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Fraud Detection Techniques Comparison

Fraud Detection Techniques Comparison
Quantile-, distribution-, and cluster-based techniques Autoencoder and isolation forest This workflow compares techniques for credit card fraud detection Z-scorenormalizationTarget columngood/bad80/20Top: good2/3 of "goods"for training1/3 of negativesand all positivesfor validationDBSCANRead credit card data (sample)Download full dataset via the Kaggle link in the descriptionCheck execution times oftechniques Normalizer Rule Engine Partitioning Quantile based Distribution based Nominal ValueRow Splitter Partitioning Concatenate Clusteringbased method File Reader Joiner Joiner Joiner Timer Info Merge Variables Table View Calculate minutesand seconds Isolation forest Autoencoder Normalizer (Apply) Joiner Bar Chart Quantile-, distribution-, and cluster-based techniques Autoencoder and isolation forest This workflow compares techniques for credit card fraud detection Z-scorenormalizationTarget columngood/bad80/20Top: good2/3 of "goods"for training1/3 of negativesand all positivesfor validationDBSCANRead credit card data (sample)Download full dataset via the Kaggle link in the descriptionCheck execution times oftechniquesNormalizer Rule Engine Partitioning Quantile based Distribution based Nominal ValueRow Splitter Partitioning Concatenate Clusteringbased method File Reader Joiner Joiner Joiner Timer Info Merge Variables Table View Calculate minutesand seconds Isolation forest Autoencoder Normalizer (Apply) Joiner Bar Chart

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Created by: Maarit Laukkanen
Created at: 2020-09-01
On NodePit since: 2021-03-02
Last update: 2024-04-27
Created with KNIME version: v4.2.1
Tags: credit cardfraudDBSCANbox plotz-scoreoutlierisolation forestautoencoder

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