Credit Card Fraud Detection Using Autoencoder & Decoder ML

Abstract

Abstract: In recent years credit card fraud has become one of the growing problems. It is vital that credit card companies can identify fraudulent credit card transactions, so that customers are not charged for the items that they did not purchase. The reputation of companies will heavily damage and endangered among the customers due to fraud in financial transactions The fraud detection techniques were increasing to improve accuracy to identify the fraudulent transactions. This project intends to build an unsupervised fraud detection method using autoencoder. An Autoencoder with four hidden layers which has been trained and tested with a dataset containing a European cardholder transaction that occurred in two days with 284,807 transactions from September 2013