Journal of Advances in Developmental Research

E-ISSN: 0976-4844   •   Impact Factor: 9.71

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

Call for Paper Volume 16 Issue 1 January-June 2025 Submit your research before last 3 days of June to publish your research paper in the issue of January-June.
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

Artificial Intelligence in Fraud Detection: Strengthening Identity Data Security in Open Banking

Author(s) Vijay Kumar Soni
Country United States
Abstract Open banking allows banks to share information with third-party providers in an easy manner through secure channels that result in enhanced usability of financial services as well as innovations. Increased connectivity of the system creates more susceptibility to fraud activities and identity theft. The paper proposes an Artificial Intelligence (AI) detection system for protecting Open Banking transactions and protecting user identity information. Machine learning algorithms such as XGBoost and Random Forest empower the system to identify fraud accurately in real-time with high precision and recall scores. Data imbalance issue needs preprocessing via Synthetic Minority Over-sampling Technique (SMOTE) that improves model sensitivity. The system adopts Representational State Transfer (REST) Application Programming Interface via OAuth2 and Open Identity (OpenID) Connect for secure deployment and employs AES (Advanced Encryption Standard) and RSA (Rivest–Shamir–Adleman) encryption to secure sensitive user information. Multi-factor authentication and tokenization provide additional identity protection. Machine Learning Operations (MLOps) allows the system to undertake continuous learning and model retraining and adaptation for identifying new fraud patterns. Experimental results highlight the superior detection accuracy and AUC (Area under the Curve) of Extreme Gradient Boosting (XGBoost), which determines the effectiveness of the system in real-world financial settings.
Keywords Open Banking, Fraud Detection, Identity Protection, Artificial Intelligence, Machine Learning, Data Security, Random Forest, XGBoost, SMOTE, MLOps.
Published In Volume 16, Issue 1, January-June 2025
Published On 2025-05-08
Cite This Artificial Intelligence in Fraud Detection: Strengthening Identity Data Security in Open Banking - Vijay Kumar Soni - IJAIDR Volume 16, Issue 1, January-June 2025. DOI 10.71097/IJAIDR.v16.i1.1438
DOI https://doi.org/10.71097/IJAIDR.v16.i1.1438
Short DOI https://doi.org/g9mw5s
View / Download PDF File

Share this



CrossRef

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

IJAIDR DOI prefix is
10.71097/IJAIDR

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 15 Isu 2Cover Page Vol 15 Isu 1Cover Page Vol 14 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 IJAIDR
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@ijaidr.com