From Algorithms to Agents: Reframing FinTech Through Agentic Artificial Intelligence

Abstract

Financial technology (FinTech) has traditionally been driven by rule-based algorithms and predictive models that automate discrete tasks such as credit scoring, fraud detection, and portfolio optimization. However, recent advances in agentic artificial intelligence (AI)—systems capable of autonomous goal-setting, planning, reasoning, and adaptive decision-making—are reshaping the conceptual and operational foundations of FinTech. This paper reframes FinTech through the lens of agentic AI, arguing that the field is transitioning from static, tool-like algorithmic systems to dynamic, goal-directed agents capable of interacting with complex financial environments. We explore how agentic AI enables continuous learning, multi-step reasoning, contextual adaptation, and proactive financial decision support across domains including digital banking, decentralized finance (DeFi), risk management, regulatory compliance, and personalized wealth management. The paper proposes a conceptual framework distinguishing algorithmic automation from agentic autonomy, highlighting implications for governance, accountability, transparency, and human–AI collaboration. By shifting the focus from isolated predictive accuracy to adaptive financial agency, this work provides a foundation for understanding the next generation of intelligent financial systems and their transformative potential for institutions, regulators, and end users.