Enhancing Personal Finance Management Using AIBased Predictive Analytics And Intelligent Chatbot System

Authors: Anurag Mall, Nitin, Kirti Rastogi, Arvind Kumar

Abstract: The rapid growth of digital financial transactions, including UPI, online payments, and subscription-based services, has significantly increased the complexity of personal finance management. Despite the availability of various financial tracking applications, most systems lack intelligent analytics, predictive capabilities, and personalized guidance. This paper presents an AI-driven platform for enhancing Optimizing personal financial management using predictive analytics and an intelligent chatbot system. The proposed system integrates deterministic data handled using evaluation methods based on large language models to produce a multi-dimensional financial insight score. User financial data is processed through a structured pipeline, where expenses are categorized using machine learning algorithms, and spending behaviour is analysed using time-series predictive models. The system leverages a Natural Language Processing (NLP)-based chatbot that Allows users to query financial insights in instantly. A composite financial health score is computed using a weighted model: 𝑭𝒔𝒄𝒐𝒓𝒆 = ⌊𝟎. 𝟑𝟎𝑬 + 𝟎. 𝟐𝟓𝑷 + 𝟎. 𝟏𝟓𝑩 + 𝟎. 𝟏𝟎(𝑺𝑨 + 𝑭𝑨 + 𝑹𝑨)⌉ where 𝑬represents expense categorization accuracy, 𝑷denotes predictive accuracy, 𝑩indicates budgeting efficiency, 𝑺𝑨is savings analysis, 𝑭𝑨is financial awareness, and 𝑹𝑨is risk assessment. Experimental evaluation conducted on 100+ user datasets demonstrates a strong correlation (𝒓 = 𝟎. 𝟗𝟏) with manual financial assessments and an average improvement of 27.5% in user savings behavior after applying AI-generated recommendations. The system maintains secure data handling, real-time insights, and scalability. The proposed platform democratizes intelligent financial planning tools, making them accessible to a wider population.