Deploying the Fraud Detector

At this point, we have an autoencoder network and a rule with acceptable performance for fraud detection. In this section, we will implement the deployment workflow.

The deployment workflow (Figure 5.11), like all deployment workflows, takes in new transaction data, passes it through the autoencoder, calculates the distance, applies the fraud detection rule, and finally, flags the input transaction as fraud or legitimate.

This workflow, named 02_Autoencoder_for_Fraud_Detection_Deployment, is downloadable from the KNIME Hub: https://hub.knime.com/kathrin/spaces/Codeless%20Deep%20Learning%20with%20KNIME/latest/Chapter%205/:

Figure 5.11 – The deployment workflow

Let's have a look at the different parts of the workflow in detail.

Reading Network, New Transactions, and Normalization Parameters

In this workflow, first the autoencoder model is read from the previously saved Keras file, using the Keras...