Next-Gen Personal Finance Platform: A One-Tap AI-Based System for Financial Growth and Planning

Abstract

Personal finance management remains a significant challenge for individuals due to the complexity of tracking spending, planning budgets, and achieving savings goals simultaneously. This paper presents NextGen Finance, an AI-integrated personal finance management web application that leverages machine learning and reinforcement learning to assist users in making data-driven financial decisions. The system is built on a three-tier architecture comprising a Spring Boot backend, a MySQL relational database, and two independent Python-based AI microservices exposed via REST APIs. The first AI module employs a Random Forest Regressor trained on a large-scale financial dataset containing features such as income, age, number of dependents, occupation type, city tier, loan repayment obligations, and insurance expenses. Given these inputs, the model predicts personalized expenditure across nine categories, namely rent, groceries, transport, dining, entertainment, utilities, healthcare, education, and desired savings. The second AI module implements a Q-Learning reinforcement learning algorithm that dynamically allocates a user\'s monthly savings across multiple financial goals based on their priority levels and remaining target amounts. The platform also includes a manual expense tracker and budget planner, enabling end-to-end financial management within a single application. Experimental results demonstrate that the Random Forest model achieves high predictive accuracy, while the Q-Learning agent converges to near-optimal allocation strategies after 200 training episodes. The proposed system addresses key limitations of existing finance applications by combining predictive analytics and adaptive decision-making in a unified, user-friendly interface.

Introduction

The text presents NextGen Finance, an AI-powered personal finance platform designed to help individuals make smarter, personalized financial decisions through predictive modeling and reinforcement learning.

Problem Statement

Many people struggle with financial management due to:

• Rising living costs and debt
• Complex financial products
• Lack of personalized budgeting advice

Existing finance apps mainly track past expenses but do not provide predictive or goal-based guidance, forcing users to rely on intuition rather than data-driven insights.

Proposed Solution: NextGen Finance

NextGen Finance is a web-based system that integrates AI into personal finance management by combining:

1. Budget prediction using machine learning
2. Savings allocation using reinforcement learning
3. Traditional expense tracking and goal management tools

AI Components

1. Budget Prediction (Random Forest Regressor)

• Predicts how users should distribute monthly income across nine expense categories (rent, food, transport, savings, etc.).
• Uses user inputs such as income, age, occupation, dependents, city tier, loans, and insurance.
• Trained on real financial datasets.
• Handles multi-output regression to generate a full budget plan.
• Adjusts predictions based on lifestyle constraints (e.g., no transport or rent).

2. Goal Allocation (Q-Learning)

• Uses reinforcement learning to distribute monthly savings across multiple financial goals.
• Learns optimal allocation strategies based on:
  • Goal priority
  • Remaining amount
  • Progress toward completion
• Rewards progress toward high-priority and nearly completed goals.
• Produces adaptive savings plans that improve over time.

System Architecture

The platform uses a 3-tier architecture:

• Frontend: JSP + Bootstrap for user interface
• Backend: Spring Boot with REST APIs and MySQL database
• AI Layer: Two independent Flask microservices:
  • Port 5000: Budget prediction model
  • Port 5001: Goal allocation model

Communication between layers is handled via REST APIs, ensuring modular and scalable design.

Methodology

Data and Features

• Budget model uses features like income, age, occupation, loans, and insurance.
• Outputs nine expense categories.
• Data is preprocessed with encoding for categorical variables.

Model Training

• Random Forest Regressor with 100 trees used for budget prediction.
• Evaluated using RMSE and R² score.
• Model saved as a reusable .pkl file.

Reinforcement Learning Model

• Q-Learning agent with:
  • 100 states and 100 actions
  • Epsilon-greedy exploration
  • Reward system based on goal progress and priority
• Trained over 200 episodes and stored as a Q-table.

API Design

• /predict (Flask 5000): Returns personalized budget breakdown.
• /suggest (Flask 5001): Returns optimal allocation of savings toward goals.

Conclusion

This paper presented NextGen Finance, an AI-integrated personal finance management web application that combines supervised machine learning and reinforcement learning to deliver personalized financial guidance to users. The system addresses a well-established gap in existing finance applications by moving beyond passive expense recording toward active, data-driven financial planning. The Random Forest Regressor successfully learns complex, non-linear relationships between a user\'s demographic and financial profile and their expected expenditure across nine spending categories. The multi-output regression approach ensures that a complete personalized budget breakdown is generated in a single model inference, making the system efficient and practical for real-time web use. The additional lifestyle flag mechanism further refines predictions to reflect the user\'s actual circumstances, enhancing the practical relevance of the recommendations. The Q-Learning reinforcement learning agent addresses the problem of optimal savings allocation across multiple financial goals. By formulating the allocation problem as a Markov Decision Process with a priority-sensitive reward function, the agent converges to allocation strategies that respect goal priorities, prevent goal neglect, and adapt dynamically to the user\'s current progress across all active goals. The three-tier architecture combining Spring Boot, MySQL, and JSP provides a stable and maintainable foundation for the application, while the microservice deployment of the two AI models as independent Flask APIs ensures clear separation of concerns and allows each component to be updated or retrained independently. The use of Spring RestTemplate for inter-service communication enables seamless integration between the Java backend and the Python AI layer without tightly coupling the two technology stacks. The platform unifies four previously separate concerns, namely AI-powered budget prediction, manual expense tracking, budget planning, and intelligent goal allocation, into a single cohesive application accessible through a standard web browser. This integration eliminates the need for users to switch between multiple tools and provides a consistent, session-aware experience throughout. Future work could explore several enhancements to the current system. The Q-Learning model could be extended to a deep reinforcement learning architecture to handle larger goal portfolios with richer state representations. The budget prediction model could incorporate time-series data to account for seasonal spending patterns. User authentication could be strengthened by replacing session-based login with JWT-based stateless authentication. Additionally, a data visualization layer presenting interactive charts of spending trends, goal progress, and budget utilization would significantly improve the user experience and the interpretability of the AI recommendations.

References

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Copyright

Copyright © 2026 Om Shahaji Shinde, Akshay Sarlal Pawar, Harsh Babusaheb Sawant, Bhushan Babulal Purkar, Anuradha Deokar. 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.