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市场调查报告书
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1733821

全球资产管理人工智慧市场规模（按技术、部署模式、应用、区域范围和预测）：

Global AI In Asset Management Market Size By Technology (Machine Learning, Natural Language Processing ), By Deployment Mode, Application, By Geographic Scope And Forecast
出版日期: | 出版商: Verified Market Research | 英文 202 Pages | 商品交期: 2-3个工作天内

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简介目录

资产管理人工智慧的市场规模与预测

预计2024年资产管理人工智慧市场规模将达27.8亿美元，到2032年将达到475.8亿美元，在2026-2032年预测期间的复合年增长率为34.37%。

资产管理中的人工智慧是应用先进的演算法和机器学习技术来管理和优化金融资产。

该技术可望增强决策流程，改善预测分析，并促进更有效率的投资组合管理。

人工智慧在资产管理的应用多种多样且成长迅速。自动交易系统、风险评估工具和投资组合最佳化模型是人工智慧应用的主要领域。

透过利用人工智慧，资产管理公司有望提高市场趋势预测的准确性，更好地使投资策略与客户目标保持一致，并简化营运效率。

预计资产管理领域人工智慧的成长将受到多种因素的推动。金融市场日益复杂以及对个人化投资解决方案的需求不断增长预计将推动人工智慧技术的采用。

此外，人工智慧能力的进步和巨量资料可用性的提高可能会进一步刺激该领域人工智慧应用的扩展。

全球资产管理人工智慧市场动态

影响全球资产管理人工智慧市场的关键市场动态是：

关键市场驱动因素

金融市场日益复杂：金融市场日益复杂：金融市场日益复杂预计将推动资产管理对人工智慧的需求。预计人工智慧技术将越来越多地融入管理复杂的金融产品和多样化的资产类别，从而增强决策流程。

个人化投资解决方案的需求：个人化投资解决方案的需求不断增长，预计将推动人工智慧在财富管理中的应用。人工智慧工具可能会被用来根据个人客户偏好和风险状况来客製化投资策略，从而提高客户满意度和投资组合绩效。德勤 2023 年的一项调查发现，72% 的资产管理公司正在投资人工智慧和机器学习，以提供更个人化的投资解决方案。此外，严重依赖人工智慧的机器人咨询市场预计将在 2023 年达到 184 亿美元，2024 年至 2030 年的复合年增长率为 31.8%。

巨量资料的可用性：巨量资料的日益普及预计将推动资产管理领域人工智慧应用的成长。增强的资料来源将实现更准确的预测分析和风险评估，并有望带来更明智的投资决策。

人工智慧技术的进步：人工智慧技术的持续进步有望促进人工智慧在资产管理领域的扩展。改进的机器学习演算法和先进的分析工具等创新将提高资产管理业务的效率和效力。

主要问题

资料安全问题：资料安全问题预计会阻碍人工智慧在资产管理中的应用。预计与资料外洩和网路攻击相关的风险将阻碍人工智慧技术在敏感金融资讯管理中的广泛应用。

实施成本高：人工智慧技术实施成本高预计会阻碍其在资产管理上的应用。开发、整合和维护先进的人工智慧系统可能需要大量投资，这可能会限制其在中小企业中的应用。

监管和合规挑战：监管和合规挑战预计将阻碍人工智慧在资产管理领域的发展。需要遵守严格的金融法规和资料隐私法，预计会使该领域人工智慧解决方案的部署和运作变得复杂。

有限的人工智慧专业知识：有限的人工智慧专业知识预计会阻碍人工智慧在资产管理中的有效整合。预计缺乏能够开发和管理人工智慧系统的熟练专业人员将阻碍这些技术的采用和最佳化。

主要趋势

机器学习演算法推出机器学习演算法的日益普及预计将成为资产管理市场人工智慧的关键趋势。这些演算法有望增强预测分析和决策能力，提供更准确的投资见解和策略。

自然语言处理 (NLP) 的使用：自然语言处理 (NLP) 的使用日益增多，有望改变财富管理中的客户互动和数据分析。 NLP 技术可能会被整合以改善对金融新闻和报告、市场情绪的解读，并完善投资策略。

关注监管科技：对监管科技的高度关注预计将塑造资产管理领域人工智慧的格局。专为法规遵从而设计的人工智慧解决方案预计将变得更加普遍，因为它们可以帮助公司应对复杂的法规并降低合规风险。

机器人顾问的采用：机器人顾问的日益普及预计将成为主要的市场趋势。机器人顾问提供自动化、演算法主导的财务规划服务，预计会让更广大的客户更容易、更经济地进行投资管理。

目录

第一章 引言

  • 市场定义
  • 市场区隔
  • 调查方法

第二章执行摘要

  • 主要发现
  • 市场概览
  • 市集亮点

第三章市场概述

  • 市场规模和成长潜力
  • 市场趋势
  • 市场驱动因素
  • 市场限制
  • 市场机会
  • 波特五力分析

第四章 资产管理市场中的人工智慧（按技术）

  • 机器学习
  • 自然语言处理（NLP）

第五章 资产管理市场中的人工智慧（依部署模式）

  • 本地
  • 云

第六章 资产管理中的人工智慧市场（按应用）

  • 投资组合最佳化
  • 对话平台
  • 风险合规
  • 数据分析
  • 流程自动化

第七章区域分析

  • 北美洲
  • 美国
  • 加拿大
  • 墨西哥
  • 欧洲
  • 英国
  • 德国
  • 法国
  • 义大利
  • 亚太地区
  • 中国
  • 日本
  • 印度
  • 澳洲
  • 拉丁美洲
  • 巴西
  • 阿根廷
  • 智利
  • 中东和非洲
  • 南非
  • 沙乌地阿拉伯
  • 阿拉伯聯合大公国

第八章市场动态

  • 市场驱动因素
  • 市场限制
  • 市场机会
  • COVID-19 市场影响

第九章 竞争态势

  • 主要企业
  • 市场占有率分析

第十章 公司简介

  • BlackRock
  • Vanguard Group
  • State Street Corporation
  • Fidelity Investments
  • Goldman Sachs Group, Inc.
  • JPMorgan Chase & Co.
  • IBM
  • Microsoft
  • Google
  • Palantir Technologies, Inc.
  • AlphaSense
  • Kensho Technologies
  • Quantiacs
  • Axioma

第十一章 市场展望与机会

  • 新兴技术
  • 未来市场趋势
  • 投资机会

第十二章 附录

  • 简称列表
  • 来源和参考文献
简介目录
Product Code: 69189

AI In Asset Management Market Size And Forecast

AI In Asset Management Market size was valued at USD 2.78 Billion in 2024 and is projected to reach USD 47.58 Billion by 2032, growing at a CAGR of 34.37% from 2026 to 2032.

AI in asset management is the application of advanced algorithms and machine learning techniques to manage and optimize financial assets.

This technology is anticipated to enhance decision-making processes, improve predictive analytics, and facilitate more efficient portfolio management.

The applications of AI in asset management are diverse and expanding rapidly. Automated trading systems, risk assessment tools, and portfolio optimization models are among the key areas where AI is being utilized.

By leveraging AI, asset managers are expected to achieve higher accuracy in forecasting market trends, better align investment strategies with client goals, and streamline operational efficiencies.

The growth of AI in asset management is anticipated to be driven by several factors. The increasing complexity of financial markets and the growing demand for personalized investment solutions are expected to propel the adoption of AI technologies.

Additionally, advancements in AI capabilities and the rising availability of big data are likely to further fuel the expansion of AI applications in this sector.

Global AI In Asset Management Market Dynamics

The key market dynamics that are shaping the global AI in asset management market include:

Key Market Drivers:

Complexity of Financial Markets: The increasing complexity of financial markets is expected to drive the demand for AI in asset management. AI technologies are anticipated to be increasingly integrated to manage intricate financial instruments and diverse asset classes, thereby enhancing decision-making processes.

Demand for Personalized Investment Solutions: The growing demand for personalized investment solutions is projected to boost the adoption of AI in asset management. AI tools are likely to be utilized to tailor investment strategies to individual client preferences and risk profiles, improving client satisfaction and portfolio performance. A survey by Deloitte in 2023 found that 72% of asset management firms were investing in AI and machine learning to deliver more personalized investment solutions. Additionally, the robo-advisory market, which heavily relies on AI, was valued at $18.4 billion in 2023 and is expected to grow at a CAGR of 31.8% from 2024 to 2030.

Availability of Big Data: The rising availability of big data is anticipated to fuel the growth of AI applications in asset management. Enhanced data sources are expected to enable more accurate predictive analytics and risk assessments, leading to better-informed investment decisions.

Advancements in AI Technologies: Continuous advancements in AI technologies are expected to contribute to the expansion of AI in asset management. Innovations such as improved machine learning algorithms and sophisticated analytical tools are likely to drive efficiency and effectiveness in asset management practices.

Key Challenges:

Data Security Concerns: Data security concerns are expected to hamper the adoption of AI in asset management. The risks associated with data breaches and cyberattacks are anticipated to inhibit the widespread implementation of AI technologies in managing sensitive financial information.

High Implementation Costs: The high implementation costs of AI technologies are projected to restrain their adoption in asset management. Significant investments are likely to be required for developing, integrating, and maintaining advanced AI systems, which may limit their accessibility to smaller firms.

Regulatory and Compliance Challenges: Regulatory and compliance challenges are anticipated to impede the growth of AI in asset management. Stringent financial regulations and the need for adherence to data privacy laws are expected to complicate the deployment and operation of AI solutions in the sector.

Limited AI Expertise: The limited availability of AI expertise is expected to restrain the effective integration of AI in asset management. The shortage of skilled professionals who can develop and manage AI systems is anticipated to hinder the adoption and optimization of these technologies.

Key Trends:

Adoption of Machine Learning Algorithms: The growing adoption of machine learning algorithms is expected to be a significant trend in the AI in asset management market. These algorithms are anticipated to enhance predictive analytics and decision-making capabilities, providing more accurate investment insights and strategies.

Use of Natural Language Processing (NLP): The increasing use of natural language processing (NLP) is projected to transform client interactions and data analysis in asset management. NLP technologies are likely to be integrated to improve the interpretation of financial news, reports, and market sentiment, thereby refining investment strategies.

Focus on Regulatory Technology (RegTech): A high focus on regulatory technology (RegTech) is anticipated to shape the AI in asset management landscape. AI solutions designed for regulatory compliance are expected to become more prevalent, helping firms navigate complex regulations and mitigate compliance risks.

Implementation of Robo-Advisors: The rising implementation of robo-advisors is expected to be a key trend in the market. Robo-advisors are anticipated to offer automated, algorithm-driven financial planning services, making investment management more accessible and cost-effective for a broader range of clients.

Global AI In Asset Management Market Regional Analysis

Here is a more detailed regional analysis of the global AI in asset management market:

North America:

According to Verified Market Research Analyst, North America is projected to dominate the AI in asset management market.

The region is expected to maintain a leading position due to its advanced financial infrastructure, high adoption rates of cutting-edge technologies, and substantial investment in AI research and development. T

he presence of major financial institutions and technology companies is anticipated to further drive the growth of AI applications in asset management. Additionally, favorable regulatory environments and a strong focus on innovation are likely to support the continued dominance of North America in this sector.

Asia Pacific:

According to Verified Market Research Analyst, Asia Pacific is estimated to be rapidly growing in the AI in asset management market.

The region is expected to experience significant growth due to its expanding financial markets, increasing adoption of AI technologies, and rising investments in digital transformation.

Rapid economic development, coupled with a growing number of high-net-worth individuals, is anticipated to drive the demand for advanced asset management solutions.

Moreover, governments in Asia Pacific are likely to support the adoption of AI through various initiatives and incentives, contributing to the rapid expansion of the market.

The Asia Pacific region has experienced a notable increase in the adoption of digital financial services, fostering a conducive environment for AI-driven asset management solutions.

A

ccording to a study conducted by Google, Temasek, and Bain & Company, the number of digital financial services users in Southeast Asia surged from 140 million in 2019 to 310 million by 2023. This significant growth in digital engagement has created ample opportunities for AI-powered asset management platforms to expand and gain prominence across the region.

Global AI In Asset Management Market Segmentation Analysis

The Global AI In Asset Management Market is Segmented on the basis of Technology, Deployment Mode, Application, And Geography.

AI In Asset Management Market, By Technology

  • Machine Learning
  • Natural Language Processing (NLP)

Based on Technology, the market is bifurcated into Machine Learning and Natural Language Processing (NLP). Machine learning is expected to hold the largest share of the technology segment in the AI in asset management market. The substantial growth of this segment is anticipated to be driven by the increasing adoption of machine learning algorithms for predictive analytics and investment strategies. Machine learning models are projected to enhance the accuracy of financial forecasts and risk assessments by analyzing vast amounts of data with greater precision.

AI In Asset Management Market, By Deployment Mode

  • On-Premises
  • Cloud

Based on Deployment Mode, the Global AI in Asset Management Market is divided into On-Premises and Cloud. Cloud deployment mode is estimated to hold the largest share of the AI in asset management market. This growth is expected to be driven by the increasing preference for scalable and flexible solutions offered by cloud-based platforms. Cloud deployment is anticipated to facilitate cost-effective implementation of AI technologies by reducing the need for significant upfront investments in hardware and infrastructure.

AI In Asset Management Market, By Application

  • Portfolio Optimization
  • Conversational Platform
  • Risk & Compliance
  • Data Analysis
  • Process Automation

Based on Application, the market is segmented into Portfolio Optimization, Conversational Platform, Risk & Compliance, Data Analysis, and Process Automation. Portfolio Optimization has held the largest share of the AI in asset management market. The growth of this segment is expected to be driven by the increasing need for advanced strategies to enhance investment performance and manage diverse asset classes efficiently. AI technologies are anticipated to provide sophisticated algorithms that analyze market data and optimize portfolio allocations to achieve better returns.

AI In Asset Management Market, By Geography

  • North America
  • Europe
  • Asia Pacific
  • Rest of the World
  • On the basis of Geography, the Global AI in Asset Management Market is classified into North America, Europe, Asia Pacific, and the Rest of the World. North America held the largest share of the AI in asset management market and is expected to continue its dominance. The region is anticipated to experience substantial growth due to its well-established financial sector, high levels of technological adoption, and substantial investment in AI innovations. The presence of major financial institutions and technology firms in North America is projected to drive the development and deployment of advanced AI solutions.

Key Players

The "Global AI In Asset Management Market" study report will provide valuable insight with an emphasis on the global market. The major players in the market are BlackRock, Vanguard Group, State Street Corporation, Fidelity Investments, Goldman Sachs Group, Inc., JPMorgan Chase & Co., IBM, Microsoft, Google, Palantir Technologies, Inc., AlphaSense, Kensho Technologies, Quantiacs, and Axioma.

Our market analysis also entails a section solely dedicated to such major players wherein our analysts provide an insight into the financial statements of all the major players, along with its product benchmarking and SWOT analysis. The competitive landscape section also includes key development strategies, market share, and market ranking analysis of the above-mentioned players globally.

  • AI In Asset Management Market Recent Developments
  • In March 2023, NVIDIA Corporation, a leading American multinational technology firm, unveiled NVIDIA DGX Cloud, an advanced AI supercomputing service. This service enables users to access powerful AI supercomputers through highly tailored web browsers.
  • In February 2023, Arcadis, a prominent entity in natural and built asset management, entered into a partnership with digital technology provider Niricson. Niricson specializes in utilizing robotics, computer vision, and acoustic technologies, combined with artificial intelligence, to deliver predictive asset management and condition assessments for concrete infrastructure such as bridges.
  • In March 2022, Baker Hughes, a leader in energy technology, formed a strategic alliance with C3 AI, Accenture, and Microsoft to enhance industrial asset management (IAM) solutions for clients in the energy and industrial sectors. This collaboration aims to advance the safety, operational efficiency, and emissions performance of industrial machinery, field equipment, and other critical assets.
  • In March 2023, Accenture PLC announced its decision to acquire Flutura, an AI solutions provider based in Bangalore. This acquisition is aimed at enhancing Accenture's industrial AI capabilities to boost the efficiency of refineries, manufacturing plants, and supply chains. It is also expected to support clients in achieving their net zero objectives more rapidly.
  • In February 2023, EagleView Technologies, Inc., a leading provider of aerial imagery, software, and analytics, introduced its latest asset management solutions.
  • In February 2023, Scotiabank, a prominent Canadian multinational banking and financial services firm, introduced Scotia Smart Investor. This new tool is designed to provide clients with enhanced control over their investment decisions.

TABLE OF CONTENTS

1. Introduction

  • Market Definition
  • Market Segmentation
  • Research Methodology

2. Executive Summary

  • Key Findings
  • Market Overview
  • Market Highlights

3. Market Overview

  • Market Size and Growth Potential
  • Market Trends
  • Market Drivers
  • Market Restraints
  • Market Opportunities
  • Porter's Five Forces Analysis

4. AI In Asset Management Market, By Technology

  • Machine Learning
  • Natural Language Processing (NLP)

5. AI In Asset Management Market, By Deployment Mode

  • On-Premises
  • Cloud

6. AI In Asset Management Market, By Application

  • Portfolio Optimization
  • Conversational Platform
  • Risk & Compliance
  • Data Analysis
  • Process Automation

7. Regional Analysis

  • North America
  • United States
  • Canada
  • Mexico
  • Europe
  • United Kingdom
  • Germany
  • France
  • Italy
  • Asia-Pacific
  • China
  • Japan
  • India
  • Australia
  • Latin America
  • Brazil
  • Argentina
  • Chile
  • Middle East and Africa
  • South Africa
  • Saudi Arabia
  • UAE

8. Market Dynamics

  • Market Drivers
  • Market Restraints
  • Market Opportunities
  • Impact of COVID-19 on the Market

9. Competitive Landscape

  • Key Players
  • Market Share Analysis

10. Company Profiles

  • BlackRock
  • Vanguard Group
  • State Street Corporation
  • Fidelity Investments
  • Goldman Sachs Group, Inc.
  • JPMorgan Chase & Co.
  • IBM
  • Microsoft
  • Google
  • Palantir Technologies, Inc.
  • AlphaSense
  • Kensho Technologies
  • Quantiacs
  • Axioma

11. Market Outlook and Opportunities

  • Emerging Technologies
  • Future Market Trends
  • Investment Opportunities

12. Appendix

  • List of Abbreviations
  • Sources and References