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

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 AIintegrated 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