Fino_X_Change: An advanced peer to peer lending platform using Machine Learning | IJCT Volume 13 – Issue 2 | IJCT-V13I2P118
International Journal of Computer Techniques
ISSN 2394-2231
Author
Mr. Ankit Kumar Pathak, Dr. Rajeev Kumar
Abstract
Conventional banking often leaves out students and freelancers because of strict credit scoring practices. Fino_X_Change tackles these challenges through a Peer-to- Peer (P2P) lending platform that promotes clear, algorithm-driven interactions between lenders and borrowers. This research outlines a comprehensive architecture utilizing React.js, Node.js, and MySQL to maintain transactional accuracy. A significant innovation is a Python-driven Machine Learning engine that assesses borrower reliability using alternative data sources. By integrating Razorpay for automated fund distribution and EMI collection, the platform minimizes operational hurdles. This adaptable marketplace makes credit access more equitable while providing lenders with data-informed investment options.
Keywords
P2P Lending Platform, Conventional Banking, Credit Scoring, Algorithm-driven Interactions, React.js, Node.js, MySQL, Transactional Accuracy, Python-driven Machine Learning, Razorpay Integration, EMI Collection, Credit Access
Conclusion
The development of the Fino_X_Change platform successfully demonstrates that a technology-driven, Peer-to-Peer (P2P) lending model is a viable and transparent alternative to traditional banking. By integrating a modern full-stack architecture utilizing React.js for a dynamic user experience, Node.js for high-performance server logic, and MySQL for transactional integrity the project has established a secure marketplace for credit. The primary achievement of this research lies in its ability to bridge the “information asymmetry” gap through automated Machine Learning-based risk assessment and a verifiable peer-rating system.
References
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How to Cite This Paper
Mr. Ankit Kumar Pathak, Dr. Rajeev Kumar (2026). Fino_X_Change: An advanced peer to peer lending platform using Machine Learning. International Journal of Computer Techniques, 13(2). ISSN: 2394-2231.
