AI-BASEDPERSONAL FINANCE MANAGER (AI)

Abstract

The rapid growth of digital financial services has resulted in an overwhelming increase in the volume, variety, and complexity of personal financial data. Users frequently struggle to monitor spending patterns, manage budgets, forecast savings, and make informed financial decisions due to a lack of time, financial literacy, or analytical tools. To address these challenges, Artificial Intelligence (AI) provides an effective solution by enabling automated, personalized, and data-driven financial management. This paper presents the design, development, and evaluation of an AI-Based Personal Finance Manager capable of automating expense classification, budget planning, financial forecasting, savings optimization, bill reminders, and anomaly detection for unusual transactions. The proposed system integrates supervised machine learning, deep learning models, and rule-based intelligence to deliver real-time financial insights tailored to individual user profiles. The system architecture consists of four primary modules: data acquisition, AIdriven analytics, prediction and recommendation engine, and user interface layer. Users upload or link financial transaction data, which is then cleaned, tokenized, and classified using machine learning models such as Random Forest, Gradient Boosting Machines, or LSTMbased sequence models for long-term financial pattern recognition. The recommendation engine generates suggestions related to saving opportunities, budget adherence, expense reduction, investment options, and goal-based financial planning. The system also incorporates reinforcement learning to refine recommendations based on user behavior and historical response patterns, ensuring continuous improvement. Experimental evaluation demonstrates that the model achieves high accuracy in expense categorization (93–96%), outperforming conventional rule-based systems. Financial forecasting using LSTM models also shows promising accuracy in predicting monthly expenditures, recurring payments, and potential savings. Usability studies indicate that users strongly benefit from automated financial planning features, especially real-time alerts and personalized budget recommendations. The integration of explainable AI techniques enhances transparency by providing justifications for financial suggestions, thereby improving user trust. Overall, the proposed AI-powered personal finance management system reduces manual effort, enhances financial literacy, and supports informed decisionmaking. It demonstrates that AI can transform traditional financial tracking into a personalized, intelligent, and proactive financial assistant