Copyright © 2026 Authors retain the copyright of this article. This article 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.

@article{169199,
        author = {Velagala Sri Surya Prakash Reddy and S.L.M.Akshay Ram and P.Nikhil and S.Dixit and Shilpa Sharma},
        title = {A Hybrid Multi-Model Approach for Credit Card Fraud Detection Using Diverse Datasets and Independent Feature Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {785-789},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169199},
        abstract = {In order to improve credit card fraud detection, this study presents a hybrid multi-model strategy that makes use of several datasets and analyzes independent aspects. Our approach integrates financial history, demographic data, and transaction data that have all been processed by specialist models. To increase accuracy and lower false positives, these individual predictions are combined using ensemble techniques. Findings from actual datasets demonstrate that the suggested method provides financial institutions with a scalable and reliable solution by accurately and successfully detecting fraud},
        keywords = {credit card fraud detection, hybrid model, ensemble learning, machine learning, multiple datasets, independent feature analysis, stacking, transaction data, financial behaviour, fraud prevention.},
        month = {November},
        }