FinIntel: An AI-Driven Predictive Ecosystem for Expense Forecasting and Investment Optimization

Abstract

Effective financial planning and expense management are critical for ensuring long-term fiscal sustainability. Traditional manual record-keeping and reactive budgeting often fail to provide the predictive insights necessary for proactive asset allocation. This study presents FinIntel, an integrated financial intelligence framework that leverages deep learning and machine learning to automate expense forecasting and behavioral analysis. The system utilizes Long Short-Term Memory (LSTM) networks, specifically optimized for time-series data, to predict future expenditure patterns based on historical transaction records. Model performance is rigorously validated using Coefficient of Determination (R2) and Mean Squared Error (MSE) metrics. Complementing the predictive engine, K-Means clustering is applied to categorize spending behaviors, while VADER sentiment analysis evaluates user feedback to refine recommendation accuracy. Developed on a Django-based web architecture with a MySQL backend, the framework provides a secure, OTP-authenticated environment for real-time financial monitoring. Experimental results indicate that the hybrid LSTMclustering approach significantly enhances forecasting precision compared to conventional linear models. By synthesizing predictive modeling with personalized investment heuristics, FinIntel offers a scalable solution for data-driven personal and institutional financial management.