(untitled) (The International Journal of Engineering & Information Technology (IJEIT), 2026)
The growth of electronic transactions has increased the need for intelligent methods to analyze personal financial data. Most expense management tools rely on manual categorization and basic visualization, providing limited analytical value. This study proposes a web-based intelligent system for automatic classification of personal expenses from unstructured transaction descriptions using natural language processing and machine learning. The system employs text preprocessing, semantic sentence embeddings, and supervised classification with XGBoost. Experimental results show that semantic representations significantly outperform TF-IDF-based approaches, achieving an accuracy of 93.6% on short financial transaction text. The proposed system supports accurate expense categorization at scale and enables aggregated spending analysis for business intelligence and sector-level economic insight.