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Fraud
Banking Credit card Fraud detection Autoencoder Cybersecurity
+4
  1. Workflow
    Fraud Detection Techniques Comparison
    Credit card Fraud DBSCAN
    +6
    This workflow shows an overview of outlier detection techniques for credit card fraud detection. The performance of the technique…
    knime > Finance, Accounting, and Audit > Fraud Detection Techniques Comparison
    3
  2. Workflow
    Keras Autoencoder for Fraud Detection - Training
    Autoencoder Keras Neural network
    +12
    This workflow trains an autoendcoder model to detect fraudulent transactions.
    kathrin > Codeless Deep Learning with KNIME > Chapter 5 > 01_Autoencoder_for_Fraud_Detection_Training
    1
  3. Workflow
    Keras Autoencoder for Fraud Detection Training
    Autoencoder Keras Neural network
    +16
    Partition numeric input data into a training, test, and validation set. Normalize the data into range [0,1]. Build a Keras autoen…
    knime > Examples > 50_Applications > 39_Fraud_Detection > 03_Keras_Autoencoder_for_Fraud_Detection_Training
    1
  4. Workflow
    Keras Autoencoder for Fraud Detection - Deployment
    Autoencoder Keras Neural network
    +15
    This workflow applies a trained autoencoder model to detect fraudulent transactions.
    kathrin > Codeless Deep Learning with KNIME > Chapter 5 > 02_Autoencoder_for_Fraud_Detection_Deployment
    1
  5. Workflow
    Overview of Credit Card Fraud Detection Techniques
    Credit card Fraud DBSCAN
    +8
    This workflow shows an overview of credit card fraud detection techniques. The performances of the techniques are evaluated on th…
    knime > Finance, Accounting, and Audit > Overview of Credit Card Fraud Detection Techniques
    1
  6. Workflow
    Keras Autoencoder for Fraud Detection Deployment
    Autoencoder Keras Neural network
    +16
    Read Keras model. Read deployment data, which are normalized into range [0,1]. Apply the Keras model to the deployment data, calc…
    knime > Examples > 50_Applications > 39_Fraud_Detection > 04_Keras_Autoencoder_for_Fraud_Detection_Deployment
    1
  7. Workflow
    Fraud Detection: Model Deployment
    Fraud Fraud detection Random forest
    +5
    This workflow, the deployment workflow, reads the trained model, as well as the new transaction and applies the model to classify…
    knime > Examples > 50_Applications > 39_Fraud_Detection > 02_Fraud_Detection_Deployment
    1
  8. Workflow
    Keras Autoencoder for Fraud Detection - Integrated Deployment Call
    Autoencoder Keras Neural network
    +16
    This workflow executes the model generated by the Integrated deployment to get a prediction of fraudolent transaction.
    knime > Education > Courses > L4-DL Introduction to Deep Learning > Supplementary workflows > Autoencoder > 02_Fraud_Detection_Call
    0
  9. Workflow
    Isolation Forest for Fraud Detection: Model Training
    Fraud H2o Isolation forest
    +3
    This workflow uses the KNIME H2O Machine Learning Integration to train an isolation forest model for fraud detection.
    knime > Examples > 50_Applications > 39_Fraud_Detection > 05_Isolation_Forest_for_Fraud_Detection_Training
    0
  10. Workflow
    Fraud Detection: Model Training
    Fraud Fraud detection Random forest
    +5
    This workflow reads in the creditcard.csv file and trains and evaluates a Random Forest model to classify transactions as either …
    knime > Examples > 50_Applications > 39_Fraud_Detection > 01_Fraud_Detection_Model_Training
    0
  11. Workflow
    Keras Autoencoder for Fraud Detection - Deployment
    Autoencoder Keras Neural network
    +16
    Exercise of the L4-DL Introduction to Deep Learning Course. The goal of this exercise is to apply a trained autoencoder to new tr…
    knime > Education > Courses > L4-DL Introduction to Deep Learning > Session2 > Exercises > 02_Fraud_Detection_Deployment
    0
  12. Workflow
    Fraud Detection by Supervised Learning
    Fraud Fraud detection Random forest
    +9
    This workflow reads in the creditcard.csv file and trains and evaluates a Logistic Regression and a Random Forest model to classi…
    knime > Education > Learnathons > Fraud_Detection_Tutorial > Exercises > 01_Fraud_Detection_by_Supervised_Learning
    0
  13. Workflow
    Fraud Detection by Unsupervised Learning
    Fraud Fraud detection Banking
    +8
    This workflow reads in the creditcard.csv file and trains and evaluates an Isolation Forest model that detects fraudulent transac…
    knime > Education > Learnathons > Fraud_Detection_Tutorial > Exercises > 02_Fraud_Detection_by_Unsupervised_Learning
    0
  14. Workflow
    Fraud Detection Techniques Comparison
    Credit card Fraud DBSCAN
    +5
    This workflow shows an overview of outlier detection techniques for credit card fraud detection. The performance of the technique…
    marcelo_linero > Public > Fraud Detection Techniques Comparison
    0
  15. Workflow
    Keras Autoencoder for Fraud Detection - Integrated Deployment
    Autoencoder Keras Neural network
    +13
    This workflow replicates the exercises of session 2, combining training and deployment using Integrated deployment. The purple bo…
    knime > Education > Courses > L4-DL Introduction to Deep Learning > Supplementary workflows > Autoencoder > 01_Fraud_Detection_Integrated_Deployment
    0
  16. Workflow
    Fraud Detection: JSON Input
    Fraud Fraud detection Random forest
    +6
    This workflow showcases how the Container Input (JSON) and Container Output (JSON) nodes can be used to create a REST API for a w…
    jtyler > KNIME-Edge-Server-Workflows > Example_Use_Cases > General > Fraud Detection > 04_Fraud_Detection_JSON_Input
    0
  17. Workflow
    Fraud Detection: Table Input
    Fraud Fraud detection Random forest
    +6
    This workflow showcases how the Container Input (Table) and Container Output (Table) nodes can be used to create a REST API for a…
    jtyler > KNIME-Edge-Server-Workflows > Example_Use_Cases > General > Fraud Detection > 02_Fraud_Detection_Table_Input
    0
  18. Workflow
    Fraud Detection: Row Input
    Fraud Fraud detection Random forest
    +6
    This workflow showcases how the Container Input (Row) and Container Output (Row) nodes can be used to create a REST API for a wor…
    jtyler > KNIME-Edge-Server-Workflows > Example_Use_Cases > General > Fraud Detection > 01_Fraud_Detection_Row_Input
    0
  19. Workflow
    Fraud Detection: Model Deployment
    Fraud Fraud detection Random forest
    +1
    This workflow, the deployment workflow, reads the trained model, as well as the new transaction and applies the model to classify…
    kathrin > Public > Faud_Detection_Autoencoder > Isolation_Forest_for_Fraud_Detection_Deployment
    0
  20. Workflow
    Fraud Detection by Supervised Learning
    Fraud Fraud detection Random forest
    +9
    This workflow reads in the creditcard.csv file and trains and evaluates a Logistic Regression and a Random Forest model to classi…
    knime > Education > Learnathons > Fraud_Detection_Tutorial > Solutions > 01_Fraud_Detection_by_Supervised_Learning
    0