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AI-Powered Detection of Financial Deception: Uncovering Credit Card Fraud

by Krati Lodha, Katib Showkat Zargar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 13
Year of Publication: 2025
Authors: Krati Lodha, Katib Showkat Zargar
10.5120/ijca2025925257

Krati Lodha, Katib Showkat Zargar . AI-Powered Detection of Financial Deception: Uncovering Credit Card Fraud. International Journal of Computer Applications. 187, 13 ( Jun 2025), 39-46. DOI=10.5120/ijca2025925257

@article{ 10.5120/ijca2025925257,
author = { Krati Lodha, Katib Showkat Zargar },
title = { AI-Powered Detection of Financial Deception: Uncovering Credit Card Fraud },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2025 },
volume = { 187 },
number = { 13 },
month = { Jun },
year = { 2025 },
issn = { 0975-8887 },
pages = { 39-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number13/ai-powered-detection-of-financial-deception-uncovering-credit-card-fraud/ },
doi = { 10.5120/ijca2025925257 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-06-21T01:57:02.460650+05:30
%A Krati Lodha
%A Katib Showkat Zargar
%T AI-Powered Detection of Financial Deception: Uncovering Credit Card Fraud
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 13
%P 39-46
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The surge in digital financial services has created new vulnerabilities to fraud, requiring advanced detection systems. Conventional fraud identification methods struggle with real-time processing, particularly when analyzing severely imbalanced datasets. This study introduces a multi-faceted AI framework combining tree-based boosting algorithms (LightGBM, XGBoost, CatBoost) with neural computation to improve fraud identification. Utilizing the creditcard.csv dataset containing 284,807 transactions where only 0.17% represents fraudulent activities, 1, 16 specialized techniques were implemented rebalancing approaches and parameter optimization to enhance detection performance. Results demonstrate that tree-based boosting approaches excel in precision metrics, lowering false alerts, while neural computation achieves superior sensitivity and discrimination capability 3, 4, 5. Specifically, XGBoost reached 88.17% precision with 97.25% area under curve, 4 CatBoost maintained balanced performance indicators, 5 and the neural architecture delivered 82.65% sensitivity with 97.95% discrimination capability 49. These outcomes illustrate how computational intelligence enhances financial security protocols, reducing unauthorized activities and minimizing institutional risk exposure.

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Index Terms

Computer Science
Information Sciences

Keywords

Transaction Fraud Detection Computational Intelligence Neural Computation LightGBM XGBoost CatBoost Imbalanced Learning Financial Cyber security

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