Autonomous AI Agents for Personalized Financial Negotiation in Consumer Banking

Abstract As Artificial Intelligence (AI) is rapidly integrated into consumer finance, existing systems remain mainly advisors or reactive. This paper proposes a paradigm change: the development and deployment of autonomous AI agents capable of interacting with financial products by individual consumers in real time with financial institutions. These agents take advantage of multi-agent reinforcement learning (MARL), personal preference modeling, and economic dialogue theory, such as loan conditions, credit card interest rates, insurance premium and membership fee to adapt results.Unlike the stable recommended engine or chatbot interface, these agents act as autonomous economic actors, represent the financial goals and obstacles of users, and dynamically bargains with institutional AI systems. Framework includes configurable user persona, real-time data feed, dialogue protocol and moral railing for autonomy and transparency.This research investigates both the technical viability of AI-to-AI interactions and the socio-regulatory implications in Finance. It examines strategic behavior in settings, trust models between humans and AI agents and legal accountability for autonomous decisions. Prototype simulation and demonstration benchmarking against traditional users has increased financial access, less interaction concern and the ability of democratic financial lens.