Article
Version 1
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Artificial Intelligence Techniques for Fraud Detection
Version 1
: Received: 14 December 2023 / Approved: 15 December 2023 / Online: 15 December 2023 (03:39:11 CET)
How to cite: Lai, G. Artificial Intelligence Techniques for Fraud Detection. Preprints 2023, 2023121115. https://doi.org/10.20944/preprints202312.1115.v1 Lai, G. Artificial Intelligence Techniques for Fraud Detection. Preprints 2023, 2023121115. https://doi.org/10.20944/preprints202312.1115.v1
Abstract
In the wake of increasing digital fraud, this paper introduces an innovative application of Artificial Intelligence (AI) in detecting fraudulent activities across finance, healthcare, and e-commerce sectors. It presents a detailed analysis of machine learning methodologies, specifically focusing on the advantages of supervised, unsupervised, and deep learning techniques. The paper addresses the challenges such as data imbalance, model interpretability, and ethical implications in AI-based fraud detection. It also discusses the necessity of high-quality datasets and advocates for the integration of traditional and advanced machine learning methods to enhance accuracy and adaptability in fraud identification. However, it acknowledges the limitations including computational demands and overfitting risks. The study underscores the importance of collaborative efforts between AI experts and industry professionals to develop ethical, efficient, and reliable AI solutions for fraud detection.
Keywords
Artificial Intelligence; Fraud Detection; Machine Learning; Deep Learning; Supervised learning
Subject
Social Sciences, Behavior Sciences
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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