Experimental Analysis of Anomaly Detection Algorithms on Banking data

In this era, use of credit has increased extensively. People are using credit cards more frequently in their daily lives. Millions of dollars were lost in forgery, however fraud detection methods have been developed to fix such types of problems. However, Nevertheless, we still face such problems through imbalance data. There is a requirement for a centralized claims database to collect views of holistic view of fraudulent characteristic behaviour. Credit card supervised learning are widely used to detect fraud based on the assumption that the pattern of fraud would depend on past transaction. This paper will give succinct understanding to avoid such problems. And will elaborate how we can use unsupervised techniques in forgery or fraud detection. In the beginning all the supervised techniques are given, types of anomalies and classification of anomaly detection techniques are given. In addition to that a project is done with the use of raw bank data and forgery is detected with unsupervised techniques.