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AI Agent Use Case: Personalized Investment Advice

Publisher IDC
Published Sep 23, 2025
Length 8 Pages
SKU # IDC20427173

Description

This IDC Perspective presents a structured, step-by-step technical overview of the agentic AI process in personalized financial advice provision. Agentic AI represents a step change in the delivery of financial advice, moving beyond static, rules-based robo-advisors to systems capable of reasoning, learning, and adapting autonomously. By integrating structured financial data, unstructured market intelligence, and user-specific preferences, agentic AI can provide hyper-personalized and continuously updated recommendations that scale across client segments. The process framework outlined in this document illustrates how data ingestion, model training, compliance, and user interaction converge into an intelligent advisory cycle. For wealth managers, this promises operational efficiency, expanded client reach, and democratized access to sophisticated advice. At the same time, it introduces new challenges around trust, explainability, and regulatory oversight. "Agentic AI delivers economical, compliant personalization at scale," says Thomas Shuster, research director for IDC Financial Insights. "Advisory will move away from periodic check-ins toward real-time, adaptive, up-to-date personalization."

Table of Contents

8 Pages

Executive Snapshot

Situation Overview

What AI Agents Are and What They're Not

Agentic AI

AI Agent Use Case: Personalized Financial Advice

Agentic AI Process in Personalized Financial Advice: A Step-by-Step Technical Overview

Data Ingestion and Preprocessing

Feature Engineering and Risk Profiling

Model Selection and Training

Autonomous Decision-Making and Execution

Continuous Learning and Feedback Loops

Explainability and Compliance Monitoring

User Interaction and Personalized Advice Delivery

Key Benefits of Agentic AI in Financial Advice

Key Challenges of Agentic AI in Financial Advice

Advice for the Technology Buyer

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