AI in Financial Planning and Wealth Management Market Size and Revenue Impact Study 2025 to 2034

Report Id: 2742 Pages: 175 Last Updated: 23 December 2025 Format: PDF / PPT / Excel / Power BI
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Segmentation of AI in Financial Planning and Wealth Management Market-

AI in Financial Planning and Wealth Management Market By Type-

  • Rule-based Al systems
  • Machine Learning-based Al systems
  • Natural Language Processing (NLP) Al systems

ai in financial planning

AI in Financial Planning and Wealth Management Market By Application-

  • Robo-advisors
  • Risk assessment and management
  • Fraud detection and prevention
  • Customer service and support
  • Personalized financial recommendations
  • Market analysis and prediction
  • Portfolio optimization

AI in Financial Planning and Wealth Management Market By End-user-

  • Banks and financial institutions
  • Investment firms and asset managers
  • Insurance companies
  • Individual investors and customers

AI in Financial Planning and Wealth Management Market By Functionality-

  • Data analysis and processing
  • Automated investment management
  • Cognitive computing and decision-making
  • Chatbots and virtual assistants
  • Predictive analytics and forecasting

AI in Financial Planning and Wealth Management Market By Region-

North America-

  • The US
  • Canada

Europe-

  • Germany
  • The UK
  • France
  • Italy
  • Spain
  • Rest of Europe

Asia-Pacific-

  • China
  • Japan
  • India
  • South Korea
  • South East Asia
  • Rest of Asia Pacific

Latin America-

  • Brazil
  • Mexico
  • Argentina
  • Rest of Latin America

 Middle East & Africa-

  • GCC Countries
  • South Africa
  • Rest of Middle East and Africa

Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions

Chapter 2. Executive Summary

Chapter 3. Global Artificial Intelligence in Financial Planning and Wealth Management Market Snapshot

Chapter 4. Global Artificial Intelligence in Financial Planning and Wealth Management Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis
4.6. Industry Analysis – Porter’s Five Forces Analysis
4.7. Competitive Landscape & Market Share Analysis
4.8. Impact of Covid-19 Analysis

Chapter 5. Market Segmentation 1: by Type Estimates & Trend Analysis
5.1. by Type & Market Share, 2024 & 2034
5.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following by Type:

5.2.1. Rule-based AI systems
5.2.2. Machine Learning-based AI systems
5.2.3. Natural Language Processing (NLP) AI systems

Chapter 6. Market Segmentation 2: by Application Estimates & Trend Analysis
6.1. by Application & Market Share, 2024 & 2034
6.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following by Application:

6.2.1. Robo-advisors
6.2.2. Risk assessment and management
6.2.3. Fraud detection and prevention
6.2.4. Customer service and support
6.2.5. Personalized financial recommendations
6.2.6. Market analysis and prediction
6.2.7. Portfolio optimization

Chapter 7. Market Segmentation 3: by End-User Estimates & Trend Analysis
7.1. by End-User & Market Share, 2024 & 2034
7.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following by End-User:

7.2.1. Banks and financial institutions
7.2.2. Investment firms and asset managers
7.2.3. Insurance companies
7.2.4. Individual investors and customers

Chapter 8. Market Segmentation 4: by Functionality Estimates & Trend Analysis
8.1. by Functionality & Market Share, 2024 & 2034
8.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following by Functionality:

8.2.1. Data analysis and processing
8.2.2. Automated investment management
8.2.3. Cognitive computing and decision-making
8.2.4. Chatbots and virtual assistants
8.2.5. Predictive analytics and forecasting

Chapter 9. Artificial Intelligence in Financial Planning and Wealth Management Market Segmentation 5: Regional Estimates & Trend Analysis

9.1. North America

9.1.1. North America Artificial Intelligence in Financial Planning and Wealth Management Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
9.1.2. North America Artificial Intelligence in Financial Planning and Wealth Management Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
9.1.3. North America Artificial Intelligence in Financial Planning and Wealth Management Market Revenue (US$ Million) Estimates and Forecasts by End-User, 2021-2034
9.1.4. North America Artificial Intelligence in Financial Planning and Wealth Management Market Revenue (US$ Million) Estimates and Forecasts by Functionality, 2021-2034
9.1.5. North America Artificial Intelligence in Financial Planning and Wealth Management Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034

9.2. Europe

9.2.1. Europe Artificial Intelligence in Financial Planning and Wealth Management Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
9.2.2. Europe Artificial Intelligence in Financial Planning and Wealth Management Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
9.2.3. Europe Artificial Intelligence in Financial Planning and Wealth Management Market Revenue (US$ Million) Estimates and Forecasts by End-User, 2021-2034
9.2.4. Europe Artificial Intelligence in Financial Planning and Wealth Management Market Revenue (US$ Million) Estimates and Forecasts by Functionality, 2021-2034
9.2.5. Europe Artificial Intelligence in Financial Planning and Wealth Management Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034

9.3. Asia Pacific

9.3.1. Asia Pacific Artificial Intelligence in Financial Planning and Wealth Management Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
9.3.2. Asia Pacific Artificial Intelligence in Financial Planning and Wealth Management Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
9.3.3. Asia-Pacific Artificial Intelligence in Financial Planning and Wealth Management Market Revenue (US$ Million) Estimates and Forecasts by End-User, 2021-2034
9.3.4. Asia-Pacific Artificial Intelligence in Financial Planning and Wealth Management Market Revenue (US$ Million) Estimates and Forecasts by Functionality, 2021-2034
9.3.5. Asia Pacific Artificial Intelligence in Financial Planning and Wealth Management Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034

9.4. Latin America

9.4.1. Latin America Artificial Intelligence in Financial Planning and Wealth Management Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
9.4.2. Latin America Artificial Intelligence in Financial Planning and Wealth Management Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
9.4.3. Latin America Artificial Intelligence in Financial Planning and Wealth Management Market Revenue (US$ Million) Estimates and Forecasts by End-User, 2021-2034
9.4.4. Latin America Artificial Intelligence in Financial Planning and Wealth Management Market Revenue (US$ Million) Estimates and Forecasts by Functionality, 2021-2034
9.4.5. Latin America Artificial Intelligence in Financial Planning and Wealth Management Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034

9.5. Middle East & Africa

9.5.1. Middle East & Africa Artificial Intelligence in Financial Planning and Wealth Management Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
9.5.2. Middle East & Africa Artificial Intelligence in Financial Planning and Wealth Management Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
9.5.3. Middle East & Africa Artificial Intelligence in Financial Planning and Wealth Management Market Revenue (US$ Million) Estimates and Forecasts by End-User, 2021-2034
9.5.4. Middle East & Africa Artificial Intelligence in Financial Planning and Wealth Management Market Revenue (US$ Million) Estimates and Forecasts by Functionality, 2021-2034
9.5.5. Middle East & Africa Artificial Intelligence in Financial Planning and Wealth Management Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034

Chapter 10. Competitive Landscape
10.1. Major Mergers and Acquisitions/Strategic Alliances
10.2. Company Profiles

10.2.1. Wealthfront
10.2.2. Betterment
10.2.3. Personal Capital
10.2.4. FutureAdvisor
10.2.5. SigFig
10.2.6. WiseBanyan
10.2.7. Nutmeg
10.2.8. Acorns
10.2.9. Charles Schwab Intelligent Portfolios
10.2.10. Vanguard Personal Advisor Services
10.2.11. BlackRock
10.2.12. Fidelity Go
10.2.13. Other Prominent Players

Research Design and Approach

This study employed a multi-step, mixed-method research approach that integrates:

  • Secondary research
  • Primary research
  • Data triangulation
  • Hybrid top-down and bottom-up modelling
  • Forecasting and scenario analysis

This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.

Secondary Research

Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.

Sources Consulted

Secondary data for the market study was gathered from multiple credible sources, including:

  • Government databases, regulatory bodies, and public institutions
  • International organizations (WHO, OECD, IMF, World Bank, etc.)
  • Commercial and paid databases
  • Industry associations, trade publications, and technical journals
  • Company annual reports, investor presentations, press releases, and SEC filings
  • Academic research papers, patents, and scientific literature
  • Previous market research publications and syndicated reports

These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.

Secondary Research

Primary Research

Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.

Stakeholders Interviewed

Primary interviews for this study involved:

  • Manufacturers and suppliers in the market value chain
  • Distributors, channel partners, and integrators
  • End-users / customers (e.g., hospitals, labs, enterprises, consumers, etc., depending on the market)
  • Industry experts, technology specialists, consultants, and regulatory professionals
  • Senior executives (CEOs, CTOs, VPs, Directors) and product managers

Interview Process

Interviews were conducted via:

  • Structured and semi-structured questionnaires
  • Telephonic and video interactions
  • Email correspondences
  • Expert consultation sessions

Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.

Data Processing, Normalization, and Validation

All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.

The data validation process included:

  • Standardization of units (currency conversions, volume units, inflation adjustments)
  • Cross-verification of data points across multiple secondary sources
  • Normalization of inconsistent datasets
  • Identification and resolution of data gaps
  • Outlier detection and removal through algorithmic and manual checks
  • Plausibility and coherence checks across segments and geographies

This ensured that the dataset used for modelling was clean, robust, and reliable.

Market Size Estimation and Data Triangulation

Bottom-Up Approach

The bottom-up approach involved aggregating segment-level data, such as:

  • Company revenues
  • Product-level sales
  • Installed base/usage volumes
  • Adoption and penetration rates
  • Pricing analysis

This method was primarily used when detailed micro-level market data were available.

Bottom Up Approach

Top-Down Approach

The top-down approach used macro-level indicators:

  • Parent market benchmarks
  • Global/regional industry trends
  • Economic indicators (GDP, demographics, spending patterns)
  • Penetration and usage ratios

This approach was used for segments where granular data were limited or inconsistent.

Hybrid Triangulation Approach

To ensure accuracy, a triangulated hybrid model was used. This included:

  • Reconciling top-down and bottom-up estimates
  • Cross-checking revenues, volumes, and pricing assumptions
  • Incorporating expert insights to validate segment splits and adoption rates

This multi-angle validation yielded the final market size.

Forecasting Framework and Scenario Modelling

Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.

Forecasting Methods

  • Time-series modelling
  • S-curve and diffusion models (for emerging technologies)
  • Driver-based forecasting (GDP, disposable income, adoption rates, regulatory changes)
  • Price elasticity models
  • Market maturity and lifecycle-based projections

Scenario Analysis

Given inherent uncertainties, three scenarios were constructed:

  • Base-Case Scenario: Expected trajectory under current conditions
  • Optimistic Scenario: High adoption, favourable regulation, strong economic tailwinds
  • Conservative Scenario: Slow adoption, regulatory delays, economic constraints

Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.

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Frequently Asked Questions

AI in Financial Planning and Wealth Management Market Size is valued at USD 20.8 Bn in 2024 and is predicted to reach USD 129.6 Bn by the year 2034

Global AI in Financial Planning and Wealth Management Market is expected to grow at a 20.2% CAGR during the forecast period for 2025 to 2034

WealthFront, Betterment, Personal Capital, FutureAdvisor, SigFig, WiseBanyan, Nutmeg, Acorns, Charles Schwab Intelligent Portfolios, Vanguard Personal

Product, Application, End-User and Functionality are the key segments of the AI in Financial Planning and Wealth Management Market.

North America region is leading the AI in Financial Planning and Wealth Management Market.

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