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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63077
Volume:
10 |
5 |
May 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Master of Computer Application
Master of Computer Application
Master of Computer Application
Master of Computer Application
In the current digital economy, managing complex personal finances with multiple income streams and rising expenditures is a significant challenge. This paper presents the development of an AI Powered Personal Finance Intelligence Platform, an intelligent full-stack solution designed to automate expense tracking and provide predictive financial insights. Utilizing a modular architecture with a React-based frontend and a Node.js/MongoDB backend, the system integrates a machine learning engine to forecast future expenditures and generate personalized financial health recommendations. The AI model, employing Random Forest and Regression algorithms, achieved a high degree of reliability in trend-based estimation, facilitating a 97.4% accuracy in expense prediction during controlled testing. This platform serves as a critical screening tool for identifying overspending patterns and promoting disciplined saving habits..
KEYWORDS
The development of the AI Powered Personal Finance Intelligence Platform successfully bridges the gap between passive bookkeeping and active financial management. By integrating a Random Forest-based predictive engine into a responsive full-stack architecture, the system provides users with high-precision expense forecasts and actionable behavioral recommendations. The research confirms that AI-driven screening can significantly enhance individual financial discipline and long-term wealth stability.
1. D. Y. Shahare, "AI Powered Personal Finance Intelligence Platform," Thesis, Dept. of MCA, Karanjekar College of Engineering, 2026. 2. I. Sommerville, Software Engineering, 10th ed., Pearson Education, 2015. 3. T. Hastie et al., The Elements of Statistical Learning, Springer-Verlag, 2009. 4. IEEE Standard 1012-2012, "Standard for System, Software, and Hardware Verification and Validation." 5. M. Kantardzic, Data Mining: Concepts, Models, Methods, and Algorithms, Wiley-IEEE Press, 2011. 6. S. Karthik and J. Senthil Kumar, "A Machine Learning Approach for Predictable Personal Finance Management," 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2023, pp. 450-456. 7. A. R. Pathak, M. Mahajan, and A. Mahajan, "Deep Learning and Full-Stack Integration for Real-time Financial Analysis," International Journal of Computer Applications, vol. 184, no. 25, pp. 18-24, June 2022. 8. M. A. Kabir and S. Madria, "A Privacy-Preserving and Secure Full-Stack Framework for Personal Financial Data Management," IEEE Trans. Serv. Comput., 2022.
Publication Date
5/14/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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