Fraud Detection Using Random Forest, Neural Autoencoder, and Isolation Forest Techniques

With global credit card fraud loss on the rise, it is important for banks, as well as e-commerce companies, to be able to detect fraudulent transactions (before they are completed). According to the Nilson Report, a publication covering the card and mobile payment industry, global card fraud losses amounted to $22.8 billion in 2016, an increase of 4.4% over 2015. This confirms the importance of the early detection of fraud in credit card transactions. Fraud detection in credit card transactions is a very wide and complex field. Over the years, a number of techniques have been proposed, mostly stemming from the anomaly detection branch of data science. In the first scenario, we can deal with the problem of fraud detection by using classic machine learning or statistics-based techniques. We can train a machine learning model or calculate some probabilities for the two classes (legitimate transactions and fraudulent transactions) and apply the model to new transactions so as to estimate their legitimacy. All supervised machine learning algorithms for classification problems work here, e.g., random forest, logistic regression, etc.