International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 04 | Apr 2026
p-ISSN: 2395-0072
www.irjet.net
OPTI-BUDGET ENGINE - AI QUERY-DRIVEN EXPENSE MANAGEMENT AND BUDGET MONITORING SYSTEM Dr. Vivek Jaladi1, Abdul Kareem Khan2, Md Khustar Ahmed3, Mohammed Afzanuddin4, Syed Shah Mustafa Hussaini5 1Professor & Head, Department of Computer Science & Engineering, Lingaraj Appa Engineering College, Bidar,
Karnataka, India
2,3,4,5BE Final Year, Department of Computer Science & Engineering, Lingaraj Appa Engineering College, Bidar,
Karnataka, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Personal expense management has become
intelligent automation, new opportunities have arisen to transform how users interact with financial systems. The Opti-Budget Engine is introduced as an AI query-driven expense management and budget monitoring system designed to simplify and modernize personal finance handling
increasingly complex due to the growth of digital transactions and diverse spending patterns. Conventional expense tracking systems depend on manual data entry and static interfaces, which often leads to incomplete records, low user engagement, and weak financial insight. This paper presents Opti‑Budget Engine, an AI query‑driven expense management and budget monitoring system designed to simplify and automate personal finance tracking. The system enables users to manage expenses through natural language interaction, allowing them to add, query, update, and delete financial records conversationally instead of filling traditional forms. An integrated AI agent interprets user intent, extracts key entities such as amount, category and date, and executes verified operations directly on the backend SQLite database, ensuring real‑time synchronization between data storage and visual analytics. The platform also offers dynamic dashboards, category‑wise and trend‑based insights, and professional PDF report generation to support informed financial decision‑making. Overall, Opti‑Budget Engine transforms conventional expense tracking into an intelligent financial assistance process by reducing user effort, improving record accuracy, and enhancing usability, providing a modern and user‑centric solution for personal finance management.
1.1 Background Survey The growing complexity of modern lifestyles has significantly increased the need for efficient personal finance management systems. With the rise of digital payments, online subscriptions, and frequent micro-transactions, individuals generate large volumes of financial data daily. Traditional expense management systems typically require users to input transaction details through structured forms, which is timeconsuming and prone to inconsistency. Recent advancements in Artificial Intelligence, particularly in natural language processing and intelligent agents, have introduced new possibilities for improving user interaction with software systems. However, in many existing finance-related applications, AI functionality is confined to providing suggestions without direct authority to modify the underlying financial database. This gap forms the foundational motivation for developing an intelligent, AI-driven expense management platform.
Key Words: Personal finance, Expense management, Budget monitoring, Conversational AI, Large language model, Flask, SQLite, Dashboard.
1.2 Problem Statement
1. INTRODUCTION
Despite the widespread availability of digital expense tracking applications, individuals continue to face significant challenges in effectively managing their personal finances. Existing systems largely depend on manual data entry, rigid input forms, and predefined categories, which require continuous user effort and discipline. This repetitive process often leads to delayed updates, incomplete records, and inaccurate categorization. Additionally, most conventional tools offer limited interaction mechanisms, restricting users to static dashboards and fixed query options. Users are unable to interact with their financial data in a natural manner using everyday language. Another critical limitation lies in the lack of real-time synchronization between user actions, data processing, and visual analytics. Therefore, the
In the contemporary digital era, personal financial management has emerged as a critical yet challenging task due to the increasing volume, diversity, and frequency of daily transactions. Individuals often struggle to maintain accurate expense records, analyze spending behavior, and derive meaningful insights from fragmented financial data. Conventional expense management applications largely depend on manual data entry and static categorization, which not only demands sustained user effort but also increases the likelihood of errors, inconsistencies, and incomplete records. With the rapid advancement of Artificial Intelligence, particularly in natural language processing and
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