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FutureCentral > AI in Wealth and Asset Management: The Guide for Investment Professionals
AI in Wealth and Asset Management book cover
Finance, Investment & Risk · Working Professionals

AI in Wealth and Asset Management

The Guide for Investment Professionals

Austin PM · FutureCentral Press

AI for investment professionals in wealth and asset management.

Working ProfessionalsManuscript chapters available
Explore the bookInquire about this bookView contents
AudienceWorking Professionals
Structure29 chapters listed
StatusManuscript chapters available

About the book

Investment research, advisory services, portfolio decisions, and market operations are changing with AI. The book examines those changes alongside brokerage, infrastructure, and digital assets, with attention to model risk, agentic workflows, and investor protection.

What you will learn

  • Evaluate investment management AI applications
  • Connect research and portfolio workflows with data
  • Assess agentic investment operations
  • Design model-risk and investor-protection controls
Read before you decide

Explore the book

Chapter overview

Inside Chapter 1

Chapter topics, summarized from “The Rise of AI in Wealth and Asset Management”:

  • Trace changes from dematerialization and electronic trading to investment platforms and AI-native management.
  • Explain how earlier infrastructure affects current investment workflows.
  • Assess how brokerage and wealth-technology platforms changed the work of investment professionals.

Preview and inspection

Ask about a sample chapter or an academic inspection copy. The Press will confirm availability and access.

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Frameworks and decision tools

The Three-Wave Architecture of AI in Indian Investment Management

Introduced in Chapter 1.

The Investment-Management Model Risk Pyramid

Introduced in Chapter 2.

The Investment Data Fabric — five layers and their owners

Introduced in Chapter 3.

The AI-Investment Thesis Coherence Test

Introduced in Chapter 4.

The AMC AI Operating Stack

Introduced in Chapter 5.

The Wealth Advisory AI Stack

Introduced in Chapter 6.

Table of contents

View all 29 chapters

Contents reflect available source documents. Final publication details remain unconfirmed.

  1. The Rise of AI in Wealth and Asset Management
  2. Machine Learning and Deep Learning in Investment Management
  3. Data, Alternative Data, and the AI Infrastructure of Investment Management
  4. The Strategic Logic of AI Adoption in Investment Management
  5. AI in Asset Management — Mutual Funds, AIFs, and Institutional Portfolios
  6. AI in Wealth Management and Private Banking — HNI and UHNI Advisory
  7. AI in Retail Brokerage and the WealthTech Platform Layer
  8. AI in Retail Algo Trading — the SEBI algo trading framework
  9. AI in Institutional Quant and High-Frequency Trading
  10. AI in Equity Research and Analyst Transformation
  11. AI in Capital Markets Infrastructure — Exchanges, Depositories, Clearing, RTAs
  12. AI in Equity Crowdfunding and Alternative Capital Raising
  13. AI in Alternative Investments — PE, VC, Hedge Funds, Real Assets
  14. AI in Digital Assets — Crypto, DeFi, and Tokenization
  15. AI in Distribution — IFAs, MFDs, RIAs, Digital Distribution
  16. Investment Risk Modeling in the AI Era — VaR, Stress Testing, Portfolio Risk
  17. Surveillance, Compliance, and Investor Protection
  18. Operational Risk, Cybersecurity, and AI-Driven Threats in Investment Management
  19. What Generative AI Means for the Investment Professional
  20. LLMs in the Investment Workflow
  21. Limits and Risks of Generative AI in Investment Management
  22. From Investment Assistants to Investment Agents
  23. Agentic Workflows in Practice
  24. Governance, Model Risk, and Responsible AI in Investment Management
  25. Regulation, Compliance, and the SEBI Architecture
  26. Cybersecurity, Privacy, and AI in Investment Management
  27. The Role-by-Role Transformation
  28. Skills, Credentials, and the Career Architecture of the AI-First Investment Firm
  29. The Future of Indian Wealth, Asset Management, and Capital Markets
Student and faculty companions

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