Advanced Credit Card Fraud Detection: An Ensemble Learning Using Random Under Sampling and Two-Stage Thresholding

The increase of Credit Card (CC) fraud in recent years requires the development of fraud detection systems that are both efficient and robust. This paper explored the utilization of machine learning models, with a particular emphasis on ensemble methods, to advance the detection of CC fraud. We pres...

Full description

Saved in:
Bibliographic Details
Main Author: Ibrahim Almubark
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10804800/
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1832592938268360704
author Ibrahim Almubark
author_facet Ibrahim Almubark
author_sort Ibrahim Almubark
collection DOAJ
description The increase of Credit Card (CC) fraud in recent years requires the development of fraud detection systems that are both efficient and robust. This paper explored the utilization of machine learning models, with a particular emphasis on ensemble methods, to advance the detection of CC fraud. We present an ensemble model that incorporates different classifiers to address the dataset imbalance issue that is present in most CC datasets. We employed synthetic over-sampling and under-sampling techniques in certain machine learning algorithms to tackle the same issue. To address the issue of under sampling, we implemented ensemble random sampling, which was subsequently followed by Two-Stage Thresholding (TST) to eliminate outliers. The well-known Synthetic Minority Over-sampling Technique (SMOTE) was used for over-sampling. Data was utilized to train the model and subsequently generate predictions by utilizing testing data following the pre-processing of the dataset. The proposed ensemble model outperformed conventional methods in addressing the challenges associated with CC fraud detection, as demonstrated by a comparative analysis. Our findings suggest that ensemble approaches are effective at fighting fraud, paving the way for more adaptive and resilient fraud detection systems.
format Article
id doaj-art-8ae2a8dd0947425a89bfc980302003ec
institution Kabale University
issn 2169-3536
language English
publishDate 2024-01-01
publisher IEEE
record_format Article
series IEEE Access
spelling doaj-art-8ae2a8dd0947425a89bfc980302003ec2025-01-21T00:01:25ZengIEEEIEEE Access2169-35362024-01-011219207919208910.1109/ACCESS.2024.351933510804800Advanced Credit Card Fraud Detection: An Ensemble Learning Using Random Under Sampling and Two-Stage ThresholdingIbrahim Almubark0https://orcid.org/0009-0005-0735-501XDepartment of Information Technology, College of Computer, Qassim University, Buraydah, Saudi ArabiaThe increase of Credit Card (CC) fraud in recent years requires the development of fraud detection systems that are both efficient and robust. This paper explored the utilization of machine learning models, with a particular emphasis on ensemble methods, to advance the detection of CC fraud. We present an ensemble model that incorporates different classifiers to address the dataset imbalance issue that is present in most CC datasets. We employed synthetic over-sampling and under-sampling techniques in certain machine learning algorithms to tackle the same issue. To address the issue of under sampling, we implemented ensemble random sampling, which was subsequently followed by Two-Stage Thresholding (TST) to eliminate outliers. The well-known Synthetic Minority Over-sampling Technique (SMOTE) was used for over-sampling. Data was utilized to train the model and subsequently generate predictions by utilizing testing data following the pre-processing of the dataset. The proposed ensemble model outperformed conventional methods in addressing the challenges associated with CC fraud detection, as demonstrated by a comparative analysis. Our findings suggest that ensemble approaches are effective at fighting fraud, paving the way for more adaptive and resilient fraud detection systems.https://ieeexplore.ieee.org/document/10804800/Credit card fraud detectionensemble learningimbalanced datarandom under samplingSMOTE
spellingShingle Ibrahim Almubark
Advanced Credit Card Fraud Detection: An Ensemble Learning Using Random Under Sampling and Two-Stage Thresholding
IEEE Access
Credit card fraud detection
ensemble learning
imbalanced data
random under sampling
SMOTE
title Advanced Credit Card Fraud Detection: An Ensemble Learning Using Random Under Sampling and Two-Stage Thresholding
title_full Advanced Credit Card Fraud Detection: An Ensemble Learning Using Random Under Sampling and Two-Stage Thresholding
title_fullStr Advanced Credit Card Fraud Detection: An Ensemble Learning Using Random Under Sampling and Two-Stage Thresholding
title_full_unstemmed Advanced Credit Card Fraud Detection: An Ensemble Learning Using Random Under Sampling and Two-Stage Thresholding
title_short Advanced Credit Card Fraud Detection: An Ensemble Learning Using Random Under Sampling and Two-Stage Thresholding
title_sort advanced credit card fraud detection an ensemble learning using random under sampling and two stage thresholding
topic Credit card fraud detection
ensemble learning
imbalanced data
random under sampling
SMOTE
url https://ieeexplore.ieee.org/document/10804800/
work_keys_str_mv AT ibrahimalmubark advancedcreditcardfrauddetectionanensemblelearningusingrandomundersamplingandtwostagethresholding