Fiinora: An AI-Driven Financial Assistant for Personalized Budgeting and Intelligent Decision Support

Abstract

Financial mismanagement among young individuals, particularly students and early-career professionals, represents a significant and growing socioeconomic challenge. Despite the proliferation of personal finance applications, adoption remains low due to complexity, lack of personalization, and insufficient intelligent support. This paper presents Fiinora, a prototype AI-driven financial assistant designed to address these deficiencies through an agent-based modular architecture encompassing automated budgeting, intelligent expense categorization, real-time overspending alerts, personalized investment recommendations, and a what-if financial simulation engine. The system's design is grounded in empirical evidence derived from a primary survey of 91 respondents, predominantly students and young professionals in the 18–24 age bracket, which revealed that 72.5% manage finances entirely by memory and 40.7% struggle with saving. Survey results further indicate that 63.7% of respondents are willing to adopt AI-based financial tools, and 57.1% would pay for such a service under suitable conditions. Fiinora's architecture, implemented using Python (FastAPI) for the backend, React (PWA) for the frontend, and MySQL for structured data persistence, is evaluated as a feasibility prototype demonstrating measurable improvements in financial decision-making, budgeting discipline, and investment awareness. The paper also benchmarks Fiinora against leading applications including Mint, YNAB, ET Money, and Walnut, demonstrating superior personalization and intelligent feature coverage.

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