International Journal on Science and Technology

E-ISSN: 2229-7677   •   Impact Factor: 9.88

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 16 Issue 1 January-March 2025 Submit your research before last 3 days of March to publish your research paper in the issue of January-March.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 16 Issue 1
2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

AI-Powered Fraud Detection in Financial Transactions

Author(s) Sandeep Yadav
Country United States
Abstract The growing sophistication of financial fraud poses significant challenges to traditional detection systems, which often fail to adapt to evolving patterns of fraudulent activity. This research explores the use of artificial intelligence (AI) to enhance fraud detection in financial transactions. By leveraging advanced machine learning models, including supervised, unsupervised, and deep learning techniques, the proposed framework offers a scalable and adaptive approach to identifying anomalies in real-time.
Key components of the framework include dynamic feature engineering, ensemble modeling, and the integration of explainable AI (XAI) to ensure transparency and regulatory compliance. The study evaluates the performance of various algorithms, such as Random Forests, Gradient Boosting Machines, and Autoencoders, on publicly available and proprietary transaction datasets. Results demonstrate significant improvements in detection accuracy, reduced false positives, and enhanced efficiency compared to traditional rule-based systems.
This research highlights the transformative potential of AI in fraud prevention, providing actionable insights for financial institutions to strengthen operational resilience and customer trust. By addressing challenges such as data imbalance and adversarial fraud techniques, the study offers a robust, real-time fraud detection system that meets the demands of modern financial ecosystems.
Keywords Fraud Detection, Artificial Intelligence (AI), Financial Transactions, Machine Learning, Anomaly Detection, Explainable AI (XAI), Supervised Learning, Unsupervised Learning, Deep Learning, Real-Time Fraud Detection, Adversarial Fraud Techniques
Published In Volume 14, Issue 4, October-December 2023
Published On 2023-11-07
Cite This AI-Powered Fraud Detection in Financial Transactions - Sandeep Yadav - IJSAT Volume 14, Issue 4, October-December 2023. DOI 10.5281/zenodo.14514160
DOI https://doi.org/10.5281/zenodo.14514160
Short DOI https://doi.org/g8wcgj
View / Download PDF File

Share this



CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJSAT DOI prefix is
10.71097/IJSAT

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 15 Isu 4Cover Page Vol 15 Isu 3Cover Page Vol 15 Isu 2

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJSAT
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us Message on WhatsApp +91-9687-182-185 editor@ijsat.org