Smart Spend-A Student Finance Manager

• Author(s): Darshan Trivedi; Dr. Balaurugan S.
• Paper ID: 1718016
• Page: 2952-2958
• Published Date: 20-05-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 11 May-2026

Abstract

Rising digital transactions and widespread use of mobile wallets have increased the complexity of everyday spending, making personal finance management (PFM) a critical need for individuals—particularly students and young adults with irregular income. Manual budgeting is time-consuming and often fails to provide forward-looking insight, while many consumer apps emphasize charts without reliable forecasting. Researchers have therefore explored machine learning (ML) and AI techniques to automate expense categorization, model spending behaviour, and support budget recommendations through approaches ranging from classical classifiers to regression and sequence-oriented predictors, NLP-based tagging of transaction text, and assistant-style interfaces. Recent studies propose models—including probabilistic classifiers, tree-based predictors, neural sequence models, and hybrid application pipelines—to improve accuracy and reduce manual effort. Some works integrate ML modules with mobile or web systems to demonstrate end-to-end workflows from data ingestion to user-facing insights. Nevertheless, the available literature can be prone to small or sensitive datasets, computationally intensive models that are difficult to deploy on smartphones, or narrowly scoped prototypes evaluated without longitudinal user studies. Benchmarking is often inconsistent because tasks, features, and metrics differ across papers, and several contributions note limitations such as weak UI coverage, recommendation cold-start, or models that are not tailored to student-centric financial constraints. In this paper, the review of recent ML solutions for personal finance tasks—especially expense categorization and budget-related prediction—will be provided, and the main research gaps in the literature will be identified. The review synthesizes how classical ML, forecasting-oriented modeling, NLP, and assistant-oriented systems address PFM problems, and it discusses integration challenges that affect real-world adoption. The paper further outlines a methodology-oriented perspective for building an integrated PFM framework with transparent evaluation and privacy-aware data practices. The suggested framework is described and assessed in terms of standard task metrics such as classification performance for categorization (e.g., accuracy, precision, recall, F1-score) and error metrics for forecasting where applicable (e.g., MAE/RMSE), together with practical considerations such as inference latency on representative hardware. The discussion emphasizes reproducible splits, baseline comparisons, and explicit reporting of data limitations common in financial behaviour research.

Keywords

Machine Learning, Personal Finance Management, Expense Categorization, Budget Forecasting, Predictive Analytics, Natural Language Processing, Recommendation Systems, Mobile Applications, Data Privacy, Student Financial Behaviour

Citations

IRE Journals:
Darshan Trivedi, Dr. Balaurugan S. "Smart Spend-A Student Finance Manager" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 2952-2958 https://doi.org/10.64388/IREV9I11-1718016

IEEE:
Darshan Trivedi, Dr. Balaurugan S. "Smart Spend-A Student Finance Manager" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1718016