International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 11, Issue 10 (October 2024), Pages: 114-121

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 Technical Note

An enhanced AI-based model for financial fraud detection

 Author(s): 

 Ahmed H. Ali 1, *, Ahmed Ali Hagag 2

 Affiliation(s):

 1Department of Electrical Quantities Metrology, National Institute of Standards (NIS), Giza, Egypt
 2Ministry of Communication and Information Technology, Giza, Egypt

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0001-8141-750X

 Digital Object Identifier (DOI)

 https://doi.org/10.21833/ijaas.2024.10.013

 Abstract

The research seeks to identify complex fraudulent activities. Artificial intelligence (AI) techniques, such as machine learning and deep learning, have shown significant potential in enhancing the accuracy and efficiency of fraud detection models. This study introduces a novel AI-based fraud detection model that combines both supervised and unsupervised learning methods. The proposed machine learning system uses these techniques to detect fraudulent transactions. The supervised learning component is trained using a labeled dataset that includes both fraudulent and non-fraudulent transactions. The dataset used in the research contains 284,807 credit card transactions. After preparing the data, four Python-based models were developed. The K-Nearest Neighbors (KNN) model successfully predicted 99.94% of credit card transactions as valid or fraudulent. A random forest (RF) model was also used to assess the legitimacy of transactions, achieving an accuracy score of 99.96% correctly classifying nearly all data points. The Support Vector Machine (SVM) model achieved 99.94% accuracy, misclassifying only 51 cases. The logistic regression (LR) model attained an accuracy of 99.92% with 70 misclassifications and 99.91% with 77 misclassifications. These models demonstrate high accuracy and efficiency.

 © 2024 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Fraud detection, Machine learning, Supervised learning, Credit card transactions, Accuracy

 Article history

 Received 29 April 2024, Received in revised form 23 August 2024, Accepted 3 October 2024

 Acknowledgment

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Ali AH and Hagag AA (2024). An enhanced AI-based model for financial fraud detection. International Journal of Advanced and Applied Sciences, 11(10): 114-121

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 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 

 Tables

 Table 1 Table 2 

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