Fraud Detection Model Using Deep Learning
In this rutorial I will you through one of the most important applications in machine learning, I will demonstrate how can construct a high-performance model to predict a credit card fraud by using deep learning model.
Dataset
The datasets contain transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.
It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.
Model overview
In this tutorial I will use a 3 fully-connected layers + Dropout, and ReLu as activation function, the first layer contains 200 units, the second also with 200 units, and the third with a single output unit. For optimization algorithm, I used Adam optimization to optimize the Accuracy Matrix.