Financial AI evolves through distinct models for different tasks
Evolution of Financial AI: From Statistical Models to Agentic AI
Computational Engineering, Finance, and Science
Summary
AI in finance has changed a lot over time, moving through different styles like simple statistics, machine learning, deep learning, and now autonomous AI agents. The paper explains that these types of AI solve different kinds of problems instead of being better or worse versions of each other. The authors provide guidance on which AI type suits particular financial tasks and caution that the newest and most complex AI isn’t always the best choice. They also point out challenges for creating trustworthy AI systems in finance.
What this means in practice
- •For quantitative analysts: Choose the AI approach best suited to the characteristics of a financial prediction or decision task, improving model effectiveness and resource use.
- •For financial technology teams: Design human-AI workflows in financial services with insight on where autonomous agents or retrieval-based methods add value versus traditional models.
A survey. It maps existing work.
Authors
Saba Asaad, Ali Bereyhi
Abstract
Artificial intelligence (AI) in finance has evolved from statistical learning and machine learning for structured prediction, through deep learning and foundation models for representation learning, language understanding, and reasoning, to retrieval-augmented generation for knowledge-grounded reasoning and agentic AI for autonomous planning and execution. In this paper, we examine how researchers and practitioners can select and apply AI methods in finance. We first show that the major paradigms of financial AI address fundamentally different classes of problems, rather than forming a strict hierarchy of capability. We then review the current state of research on AI in finance and trace how these paradigms have reshaped financial data, financial tasks, decision support, and human-AI collaboration. Building on this, we propose practical guidelines for matching AI paradigms to task characteristics, and show that more advanced models, such as foundation models or autonomous agents, are not universally superior. We conclude by outlining open challenges for researchers and practitioners working toward reliable financial AI.