An Intelligent System for Semantic Classification of Personal Expenses from Transaction Descriptions
DOI:
https://doi.org/10.36602/ijeit.v14i2.609Keywords:
Transaction classification, AI model, Financial Data Categorization, Accuracy, Personal Expenses, Business PlanningAbstract
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.
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[1] Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785
[2] Chen, J., Li, X., & Zhao, Y. (2024). Deep learning models for intelligent financial text classification. IEEE Access, 12, 45678–45690. https://doi.org/10.1109/ACCESS.2024.3365123
[3] Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357. https://doi.org/10.1613/jair.953
[4] Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
[5] Fernández, A., García, S., Herrera, F., & Chawla, N. V. (2018). SMOTE for learning from imbalanced data: Progress and challenges. Pattern Recognition, 61, 363–378. https://doi.org/10.1016/j.patcog.2016.08.003
[6] García-Méndez, S., López-García, P., & Ruiz-Martínez, A. (2024). Natural language processing techniques for bank transaction categorization. Knowledge-Based Systems, 292, 111671. https://doi.org/10.1016/j.knosys.2024.111671
[7] He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263–1284. https://doi.org/10.1109/TKDE.2008.239
[8] He, X., Zhang, J., & Wang, M. (2021). Learning user spending behavior for intelligent financial recommendation systems. Information Sciences, 570, 84–99. https://doi.org/10.1016/j.ins.2021.04.056
[9] Jeong, S., Kim, H., & Park, J. (2025). Transformer-based models for financial transaction understanding. ACM Transactions on Information Systems, 43(1), Article 7. https://doi.org/10.1145/3634129
[10] Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. Proceedings of the European Conference on Machine Learning (ECML), 137–142.
[11] Jørgensen, R. K. (2021). Machine learning for financial transaction classification using character-level word embeddings. Information Systems Audit and Control Journal, 6(2).
[12] Lei, Z., Zhang, H., & Chen, L. (2025). Semantic text representation for short financial transaction classification. Information Processing & Management, 62(1), 103109. https://doi.org/10.1016/j.ipm.2024.103109
[13] Maree, R., Fourie, J., & Eloff, J. H. P. (2020). Automated financial transaction classification using machine learning. South African Journal of Information Management, 22(1), a1196. https://doi.org/10.4102/sajim.v22i1.1196
[14] McCallum, A., & Nigam, K. (1998). A comparison of event models for Naive Bayes text classification. AAAI Workshop on Learning for Text Categorization.
[15] Oyewole, O. O., Adekunle, A. A., & Salami, R. A. (2024). Machine learning approaches for automated expense classification in financial systems. Expert Systems with Applications, 235, 121065. https://doi.org/10.1016/j.eswa.2023.121065
[16] Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence embeddings using Siamese BERT-networks. Proceedings of EMNLP-IJCNLP, 3982–3992. https://doi.org/10.18653/v1/D19-1410
[17] Ta, V. T., Liu, C., & Zhang, J. (2018). FinTech and banking: A systematic review and future research directions. International Journal of Financial Studies, 6(2), 53. https://doi.org/10.3390/ijfs6020053
[18] Talasila, S. (2024). AI-driven personal finance management: Revolutionizing budgeting and financial planning. International Journal of Advanced Research in Computer Science and Technology, 12(1), 45–54.
[19] Toran, L., et al. (2023). Scalable and weakly supervised bank transaction categorization using NLP. arXiv preprint arXiv:2305.18430.
[20] Wei, J., & Zou, K. (2019). EDA: Easy data augmentation techniques for boosting performance on text classification tasks. Proceedings of EMNLP-IJCNLP, 6383–6389.
[21] Wang, Y. (2024). Artificial intelligence techniques for personal financial management: Trends, challenges, and opportunities. Journal of Financial Technology, 8(1), 1–15. https://doi.org/10.1016/j.jft.2024.01.003
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