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市場調查報告書
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1733821

全球資產管理人工智慧市場規模（按技術、部署模式、應用、區域範圍和預測）：

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個工作天內

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簡介目錄

資產管理人工智慧的市場規模與預測

預計2024年資產管理人工智慧市場規模將達27.8億美元，到2032年將達到475.8億美元，在2026-2032年預測期間的複合年成長率為34.37%。

資產管理中的人工智慧是應用先進的演算法和機器學習技術來管理和最佳化金融資產。

該技術可望增強決策流程，改善預測分析，並促進更有效率的投資組合管理。

人工智慧在資產管理的應用多種多樣且成長迅速。自動交易系統、風險評估工具和投資組合最佳化模型是人工智慧應用的主要領域。

透過利用人工智慧，資產管理公司有望提高市場趨勢預測的準確性，更好地使投資策略與客戶目標保持一致，並簡化營運效率。

預計資產管理領域人工智慧的成長將受到多種因素的推動。金融市場日益複雜以及對個人化投資解決方案的需求不斷成長預計將推動人工智慧技術的採用。

此外，人工智慧能力的進步和巨量資料可用性的提高可能會進一步刺激該領域人工智慧應用的擴展。

全球資產管理人工智慧市場動態

影響全球資產管理人工智慧市場的關鍵市場動態是：

關鍵市場促進因素

金融市場日益複雜：金融市場日益複雜：金融市場日益複雜預計將推動資產管理對人工智慧的需求。預計人工智慧技術將擴大融入管理複雜的金融產品和多樣化的資產類別，從而增強決策流程。

個人化投資解決方案的需求：個人化投資解決方案的需求不斷成長，預計將推動人工智慧在財富管理中的應用。人工智慧工具可能會被用來根據個人客戶偏好和風險狀況來客製化投資策略，從而提高客戶滿意度和投資組合績效。德勤 2023 年的一項調查發現，72% 的資產管理公司正在投資人工智慧和機器學習，以提供更個人化的投資解決方案。此外，嚴重依賴人工智慧的機器人諮詢市場預計將在 2023 年達到 184 億美元，2024 年至 2030 年的複合年成長率為 31.8%。

巨量資料的可用性：巨量資料的日益普及預計將推動資產管理領域人工智慧應用的成長。增強的資料來源將實現更準確的預測分析和風險評估，並有望帶來更明智的投資決策。

人工智慧技術的進步：人工智慧技術的持續進步有望促進人工智慧在資產管理領域的擴展。改進的機器學習演算法和先進的分析工具等創新將提高資產管理業務的效率和效力。

主要問題

資料安全問題：資料安全問題預計會阻礙人工智慧在資產管理中的應用。預計與資料外洩和網路攻擊相關的風險將阻礙人工智慧技術在敏感金融資訊管理中的廣泛應用。

實施成本高：人工智慧技術實施成本高預計會阻礙其在資產管理上的應用。開發、整合和維護先進的人工智慧系統可能需要大量投資，這可能會限制其在中小企業中的應用。

監管和合規挑戰：監管和合規挑戰預計將阻礙人工智慧在資產管理領域的發展。需要遵守嚴格的金融法規和資料隱私法，預計會使該領域人工智慧解決方案的部署和運作變得複雜。

有限的人工智慧專業知識：有限的人工智慧專業知識預計會阻礙人工智慧在資產管理中的有效整合。預計缺乏能夠開發和管理人工智慧系統的熟練專業人員將阻礙這些技術的採用和最佳化。

主要趨勢

機器學習演算法推出機器學習演算法的日益普及預計將成為資產管理市場人工智慧的關鍵趨勢。這些演算法有望增強預測分析和決策能力，提供更準確的投資見解和策略。

自然語言處理 (NLP) 的使用：自然語言處理 (NLP) 的使用日益增多，有望改變財富管理中的客戶互動和數據分析。 NLP 技術可能會被整合以改善對金融新聞和報告、市場情緒的解讀，並完善投資策略。

關注監管科技：對監管科技的高度關注預計將塑造資產管理領域人工智慧的格局。專為法規遵從而設計的人工智慧解決方案預計將變得更加普遍，因為它們可以幫助公司應對複雜的法規並降低合規風險。

機器人顧問的採用：機器人顧問的日益普及預計將成為主要的市場趨勢。機器人顧問提供自動化、演算法主導的財務規劃服務，預計會讓更廣大的客戶更容易、更經濟地進行投資管理。

目錄

第1章 引言

  • 市場定義
  • 市場區隔
  • 調查方法

第2章執行摘要

  • 主要發現
  • 市場概覽
  • 市場亮點

第3章市場概述

  • 市場規模和成長潛力
  • 市場趨勢
  • 市場促進因素
  • 市場限制
  • 市場機會
  • 波特五力分析

第4章 資產管理市場中的人工智慧（按技術）

  • 機器學習
  • 自然語言處理（NLP）

第5章 資產管理市場中的人工智慧（依部署模式）

  • 本地
  • 雲

第6章 資產管理中的人工智慧市場（按應用）

  • 投資組合最佳化
  • 對話平台
  • 風險合規
  • 數據分析
  • 流程自動化

第7章區域分析

  • 北美洲
  • 美國
  • 加拿大
  • 墨西哥
  • 歐洲
  • 英國
  • 德國
  • 法國
  • 義大利
  • 亞太地區
  • 中國
  • 日本
  • 印度
  • 澳洲
  • 拉丁美洲
  • 巴西
  • 阿根廷
  • 智利
  • 中東和非洲
  • 南非
  • 沙烏地阿拉伯
  • 阿拉伯聯合大公國

第8章市場動態

  • 市場促進因素
  • 市場限制
  • 市場機會
  • COVID-19 市場影響

第9章 競爭態勢

  • 主要企業
  • 市場佔有率分析

第10章 公司簡介

  • 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11章 市場展望與機會

  • 新興技術
  • 未來市場趨勢
  • 投資機會

第12章 附錄

  • 簡稱列表
  • 來源和參考文獻
簡介目錄
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