Analysis of credit card fraud data using neural networks.

The datasets contains credit card transactions over a two day collection period in September 2013 by European cardholders. There are a total of 284,807 transactions, of which 492 (0.172%) are fraudulent.

The dataset contains numerical variables that are the result of a principal components analysis (PCA) transformation. This transformation was applied by the original authors to maintain confidentiality of sensitive information. Additionally the dataset contains Time and Amount, which were not transformed by PCA. The Time variable contains the seconds elapsed between each transaction and the first transaction in the dataset. The Amount variable is the transaction amount, this feature can be used for example-dependant cost-senstive learning. The Class variable is the response variable and indicates whether the transaction was fraudulant.

The dataset was collected and analysed during a research collaboration of Worldline and the Machine Learning Group of Université Libre de Bruxelles (ULB) on big data mining and fraud detection.

The model is a stratified k-fold multi-layer perceptron (MLP) neural network, implemented in Python using the Keras module and Theano backend (although you can use TensorFlow if you like).

Run the model using the following command:

KERAS_BACKEND=theano ipython src/model.py

The model achieves an overall f1 score of 0.99, with 99% sensitivity and 14% precision for the positive class. That is, the model correctly identifies 99% of the fraud cases (true positives) but only 14% of the transactions predicted as fraudulent were actually fraudulent.

The classification report and cross-tabulation are below:

             precision    recall  f1-score   support

        0.0       1.00      0.99      0.99    284315
        1.0       0.14      0.99      0.24       492

avg / total       1.00      0.99      0.99    284807

Predictions       0     1
Truth                    
0.0          281250  3065
1.0               6   486

Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015 (PDF)

The data was released under Open Database License and individual contents under Database Contents License.

This code repository is released under the MIT "Expat" License.