Training Workflow Fraud Detection

In this simple example a Random Forest model has been trained to detect potential Fraud Transactions.

To train the model it has been generated one year of data (current date to +1 year) following the "Reproducible Machine Learning for Credit Card Fraud Detection - Practical Handbook".

We opted to keep simple the model, for this reason we are arbitrary labeling as Fraud transactions the 99 percentile of trained dataset.

Then we have applied an artificially inflation to our future transactions to be sure that our model needs to retrain after a certain number of iterations.

External resources

• An Introduction to Integrated Deployment
• Transaction data simulator