Master's Thesis presentation, Ramin Maghsood

Abstract: Credit card fraud is an important and increasing problem for banks and individuals, all around the world. This thesis applies supervised and unsupervised nearest neighbor algorithms for fraud detection on a Kaggle data set consisting of 284,807 credit card transactions out of which 492 are frauds, and which includes 30 covariates per transaction. The supervised methods are shown to be quite efficient, but require that the user has access to labelled training data where one knows which transactions are frauds. Unsupervised detection is harder and, e.g., for finding find 80% of the frauds, the algorithm classifies more 50 times as many valid transactions as fraud cases. The unsupervised nearest neighbor distance method is compared to methods using the distance to the center of the data for fraud detection, and detection algorithms which combine the two methods.