Detection of Fraudulence in Credit Card Transactions using Machine Learning on Azure ML

With the advancement of mobile and cloud technologies, there is a sharp increase in online transactions. Detecting fraudulent credit card transactions on a timely basis is a very critical and challenging problem in Financial Industry. Although online transactions are very convenient, they bring the risk of fraudulence on many aspects. Some of the key challenges in detecting fraudulence in online transactions include irregular behavioral patterns, skewed dataset i.e. high normal transaction to fraudulent transaction ratio, limited availability of data and dynamically changing environment. Every year people lose millions of dollars due to credit card fraud. There is a lack of quality research in this domain. We have used a dataset comprising of European cardholders which has 284,807 transactions to model our system. In this paper, we will design and develop credit card fraudulence detection system by training and testing two ML algorithms: Decision Forest (DF) and Decision Jungle (DJ) classifiers. Our results successfully demonstrate that DJ classifier delivers higher performance compared to DF classifier.