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© 2019 KNIME AG. All Right Reserved. Tutorial on Credit Card Fraud Detection Maarit Widmann maarit.widmann@knime.com Kathrin Melcher kathrin.melcher@knime.com
© 2019 KNIME AG. All Rights Reserved. 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
© 2019 KNIME AG. All Rights Reserved. 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 6
© 2019 KNIME AG. All Rights Reserved. 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. 7
© 2019 KNIME AG. All Rights Reserved. 8 Situation 1: The dataset has enough fraud examples
© 2019 KNIME AG. All Rights Reserved. Model training with labeled data
© 2019 KNIME AG. All Rights Reserved. Model training with labeled data
© 2019 KNIME AG. All Rights Reserved. 11 Situation 2: The dataset has no fraud examples
© 2019 KNIME AG. All Rights Reserved. Idea of an autoencoder 12 Decoder Training with numbers: Input Compressed representation Reconstructed input − −= small = big Encoder Decoder Appling the trained autoencoder: Encoder Decoder Encoder
© 2019 KNIME AG. All Rights Reserved. Fraud detection using an autoencoder 13 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:
© 2019 KNIME AG. All Rights Reserved. Fraud detection using an autoencoder 14
© 2019 KNIME AG. All Rights Reserved. Isolation forest algorithm Idea: Outlier can be isolated with less random splits 15 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
© 2019 KNIME AG. All Rights Reserved. Fraud detection using isolation forest 16
© 2019 KNIME AG. All Rights Reserved. Today’s project 17 https://tinyurl.com/ydov2gvw
© 2019 KNIME AG. All Rights Reserved. The KNIME Hub 18 https://hub.knime.com
© 2019 KNIME AG. All Rights Reserved. 20 Deployment options
© 2019 KNIME AG. All Rights Reserved. A second workflow for deployment 21
© 2019 KNIME AG. All Rights Reserved. Deployment via REST on KNIME Server 22 Workflow deployed as (REST) web service on KNIME Server Workflow calling another workflow on KNIME Server
© 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. 23 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. 24 Thank You

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Credit Card Fraud Detection Tutorial

  • 1. © 2019 KNIME AG. All Right Reserved. Tutorial on Credit Card Fraud Detection Maarit Widmann maarit.widmann@knime.com Kathrin Melcher kathrin.melcher@knime.com
  • 2. © 2019 KNIME AG. All Rights Reserved. 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
  • 3. © 2019 KNIME AG. All Rights Reserved. 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 6
  • 4. © 2019 KNIME AG. All Rights Reserved. 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. 7
  • 5. © 2019 KNIME AG. All Rights Reserved. 8 Situation 1: The dataset has enough fraud examples
  • 6. © 2019 KNIME AG. All Rights Reserved. Model training with labeled data
  • 7. © 2019 KNIME AG. All Rights Reserved. Model training with labeled data
  • 8. © 2019 KNIME AG. All Rights Reserved. 11 Situation 2: The dataset has no fraud examples
  • 9. © 2019 KNIME AG. All Rights Reserved. Idea of an autoencoder 12 Decoder Training with numbers: Input Compressed representation Reconstructed input − −= small = big Encoder Decoder Appling the trained autoencoder: Encoder Decoder Encoder
  • 10. © 2019 KNIME AG. All Rights Reserved. Fraud detection using an autoencoder 13 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:
  • 11. © 2019 KNIME AG. All Rights Reserved. Fraud detection using an autoencoder 14
  • 12. © 2019 KNIME AG. All Rights Reserved. Isolation forest algorithm Idea: Outlier can be isolated with less random splits 15 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
  • 13. © 2019 KNIME AG. All Rights Reserved. Fraud detection using isolation forest 16
  • 14. © 2019 KNIME AG. All Rights Reserved. Today’s project 17 https://tinyurl.com/ydov2gvw
  • 15. © 2019 KNIME AG. All Rights Reserved. The KNIME Hub 18 https://hub.knime.com
  • 16. © 2019 KNIME AG. All Rights Reserved. 20 Deployment options
  • 17. © 2019 KNIME AG. All Rights Reserved. A second workflow for deployment 21
  • 18. © 2019 KNIME AG. All Rights Reserved. Deployment via REST on KNIME Server 22 Workflow deployed as (REST) web service on KNIME Server Workflow calling another workflow on KNIME Server
  • 19. © 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. 23 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. 24 Thank You