AI-Enabled NLP Framework for Automated Expense Management and Financial Analysis
DOI:
https://doi.org/10.15662/IJEETR.2026.0802073Keywords:
Natural Language Processing, Expense Management, Financial Analysis, Transformer Architecture, UPI Transactions, SMS Parsing, Anomaly Detection, Personal Finance, Explainable AI.Abstract
In today's digital economy, the increasing dependence on online payments, UPI transactions, and electronic banking has made personal finance management both essential and complex. Individuals face significant challenges in tracking multiple transactions across diverse financial platforms, leading to disorganized spending habits and poor financial awareness. This paper presents a comprehensive AI-enabled Natural Language Processing framework that automates the tracking, categorization, and analysis of personal financial activities. The proposed system employs advanced NLP techniques to read and interpret transactional messages received on mobile devices, including SMS alerts and notifications from banks, UPI gateways, and credit card providers. The NLP engine identifies transaction-related data such as account numbers, payment amounts, merchant details, and timestamps, verifying them against pre-linked financial accounts to ensure authenticity and prevent erroneous classification. We introduce a novel hybrid architecture combining transformer-based language models with rule-based verification systems to achieve 96.8% accuracy in transaction extraction and 94.3% precision in merchant identification. The framework incorporates a multi-layered security protocol that detects fraudulent patterns and flags suspicious transactions with 91.7% sensitivity. Additionally, the system provides personalized financial insights through interactive dashboards, enabling users to examine spending patterns, evaluate budget utilization, and set financial goals. Experimental evaluation using real-world transaction datasets demonstrates that our approach reduces manual effort by 85.6% while improving financial awareness through actionable intelligence. This work contributes a production-ready framework that represents a novel integration of Artificial Intelligence, Natural Language Processing, and Data Analytics for intelligent, secure, and user-centric personal finance management
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