Published on: April 2026
DONIFY: AN AI-POWERED CROWDFUNDING PLATFORM FOR EFFICIENT AND SECURE FUNDRAISING USING MACHINE LEARNING
Mohammad Kaif Ansari
Prof. Rahul D. Ingle , Prof. Rohan B. Kokate
Article Status
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Abstract
This paper presents the design, development, and evaluation of Donify, an AI-powered crowdfunding platform aimed at improving transparency, security, and efficiency in digital fundraising. Traditional crowdfunding platforms often suffer from issues such as fraudulent campaigns, lack of donor trust, and inefficient campaign creation processes. To address these challenges, the proposed system integrates Generative AI for automated campaign generation, machine learning-based fraud detection, and blockchain-inspired verification mechanisms to ensure authenticity. The platform is developed using modern web technologies, including Next.js for the frontend and serverless APIs for backend processing. Additionally, a dynamic UPI QR-based payment system ensures secure and traceable transactions. The system incorporates real-time monitoring to detect anomalies and prevent fraud. Experimental evaluation demonstrates improved campaign quality, reduced setup time, and enhanced user engagement. The proposed solution is scalable and provides a robust foundation for next-generation intelligent crowdfunding systems.
How to Cite this Paper
Ansari, M. K. (2026). Donify: An AI-Powered Crowdfunding Platform for Efficient and Secure Fundraising using Machine Learning. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.642
Ansari, Mohammad. "Donify: An AI-Powered Crowdfunding Platform for Efficient and Secure Fundraising using Machine Learning." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.642.
Ansari, Mohammad. "Donify: An AI-Powered Crowdfunding Platform for Efficient and Secure Fundraising using Machine Learning." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.642.
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Ethical Compliance & Review Process
- •All submissions are screened under plagiarism detection.
- •Review follows editorial policy.
- •Authors retain copyright.
- •Peer Review Type: Double-Blind Peer Review
- •Published on: Apr 23 2026
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.

