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AI in Asset Management Market By Offering (Solution, Services), By Technology (Machine Learning, Natural Language Processing, Predictive Analytics, Robotic Process Automation), By Application (Portfolio Management, Risk Management, Trading), By End-User (Institutional Investors, Retail Investors), and By Region; Global Insights & Forecast (2024 – 2030)

Published: December, 2024  
|   Report ID: TMT4261  
|   Technology, Media, and Telecommunications

As per Intent Market Research, the AI in Asset Management Market was valued at USD 2.3 billion in 2023 and will surpass USD 5.5 billion by 2030; growing at a CAGR of 13.1% during 2024 - 2030.

The AI in asset management market is experiencing rapid growth as financial institutions increasingly adopt artificial intelligence technologies to enhance their investment strategies and improve operational efficiencies. The integration of AI into asset management allows for more accurate data analysis, better risk assessment, and improved decision-making processes. As firms strive to meet the demands of a fast-paced financial environment and ever-changing market dynamics, AI-driven solutions are becoming essential. This transformative technology is not only optimizing traditional asset management practices but also enabling new strategies that cater to a broader range of investor needs, from institutional to retail clients.

Solutions Offering Is Largest Owing To Comprehensive Capabilities

Within the offerings in the AI in asset management market, solutions represent the largest segment, driven by their comprehensive capabilities to enhance various aspects of investment management. AI solutions enable asset managers to process vast amounts of data, uncover patterns, and gain insights that inform strategic investment decisions. By employing advanced algorithms and machine learning techniques, these solutions facilitate portfolio optimization, risk assessment, and performance measurement, significantly enhancing the efficiency of asset management operations.

The demand for integrated solutions that encompass various functions—such as portfolio management, trading, and compliance—has propelled the growth of this segment. As asset management firms increasingly recognize the value of deploying AI-driven solutions to streamline processes and enhance investment performance, the solutions offering is expected to maintain its dominant position in the market, fostering innovation and improving client outcomes.

AI in Asset Management Market By Offering

Machine Learning Is Fastest Growing Technology Owing To Data-Driven Insights

Among the technologies utilized in the AI in asset management market, machine learning is the fastest-growing area, largely due to its ability to provide data-driven insights and improve predictive capabilities. Machine learning algorithms enable asset managers to analyze historical data and identify trends, allowing for better forecasting and investment strategies. This technology is particularly beneficial in areas such as risk management, where it can enhance the accuracy of risk assessments by adapting to new data and evolving market conditions.

The growing reliance on machine learning in trading algorithms, portfolio management, and client service optimization highlights its transformative potential in the asset management industry. As firms increasingly invest in machine learning capabilities to enhance their analytical tools and improve decision-making processes, this technology is set to witness significant growth, solidifying its role in the ongoing digital transformation of asset management.

Portfolio Management Application Is Largest Owing To Investment Optimization

In the application segment, portfolio management is the largest area in the AI in asset management market, reflecting the critical role of effective investment strategies in achieving optimal financial returns. The application of AI technologies in portfolio management enables asset managers to analyze diverse datasets, optimize asset allocation, and tailor investment strategies to meet specific client goals. By leveraging AI, firms can enhance their ability to make data-driven decisions, adapt to changing market conditions, and ultimately improve portfolio performance.

The increasing complexity of financial markets and the growing need for customized investment solutions are driving the demand for AI-driven portfolio management applications. As asset managers seek to deliver superior investment outcomes and meet the diverse needs of their clients, the portfolio management application is expected to maintain its prominence in the market, contributing to the overall growth of AI in asset management.

Institutional Investors End-User Is Largest Owing To High Capital Inflows

Among the end-user segments, institutional investors represent the largest area in the AI in asset management market. Institutional investors, including pension funds, insurance companies, and hedge funds, manage substantial capital inflows and are increasingly leveraging AI technologies to enhance their investment strategies. The ability to analyze vast amounts of data and implement sophisticated algorithms enables these investors to make informed decisions, manage risk effectively, and optimize their portfolios.

As institutional investors prioritize technology adoption to stay competitive and achieve better returns, the demand for AI solutions tailored to their specific needs is expected to grow. The trend towards greater efficiency and transparency in investment processes positions institutional investors as a key driver of growth in the AI in asset management market.

North America Is Largest Region Owing To Innovation and Investment

Geographically, North America is the largest region in the AI in asset management market, propelled by a strong presence of financial institutions and a culture of innovation. The region's asset management firms are increasingly investing in AI technologies to enhance operational efficiency, improve client engagement, and drive investment performance. The availability of advanced data analytics tools and a skilled workforce further bolster North America's leadership in AI adoption within the asset management sector.

Additionally, the competitive landscape in North America is marked by a collaboration between established asset management firms and emerging fintech companies, leading to the development of innovative AI solutions. As the demand for AI-driven investment strategies continues to grow, North America is expected to remain at the forefront of the AI in asset management market, shaping the future of investment management practices.

AI in Asset Management Market share by region

Competitive Landscape and Leading Companies

The competitive landscape of the AI in asset management market is characterized by the presence of both established financial institutions and innovative technology providers. Leading companies such as BlackRock, Goldman Sachs, and J.P. Morgan are increasingly integrating AI into their asset management practices to enhance decision-making and improve operational efficiency. These firms are focused on leveraging AI technologies to gain a competitive edge in the market, optimizing their investment strategies, and enhancing client experiences.

Furthermore, technology firms specializing in AI and data analytics, such as Palantir Technologies, Zest AI, and Numerai, are also making significant inroads into the asset management space. Their innovative solutions are enabling asset managers to harness the power of AI for more effective portfolio management and risk assessment. As the market evolves, collaboration between traditional asset managers and technology providers will likely intensify, fostering a dynamic and competitive environment in the AI in asset management market.

Recent Developments:

  • BlackRock announced the launch of a new AI-driven investment platform aimed at enhancing portfolio optimization.
  • Goldman Sachs expanded its AI capabilities by acquiring a fintech startup specializing in predictive analytics for asset management.
  • J.P. Morgan introduced an AI tool designed to provide real-time market insights and trade recommendations.
  • Fidelity Investments implemented AI technologies to streamline its trading operations and improve execution speed.
  • UBS launched a robo-advisory service utilizing AI to tailor investment strategies for individual investors.

List of Leading Companies:

  • Amundi Asset Management
  • BlackRock
  • Charles Schwab Investment Management
  • Fidelity Investments
  • Franklin Templeton Investments
  • Goldman Sachs Asset Management
  • Invesco Ltd.
  • J.P. Morgan Asset Management
  • Morgan Stanley Investment Management
  • Northern Trust Asset Management
  • State Street Global Advisors
  • T. Rowe Price
  • UBS Asset Management
  • Vanguard Group
  • Wells Fargo Asset Management

Report Scope:

Report Features

Description

Market Size (2023)

USD 2.3 billion

Forecasted Value (2030)

USD 5.5 billion

CAGR (2024 – 2030)

13.1%

Base Year for Estimation

2023

Historic Year

2022

Forecast Period

2024 – 2030

Report Coverage

Market Forecast, Market Dynamics, Competitive Landscape, Recent Developments

Segments Covered

AI in Asset Management Market By Offering (Solution, Services), By Technology (Machine Learning, Natural Language Processing, Predictive Analytics, Robotic Process Automation), By Application (Portfolio Management, Risk Management, Trading), By End-User (Institutional Investors, Retail Investors)

Regional Analysis

North America (US, Canada, Mexico), Europe (Germany, France, UK, Italy, Spain, and Rest of Europe), Asia-Pacific (China, Japan, South Korea, Australia, India, and Rest of Asia-Pacific), Latin America (Brazil, Argentina, and Rest of Latin America), Middle East & Africa (Saudi Arabia, UAE, Rest of Middle East & Africa)

Major Companies

Amundi Asset Management, BlackRock, Charles Schwab Investment Management, Fidelity Investments, Franklin Templeton Investments, Goldman Sachs Asset Management, Invesco Ltd., J.P. Morgan Asset Management, Morgan Stanley Investment Management, State Street Global Advisors, T. Rowe Price, UBS Asset Management, UBS Asset Management, Vanguard Group, Wells Fargo Asset Management

Customization Scope

Customization for segments, region/country-level will be provided. Moreover, additional customization can be done based on the requirements

1. Introduction

   1.1. Market Definition

   1.2. Scope of the Study

   1.3. Research Assumptions

   1.4. Study Limitations

2. Research Methodology

   2.1. Research Approach

      2.1.1. Top-Down Method

      2.1.2. Bottom-Up Method

      2.1.3. Factor Impact Analysis

  2.2. Insights & Data Collection Process

      2.2.1. Secondary Research

      2.2.2. Primary Research

   2.3. Data Mining Process

      2.3.1. Data Analysis

      2.3.2. Data Validation and Revalidation

      2.3.3. Data Triangulation

3. Executive Summary

   3.1. Major Markets & Segments

   3.2. Highest Growing Regions and Respective Countries

   3.3. Impact of Growth Drivers & Inhibitors

   3.4. Regulatory Overview by Country

4. AI in Asset Management Market, by Offering (Market Size & Forecast: USD Million, 2022 – 2030)

   4.1. Solution

   4.2. Services

5. AI in Asset Management Market, by Technology (Market Size & Forecast: USD Million, 2022 – 2030)

   5.1. Machine Learning

      5.1.1. Supervised Learning

      5.1.2. Unsupervised Learning

   5.2. Natural Language Processing

      5.2.1. Text Analytics

      5.2.2. Sentiment Analysis

   5.3. Predictive Analytics

   5.4. Robotic Process Automation

   5.5. Others

6. AI in Asset Management Market, by Application (Market Size & Forecast: USD Million, 2022 – 2030)

   6.1. Portfolio Management

      6.1.1. Asset Allocation

      6.1.2. Performance Measurement

   6.2. Risk Management

      6.2.1. Stress Testing

      6.2.2. Credit Risk Assessment

   6.3. Trading

      6.3.1. Algorithmic Trading

      6.3.2. High-Frequency Trading

   6.4. Others

7. AI in Asset Management Market, by End-User (Market Size & Forecast: USD Million, 2022 – 2030)

   7.1. Institutional Investors

   7.2. Retail Investors

8. Regional Analysis (Market Size & Forecast: USD Million, 2022 – 2030)

   8.1. Regional Overview

   8.2. North America

      8.2.1. Regional Trends & Growth Drivers

      8.2.2. Barriers & Challenges

      8.2.3. Opportunities

      8.2.4. Factor Impact Analysis

      8.2.5. Technology Trends

      8.2.6. North America AI in Asset Management Market, by Offering

      8.2.7. North America AI in Asset Management Market, by Technology

      8.2.8. North America AI in Asset Management Market, by Application

      8.2.9. North America AI in Asset Management Market, by End-User

      8.2.10. By Country

         8.2.10.1. US

               8.2.10.1.1. US AI in Asset Management Market, by Offering

               8.2.10.1.2. US AI in Asset Management Market, by Technology

               8.2.10.1.3. US AI in Asset Management Market, by Application

               8.2.10.1.4. US AI in Asset Management Market, by End-User

         8.2.10.2. Canada

         8.2.10.3. Mexico

    *Similar segmentation will be provided for each region and country

   8.3. Europe

   8.4. Asia-Pacific

   8.5. Latin America

   8.6. Middle East & Africa

9. Competitive Landscape

   9.1. Overview of the Key Players

   9.2. Competitive Ecosystem

      9.2.1. Level of Fragmentation

      9.2.2. Market Consolidation

      9.2.3. Product Innovation

   9.3. Company Share Analysis

   9.4. Company Benchmarking Matrix

      9.4.1. Strategic Overview

      9.4.2. Product Innovations

   9.5. Start-up Ecosystem

   9.6. Strategic Competitive Insights/ Customer Imperatives

   9.7. ESG Matrix/ Sustainability Matrix

   9.8. Manufacturing Network

      9.8.1. Locations

      9.8.2. Supply Chain and Logistics

      9.8.3. Product Flexibility/Customization

      9.8.4. Digital Transformation and Connectivity

      9.8.5. Environmental and Regulatory Compliance

   9.9. Technology Readiness Level Matrix

   9.10. Technology Maturity Curve

   9.11. Buying Criteria

10. Company Profiles

   10.1. Amundi Asset Management

      10.1.1. Company Overview

      10.1.2. Company Financials

      10.1.3. Product/Service Portfolio

      10.1.4. Recent Developments

      10.1.5. IMR Analysis

    *Similar information will be provided for other companies 

   10.2. BlackRock

   10.3. Charles Schwab Investment Management

   10.4. Fidelity Investments

   10.5. Franklin Templeton Investments

   10.6. Goldman Sachs Asset Management

   10.7. Invesco Ltd.

   10.8. J.P. Morgan Asset Management

   10.9. Morgan Stanley Investment Management

   10.10. Northern Trust Asset Management

   10.11. State Street Global Advisors

   10.12. T. Rowe Price

   10.13. UBS Asset Management

   10.14. Vanguard Group

   10.15. Wells Fargo Asset Management

11. Appendix

 

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A comprehensive market research approach was employed to gather and analyze data on the AI in Asset Management Market. In the process, the analysis was also done to analyze the parent market and relevant adjacencies to measure the impact of them on the AI in Asset Management Market. The research methodology encompassed both secondary and primary research techniques, ensuring the accuracy and credibility of the findings.

Research Approach - AI in Asset Management Market

Secondary Research

Secondary research involved a thorough review of pertinent industry reports, journals, articles, and publications. Additionally, annual reports, press releases, and investor presentations of industry players were scrutinized to gain insights into their market positioning and strategies.

Primary Research

Primary research involved conducting in-depth interviews with industry experts, stakeholders, and market participants across the AI in Asset Management ecosystem. The primary research objectives included:

  • Validating findings and assumptions derived from secondary research
  • Gathering qualitative and quantitative data on market trends, drivers, and challenges
  • Understanding the demand-side dynamics, encompassing end-users, component manufacturers, facility providers, and service providers
  • Assessing the supply-side landscape, including technological advancements and recent developments

Market Size Assessment

A combination of top-down and bottom-up approaches was utilized to analyze the overall size of the AI in Asset Management Market. These methods were also employed to assess the size of various subsegments within the market. The market size assessment methodology encompassed the following steps:

  1. Identification of key industry players and relevant revenues through extensive secondary research
  2. Determination of the industry's supply chain and market size, in terms of value, through primary and secondary research processes
  3. Calculation of percentage shares, splits, and breakdowns using secondary sources and verification through primary sources

Bottom Up and Top Down - AI in Asset Management Market

Data Triangulation

To ensure the accuracy and reliability of the market size, data triangulation was implemented. This involved cross-referencing data from various sources, including demand and supply side factors, market trends, and expert opinions. Additionally, top-down and bottom-up approaches were employed to validate the market size assessment.

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