Journal of Applied Data Sciences
Vol 2, No 1: JANUARY 2021

Semi-Supervised Classification on Credit Card Fraud Detection using AutoEncoders

Nur Rachman Dzakiyullah (Faculty Computer, Department of Informatics, Alma Ata Universty, Yogyakarta, Indonesia)
Andri Pramuntadi (Faculty Computer, Department of Informatics, Alma Ata University, Yogyakarta, Indonesia)
Anni Karimatul Fauziyyah (Faculty Computer, Department of Informatics, Alma Ata University, Yogyakarta, Indonesia)

Article Info

Publish Date
15 Jan 2021

Abstract

The use of credit cards for online purchases has increased dramatically and led to an explosion in credit card fraud. Credit card companies need to be able to identify fraudulent credit card transactions so that customers are not charged for items they do not buy. In this study, we will use semi-supervised learning and combine it with AutoEncoders to identify fraudulent credit card transactions. In this paper, we will implement the use of T-SNE to visualize fraud and non-fraud transactions, then improve the visualization using autoencoders. Classification report proved that it is possible to achieve very acceptable precision using semi-supervised classification to detect credit card fraud.

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Journal Info
Journal of Applied Data Sciences
Website

Abbrev

JADS

Publisher

Bright Publisher

Subject

Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

Description

One of the current hot topics in science is data: how can datasets be used in scientific and scholarly research in a more reliable, citable and accountable way? Data is of paramount importance to scientific progress, yet most research data remains private. Enhancing the transparency of the processes ...