An Autoencoder Based Model for Detecting Fraudulent Credit Card Transaction
Sumit Misra, Soumyadeep Thakur, Manosij Ghosh, Sanjoy Kumar Saha · Procedia Computer Science · 2020
With the rapid growth in credit card based financial transactions, it has become important to identify the fraudulent ones. In this work, a two stage model is proposed to identify such fraudulent transactions. To make a fraud detection system trustworthy, both miss in fraud detection and false alarms are to minimized. Understanding and learning the complex associations among the transaction attributes is a major problem. To address this issue, at the first stage of the proposed model an autoencoder is used to transform the transaction attributes to a feature vector of lower dimension. The feature vector thus obtained is used as the input to a classifier at the second stage. Experiment is done on a benchmarked dataset. It is observed that in terms of F1-measure, proposed two stage model performs better than the systems relying on only classifier and other autoencoder based systems.