Credit Card Fraud Detection using Deep Learning based on Auto-Encoder and Restricted Boltzmann Machine

Apapan Pumsirirat,Liu Yan

doi:10.14569/ijacsa.2018.090103

Abstract

Frauds have no constant patterns. They always change their behavior; so, we need to use an unsupervised learning. Fraudsters learn about new technology that allows them to execute frauds through online transactions. Fraudsters assume the regular behavior of consumers, and fraud patterns change fast. So, fraud detection systems need to detect online transactions by using unsupervised learning, because some fraudsters commit frauds once through online mediums and then switch to other techniques. This paper aims to 1) focus on fraud cases that cannot be detected based on previous history or supervised learning, 2) create a model of deep Auto-encoder and restricted Boltzmann machine (RBM) that can reconstruct normal transactions to find anomalies from normal patterns. The proposed deep learning based on auto-encoder (AE) is an unsupervised learning algorithm that applies backpropagation by setting the inputs equal to the outputs. The RBM has two layers, the input layer (visible) and hidden layer. In this research, we use the Tensorflow library from Google to implement AE, RBM, and H2O by using deep learning. The results show the mean squared error, root mean squared error, and area under curve.

Highlights

Fraud detection in online shopping systems is the hottest topic nowadays
Fraud detection can be divided into two groups: anomaly detection and misuse detection [2]
A misuse fraud detection system uses the labeled transaction as normal or fraud transaction to be trained in the database history

Summary

Introduction

Fraud detection in online shopping systems is the hottest topic nowadays. Fraud investigators, banking systems, and electronic payment systems such as PayPal must have an efficient and complex fraud detection system to prevent fraud activities that change rapidly. According to a CyberSource report from 2017, the present fraud loss by order channel, that is, the percentage of fraud loss in their web store was 74 percent and 49 percent in their mobile channels [1] Based on this information, the lesson is to determine anomalies across patterns of fraud behavior that have undergone change relative to the past. A misuse fraud detection system uses the labeled transaction as normal or fraud transaction to be trained in the database history This misuse detection system entails a system of supervised learning and anomaly detection system a system of unsupervised learning. They use labeled datasets to train and to render it accurate by changing the parameters of the learning rate After that, they apply parameters of learning rate to the dataset, the techniques that implement supervised learning such as multilayerperceptron (MLP) to build the model based on the history of the database. This unsupervised learning is more difficult than supervised learning, because we have to use appropriate techniques to detect anomalous behavior

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Methods

Conclusion