Global 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 at a 20.2% CAGR during the forecast period for 2025-2034.
AI in the financial planning and wealth management industry has been growing in recent years, although leading wealth managers have been using AI and machine learning for years. As AI technology advances, the way the financial sector operates changes, enabling significant improvements and creating new opportunities for wealth management firms. According to OT, driven by client demand and increased expectations of wealth management services, AI is enabling customization and personalization at scale, targeting improved communication with clients, which is more seamless, value-adding, and real-time.
In addition, AI can help relationship managers in wealth management build stronger relationships with clients, helping them, based on best practices, to become more effective in client acquisition, client development, client activation, and client retention.
Important drivers of this market include the rising adoption of AI-driven robo-advisors, which provide automated, algorithm-based portfolio management advice with minimal human intervention. These platforms offer cost-effective, personalized financial planning to a broader audience, including those previously underserved by traditional financial advisors. Additionally, wealth management firms must comply with a vast array of regulations, which can vary between jurisdictions and evolve. Compliance requirements seek to protect clients' interests, maintain market integrity, and prevent financial crime. AI can help wealth management firms and managers comply with rules and regulations by automating tasks such as identifying suspicious activities, monitoring transactions, and reporting them to regulators. However, AI must be transparent and accountable.
The AI in Financial Planning and Wealth Management market is segmented on the basis of product, application, end-user, and functionality. As per the product, the market is segmented into rule-based Al systems, machine learning-based Al systems, and natural language processing (NLP) Al systems. By application, the market is segmented into Robo-advisors, Risk assessment and management, Fraud detection and prevention, Customer service and support, personalized financial recommendations, Market analysis and prediction, and Portfolio optimization. Based on end-users, the market is segmented into banks and financial institutions, investment firms and asset managers, insurance companies, individual investors, and customers. The functionality segment includes Data analysis and processing, automated investment management, Cognitive computing and decision-making, Chatbots and virtual assistants, Predictive analytics, and forecasting.
The Rule-based Al systems category is expected to lead with a major share in the global AI in Financial Planning and Wealth Management market. These systems operate on predefined rules and logic to provide financial advice and manage wealth. They are specifically useful for automating repetitive tasks, such as portfolio rebalancing, tax-loss harvesting, and compliance checks. Rule-based AI systems enhance efficiency and reduce operational costs by eliminating manual interventions. In North America, the adoption of these systems is driven by a mature financial sector and stringent regulatory requirements. At the same time, the Asia Pacific region sees rapid growth due to increasing digitalization and financial inclusion efforts. These systems are crucial for delivering consistent and reliable financial services, thereby improving client satisfaction and trust in AI-driven financial planning solutions.
The robo-advisors segment is projected to grow rapidly in the global AI in Financial Planning and Wealth Management market owing to automated platforms leveraging AI algorithms to provide significant investment advice and portfolio management with minimal human intervention. Robo-advisors analyze vast amounts of financial data, assess risk tolerance, and offer tailored recommendations, making investment accessible and affordable to a broader audience. Their ability to operate 24/7, coupled with lower fees compared to traditional financial advisors, has fueled their popularity, especially among tech-savvy millennials and cost-conscious investors. As AI technology continues to advance, robo-advisors are expected to become more sophisticated, offering increasingly precise and customized financial planning solutions, thereby driving significant growth in the financial planning and wealth management market.
The North American AI in Financial Planning and Wealth Management market holds a significant revenue share due to several factors. The region boasts advanced technological infrastructure, high adoption rates of AI technologies, and a robust financial sector. The presence of leading financial institutions and wealth management firms drives the demand for AI solutions to enhance customer experiences, optimize investment strategies, and improve operational efficiency. Additionally, regulatory support for technological innovation and a well-established data analytics ecosystem further contribute to market growth. The increasing need for personalized financial advice and the growing adoption of AI-driven tools for risk management, fraud detection, and compliance are key drivers propelling the market's expansion in North America.
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Report Attribute |
Specifications |
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Market Size Value In 2024 |
USD 20.8 Bn |
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Revenue Forecast In 2034 |
USD 129.6 Bn |
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Growth Rate CAGR |
CAGR of 20.2% from 2025 to 2034 |
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Quantitative Units |
Representation of revenue in US$ Bn and CAGR from 2025 to 2034 |
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Historic Year |
2021 to 2024 |
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Forecast Year |
2025-2034 |
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Report Coverage |
The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
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Segments Covered |
By Product, Application, End-User and Functionality |
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Regional Scope |
North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
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Country Scope |
U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
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Competitive Landscape |
WealthFront, Betterment, Personal Capital, FutureAdvisor, SigFig, WiseBanyan, Nutmeg, Acorns, Charles Schwab Intelligent Portfolios, Vanguard Personal Advisor Services, BlackRock, and Fidelity Go. |
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Customization Scope |
Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
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Pricing And Available Payment Methods |
Explore pricing alternatives that are customized to your particular study requirements. |
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
AI in Financial Planning and Wealth Management Market By Type-
AI in Financial Planning and Wealth Management Market By Application-
AI in Financial Planning and Wealth Management Market By End-user-
AI in Financial Planning and Wealth Management Market By Functionality-
AI in Financial Planning and Wealth Management Market By Region-
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
InsightAce Analytic follows a standard and comprehensive market research methodology focused on offering the most accurate and precise market insights. The methods followed for all our market research studies include three significant steps – primary research, secondary research, and data modeling and analysis - to derive the current market size and forecast it over the forecast period. In this study, these three steps were used iteratively to generate valid data points (minimum deviation), which were cross-validated through multiple approaches mentioned below in the data modeling section.
Through secondary research methods, information on the market under study, its peer, and the parent market was collected. This information was then entered into data models. The resulted data points and insights were then validated by primary participants.
Based on additional insights from these primary participants, more directional efforts were put into doing secondary research and optimize data models. This process was repeated till all data models used in the study produced similar results (with minimum deviation). This way, this iterative process was able to generate the most accurate market numbers and qualitative insights.
Secondary research
The secondary research sources that are typically mentioned to include, but are not limited to:
The paid sources for secondary research like Factiva, OneSource, Hoovers, and Statista
Primary Research:
Primary research involves telephonic interviews, e-mail interactions, as well as face-to-face interviews for each market, category, segment, and subsegment across geographies
The contributors who typically take part in such a course include, but are not limited to:
Data Modeling and Analysis:
In the iterative process (mentioned above), data models received inputs from primary as well as secondary sources. But analysts working on these models were the key. They used their extensive knowledge and experience about industry and topic to make changes and fine-tuning these models as per the product/service under study.
The standard data models used while studying this market were the top-down and bottom-up approaches and the company shares analysis model. However, other methods were also used along with these – which were specific to the industry and product/service under study.
