Authors :
Abubakar Babayo Munkaila; Abdulsalam Ya’u Gital; A. M. Kwami; Ramson Emmanuel Nannim; Mustapha Abdulrahman Lawal; Ismail Zahraddeen Yakubu
Volume/Issue :
Volume 9 - 2024, Issue 1 - January
Google Scholar :
http://tinyurl.com/mukcakm7
Scribd :
http://tinyurl.com/3ujjxdj3
DOI :
https://doi.org/10.5281/zenodo.10629428
Abstract :
Fraud detection is a critical aspect of
safeguarding financial systems and online transactions.
Traditional methods often face challenges in handling
imbalanced datasets, where fraudulent instances are
significantly outnumbered by legitimate transactions.
This research explores the effectiveness of combining
deep learning methods with Adaptive Synthetic Sampling
(ADASYN) to improve the performance of fraud
detection models. Experimentation on python shows that
the proposed DNN with ADASYN model achieved the
best and highest classification accuracy of 97.8% as
against the existing algorithm including DT with SMOTE
which achieved 91%, NB with SMOTE which achieved
95% and RF with SMOTE which achieved 95%
respectively. thus, from the experiment, it is noticed that
addressing data class imbalance using techniques like
ADASYN and SMOTE can positively impact fraud
detection accuracy by mitigating the challenges posed by
imbalanced datasets. The successful development of the
proposed method has extended the detection accuracy,
precision, recall and F-score of the methods compared to
other classical machine learning methods. Thus, this
enhances the effective fraud detection system for e-
commerce security and trustworthiness of the platform
protect users from fraudulent activities, reduce financial
losses, and preserve the platform's reputation.
Keywords :
Deep Neural Network; Feature Extraction; Machine Learning; Fraud Detection, ADASYN.
Fraud detection is a critical aspect of
safeguarding financial systems and online transactions.
Traditional methods often face challenges in handling
imbalanced datasets, where fraudulent instances are
significantly outnumbered by legitimate transactions.
This research explores the effectiveness of combining
deep learning methods with Adaptive Synthetic Sampling
(ADASYN) to improve the performance of fraud
detection models. Experimentation on python shows that
the proposed DNN with ADASYN model achieved the
best and highest classification accuracy of 97.8% as
against the existing algorithm including DT with SMOTE
which achieved 91%, NB with SMOTE which achieved
95% and RF with SMOTE which achieved 95%
respectively. thus, from the experiment, it is noticed that
addressing data class imbalance using techniques like
ADASYN and SMOTE can positively impact fraud
detection accuracy by mitigating the challenges posed by
imbalanced datasets. The successful development of the
proposed method has extended the detection accuracy,
precision, recall and F-score of the methods compared to
other classical machine learning methods. Thus, this
enhances the effective fraud detection system for e-
commerce security and trustworthiness of the platform
protect users from fraudulent activities, reduce financial
losses, and preserve the platform's reputation.
Keywords :
Deep Neural Network; Feature Extraction; Machine Learning; Fraud Detection, ADASYN.
