Deep learning approach for credit card fraud detection
Date
2021-07
Authors
Journal Title
Journal ISSN
Volume Title
Type
Article
Publisher
Institute of Electrical and Electronics Engineers Inc.
Series Info
ICEEM 2021 - 2nd IEEE International Conference on Electronic Engineering.;3 July 2021 Article number 94806392nd IEEE International Conference on Electronic Engineering, ICEEM 2021,
Doi
Scientific Journal Rankings
Abstract
As technology evolves rapidly, the world is using credit cards instead of cash in its everyday lives, opening up a new way for fraudulent people to abuse them. Credit card fraud losses reached approximately $28.65 billion in 2019, according to Nilsson's report, and global card fraud is expected to reach around $32.96 billion by 2023. Providers should therefore develop an efficient model to detect and prevent fraud early. In this paper, we used deep learning techniques as an effective way to detect fraudsters in credit card transactions. Therefore, we present a model for predicting legitimate transactions or fraud on Kaggle's credit card dataset. The proposed model is OSCNN (Over Sampling with Convolution Neural Network) which is based on over-sampling preprocessing and CNN (convolution neural network). The MLP (Multi-layer perceptron) was also applied to the dataset. Comparing the MLP-OSCNN results, they proved that the proposed model achieved better results with 98% accuracy. © 2021 IEEE.
Description
Scopus
Keywords
And fraud detection, CNN, Credit card, Deep learning, Imbalanced data