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.
Khan et al. (2026) studied this question.