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Fraud detection
Anomaly detection Fraud Banking Credit card Outlier detection Education Contracts Deployment Pdf
  1. Workflow
    Outlier Detection in Medical Claims
    Fraud detection Outlier detection
    This workflow identifies outliers in medical claim data such as claims with an unusual high cost for a certain disease. Firstly, …
    knime > Examples > 50_Applications > 14_Medical_Claims > 01_Interactive_Outlier_Detection
    2
    knime
  2. Workflow
    Outlier Dection / Fraud Detection in Contracts
    Fraud detection Anomaly detection Text processing
    +4
    Discover anomalies / irregularities / Frauds(?) in contracts payment amounts via: - data visualization - basic stats - clustering…
    rs1 > Public > Contracts_Fraud_Detection_Usecase_example
    1
    rs1
  3. Workflow
    Autoencoder MNIST MidPoint Focus
    Autoencoder Keras Neural network
    +8
    Exploring the latent space of an autoencoder for dimensional reduction
    iceman > Public > Autoencoder MNIST MidPoint Focus
    1
    iceman
  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
    kathrin
  5. 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
    kathrin
  6. 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
    knime
  7. 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
    knime
  8. 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
    knime
  9. Workflow
    Different options to train an autoencoder using TensorFlow 2
    Autoencoder Keras Neural network
    +5
    This workflow shows the different options of training and executing a network using TF2 on the example of an autoencoder: Option …
    knime > Examples > 04_Analytics > 14_Deep_Learning > 04_TensorFlow2 > 02_Tensorflow2_Autoencoder_for_Fraud_Detection_Training
    1
    knime
  10. 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
    jtyler
  11. 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
    jtyler
  12. Workflow
    Anomaly Detection (Exercise) - Solution
    Fraud detection Anomaly detection Text processing
    +5
    This workflow instructs you to detect anomalies in contracts via the IQR, z-score, and isolation forest techniques. Furthermore, …
    hayasaka > KNIME Fall Summit Training 2022 > L4-DV Low Code Data Extraction and Visualization > Session_4 > 02_Solutions > 04_Anomaly_Detection_Solution
    0
    hayasaka
  13. Workflow
    Outlier Dection / Fraud Detection in Contracts
    Fraud detection Anomaly detection Text processing
    +4
    Discover anomalies / irregularities / Frauds(?) in contracts payment amounts via: - data visualization - basic stats - clustering…
    chemgirl36 > Public Space > L4-DV Low Code Data Extraction and Visualization > Session_4 > 00_Demos > 01_Anomaly_Detection_Demo
    0
    chemgirl36
  14. Workflow
    Anomaly Detection (Exercise)
    Fraud detection Anomaly detection Text processing
    +5
    This workflow instructs you to detect anomalies in contracts via the IQR, z-score, and isolation forest techniques. Furthermore, …
    chemgirl36 > Public Space > L4-DV Low Code Data Extraction and Visualization > Session_4 > 01_Exercises > 04_Anomaly_Detection_Exercise
    0
    chemgirl36
  15. 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
    jtyler
  16. Workflow
    Outlier Dection / Fraud Detection in Contracts
    Fraud detection Anomaly detection Text processing
    +4
    Discover anomalies / irregularities / Frauds(?) in contracts payment amounts via: - data visualization - basic stats - clustering…
    hayasaka > KNIME Fall Summit Training 2022 > L4-DV Low Code Data Extraction and Visualization > Session_4 > 00_Demos > 01_Anomaly_Detection_Demo
    0
    hayasaka
  17. Workflow
    Anomaly Detection (Exercise)
    Fraud detection Anomaly detection Text processing
    +5
    This workflow instructs you to detect anomalies in contracts via the IQR, z-score, and isolation forest techniques. Furthermore, …
    hayasaka > KNIME Fall Summit Training 2022 > L4-DV Low Code Data Extraction and Visualization > Session_4 > 01_Exercises > 04_Anomaly_Detection_Exercise
    0
    hayasaka
  18. Workflow
    Training Workflow Fraud Detection
    Integrated deployment Continous Model monitoring
    +4
    In this simple example a Random Forest model has been trained to detect potential Fraud Transactions. To train the model it has b…
    knime > Continuous Deployment for Data Science > CDDS Workflows > Development > Fraud Detection with Monitoring > Training > Training_workflow
    0
    knime
  19. 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
    knime
  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 > Exercises > 01_Fraud_Detection_by_Supervised_Learning
    0
    knime