THE EVOLUTION OF FINANCIAL ANALYSIS: FROM MANUAL METHODS TO AI AND AI AGENTS

Abstract

Purpose: This study examines the transformation of financial deci-

sion-making through the adoption of artificial intelligence, focusing

on the shift from conventional AI systems to AI agents and agentic AI.

It differentiates between automated analytical tools and autonomous,

goal-oriented systems that increasingly assume decision-making au-

thority within financial operations.

Design/Methodology/Approach: Employing a qualitative multi-meth-

od approach—comprising semi-structured expert interviews, industry

report synthesis, in-depth case studies, and a comparative performance

evaluation—this research investigates AI agent implementation

across SMEs, pharmaceutical analytics, and ERP-integrated corpo-

rate finance. Theoretically, it extends foundational models including

the Efficient Market Hypothesis (EMH), Behavioral Finance, and the

Adaptive Markets Hypothesis (AMH) by embedding the dynamic,

learning-driven nature of AI agents into financial decision logic.

Findings: The results indicate that AI agents introduce novel forms

of informational asymmetry, enhance bias mitigation through adaptive

modeling, and give rise to emergent decision structures via multi-agent

interactions. These dynamics challenge core assumptions of market ra-

tionality and static efficiency. Practically, the study offers a structured

framework for AI agent integration, emphasizing explainability, hy-

brid human-AI governance, and risk-specific safeguards to navigate

ethical and regulatory constraints. The proposed conceptual taxonomy

and cross-industry implementation roadmap reposition agentic AI as a

strategic transformation—reshaping how financial institutions process

data, execute judgments, and regulate algorithmic autonomy.