AI-Powered Fraud Detection: A Comparative Study of Gradient Boosting Machines and Neural Networks

Yefei Wang

2024

Abstract

As digital economies expand and financial transactions become more commonplace online, the potential for fraud increases, creating significant challenges to the security of financial systems and affecting consumer trust on a global scale. This paper presents an investigation into the utilisation of sophisticated machine learning methodologies for the identification of fraudulent activity within financial transactions. In view of the increasing prevalence of digital financial activities, it is of the utmost importance to implement robust fraud detection measures in order to safeguard assets and maintain consumer trust. This study employs Gradient Boosting Machines (GBMs) and Neural Networks (NNs), with a particular focus on addressing the challenges associated with imbalanced datasets and model overfitting. The experimental results demonstrate the efficacy of GBMs and NNs in accurately identifying fraudulent transactions, significantly reducing false negatives while maintaining high precision. These findings contribute to the broader literature on fraud detection and machine learning applications, suggesting that such models are not only effective but crucial for the ongoing battle against financial fraud. Future research directions include refining these models to improve their accuracy further and developing capabilities for real-time fraud detection, which are vital for adapting to the rapidly evolving landscape of digital transactions.

Paper Citation

in Harvard Style

Wang Y. (2024). AI-Powered Fraud Detection: A Comparative Study of Gradient Boosting Machines and Neural Networks. In Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-754-2, SciTePress, pages 85-91. DOI: 10.5220/0013487900004619

in Bibtex Style

@conference{daml24,
author={Yefei Wang},
title={AI-Powered Fraud Detection: A Comparative Study of Gradient Boosting Machines and Neural Networks},
booktitle={Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2024},
pages={85-91},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013487900004619},
isbn={978-989-758-754-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - AI-Powered Fraud Detection: A Comparative Study of Gradient Boosting Machines and Neural Networks
SN - 978-989-758-754-2
AU - Wang Y.
PY - 2024
SP - 85
EP - 91
DO - 10.5220/0013487900004619
PB - SciTePress