A Smart AI-Based Personal Finance Assistant: A Multi-Model Approach for Expense Categorization, Budgeting, Forecasting and Anomaly Detection

Abstract

In the digital era, effective personal finance management has become increasingly complex due to the high volume of financial transactions and unstructured spending data. Traditional finance management tools rely on manual categorization and static budgeting, often leading to inefficient financial planning. This paper presents a Smart Personal Finance Assistant, an AI-driven system that integrates four key financial automation modules: BERT-based expense categorization, reinforcement learning for budget recommendation, LSTM for savings forecasting, and Isolation Forest for anomaly detection. The system is designed to improve personal financial decision-making through intelligent automation and adaptive learning. The research focuses on optimizing model training, handling class imbalances in transaction data, and ensuring seamless integration of AI models into a user-friendly interface. Extensive experimental analysis demonstrates the effectiveness of the proposed system, with improvements in expense classification accuracy, personalized budgeting efficiency, and anomaly detection precision. The findings indicate that AI-driven financial management can significantly enhance user financial literacy and decision-making.

Introduction

Managing personal finances is increasingly complex due to digital transactions. Traditional tools are manual, error-prone, and non-personalized. This project proposes an AI-driven Smart Personal Finance Assistant to automate financial tasks, adapt to user behavior, and provide intelligent insights.

Motivation:
Personal finance management is time-consuming and often leads to inefficient budgeting and savings. Existing tools lack AI-based automation and personalization. Integrating NLP, reinforcement learning, time-series forecasting, and anomaly detection can enhance financial decision-making.

Objectives:

1. Expense Categorization: Use BERT-based NLP to classify transactions into 50 categories.

2. Budget Recommendation: Apply Q-learning reinforcement learning for dynamic, personalized budgets.

3. Savings Forecasting: Employ LSTM for predicting future savings trends.

4. Anomaly Detection: Use Isolation Forest to flag unusual or fraudulent transactions.

Scope:
Targeted at individuals, households, and smart city/building applications needing automated financial insights.

Literature Review & Contributions:

• Expense Categorization: Fine-tuned BERT with improved preprocessing and SMOTE for class balancing.

• Budget Recommendation: Q-learning-based dynamic budget adaptation.

• Savings Forecasting: Feature-engineered LSTM model with real-time prediction capabilities.

• Anomaly Detection: Fine-tuned Isolation Forest reducing false positives and incorporating financial ratios.

System Architecture:

• Modules: Expense Categorization (BERT), Budget Recommendation (RL), Savings Forecasting (LSTM), Anomaly Detection (Isolation Forest).

• Workflow: User inputs → data preprocessing → modules execute → insights delivered.

• Integration: Python backend with Streamlit UI; supports real-time processing and secure data handling.

Methodology:

• Expense Categorization: 50-category BERT model, SMOTE for class imbalance, cross-entropy loss, evaluated via accuracy, precision, recall, F1-score.

• Budget Recommendation: Q-learning with ε-greedy policy, state-action-reward modeling; performance measured by Q-value convergence and reward improvement.

• Savings Forecasting: LSTM with 12-month sequences, feature engineering, min-max scaling; R² = 0.63.

• Anomaly Detection: Isolation Forest with engineered features (savings and expense ratios), 300 estimators, contamination 0.015; metrics: precision 0.93, recall 0.94, F1-score 0.94, R² = 0.87.

Key Contributions:
The system provides automated, personalized, and adaptive financial management while improving prediction accuracy and anomaly detection efficiency compared to traditional methods.

Conclusion

This research presents a Smart AI-Based Personal Finance Assistant, an integrated system that automates expense categorization, budget recommendations, savings forecasting, and anomaly detection using advanced machine learning techniques. The key findings of this study are: BERT-based expense categorization effectively classifies transactions with high accuracy, outperforming traditional NLP models. Reinforcement learning-based budgeting provides adaptive financial planning, dynamically adjusting to spending habits. LSTM-based savings forecasting improves prediction accuracy compared to traditional time-series models. Isolation Forest-based anomaly detection enhances financial security by flagging unusual transactions with optimized precision. The results indicate that AI-driven financial management can significantly improve financial decision-making, automate complex financial tasks, and enhance fraud detection capabilities. This system can be a scalable and intelligent solution for individuals, households, and smart financial ecosystems.

References

[1] Dwivedi, Y. K., Hughes, D. L., & Ismagilova, E. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on financial forecasting. International Journal of Information Management, 56, 1542-1549. [2] Sezer, O. B., Gudelek, M. U., & Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning. Applied Soft Computing Journal, 89, 960-966. [3] Li, X., & Wang, T. (2019). Machine learning approaches for high-frequency trading. Journal of Financial Economics, 5(4), 830-839. [4] Zhang, T., & Liu, Q. (2018). AI applications in credit scoring and risk assessment: A survey. Expert Systems with Applications, 92, 725-735. [5] Chen, J., & Zhang, L. (2017). Predicting stock returns using neural networks. Journal of Banking & Finance, 81, 690-699. [6] Kumar, S., & Lee, H. (2016). Financial sentiment analysis with natural language processing. Financial Studies Review, 24(3), 680-689. [7] Miller, H. (2015). Portfolio management using AI techniques. Applied Economics, 77, 670-675. [8] Garcia, R., & Sun, Y. (2014). Algorithmic trading and AI integration in finance. Journal of Financial Markets, 33, 620-626. [9] Tanaka, M. (2013). Risk assessment models in finance using machine learning. Journal of Computational Finance, 18, 590-598. [10] Chen, Z. (2012). Predictive analytics in finance with machine learning. Journal of Applied Economics, 52, 560-568. [11] Xu, L., & Lee, C. (2011). Credit risk prediction with advanced machine learning models. Expert Systems with Applications, 85, 550-556. [12] Lee, H. (2010). Text-based predictive models for stock market forecasting. Financial Research Letters, 28(1), 530-535. [13] Wang, J. (2009). AI-based risk management systems for banking. Journal of Risk Management, 43(2), 520-526. [14] Sharma, P. (2008). Deep learning for financial fraud detection. International Journal of Financial Studies, 15, 510-515. [15] Liu, Y., & Patel, R. (2007). Big data analytics in finance. Journal of Financial Data Science, 11(3), 500-506. [16] Chen, A. (2005). Economic forecasting with neural networks. Journal of Economic Modeling, 19(4), 480-485. [17] Zhang, Y., & Liu, S. (2004). AI-driven investment strategies. International Journal of Economics and Finance, 14, 470-474. [18] Sun, Q. (2003). Portfolio optimization through machine learning. Journal of Banking Research, 25(2), 460-465. [19] Yoon, J. (2002). Detecting market anomalies with AI. Journal of Financial Technology, 9(2), 450-455. [20] Dwivedi, Y. K., & Williams, M. D. (2021). Bibliometric analysis of AI applications in finance. Journal of Information Systems, 34(3), 1520-1535. [21] Nguyen, C., & Tran, T. (2019). AI in financial regulation and compliance. Computational Finance, 26(5), 250-258. [22] Singh, R., & Kumar, A. (2020). Data mining for predictive insights in finance. Journal of Data Science and Finance, 18(4), 220-229. [23] Zhang, L., & Li, J. (2018). Ethics and transparency in AI-driven financial decisions. Ethics in Information Technology, 20(1), 32-40. [24] Liu, J., & Chen, Y. (2017). Natural language processing in financial risk management. Journal of Financial Analytics, 12(3), 90-97. [25] Chatterjee, S., & Sengupta, R. (2015). Machine learning algorithms in stock price forecasting. Financial Markets Analysis, 21, 250-260. [26] Wang, X., & Li, Z. (2021). AI applications in financial technology. International Journal of Finance and Economics, 28, 522-530. [27] Kumar, A. (2019). Advanced AI applications in portfolio optimization. Journal of Computational Intelligence in Finance, 24(2), 162-170. [28] Jiang, S., & Zhao, L. (2018). Deep learning for AI-enhanced financial insights. Machine Learning in Finance, 16(4), 170-178. [29] Qiu, X., & Yin, X. (2020). Bibliometric analysis of AI and fintech trends. Journal of Financial Studies and Technology, 14(3), 210-219. [30] Zhang, H., & Feng, Y. (2019). Co-word analysis for AI themes in finance. Journal of Applied Bibliometrics, 25(1), 50-59.

Copyright

Copyright © 2025 Reena Kharat, Siddhi Solapurkar, Akul Tikkas, Mrunay Uttarwar, Akshay Yadav . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.