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資産管理におけるAIの世界市場規模：技術別、展開モード別、用途別、地域範囲別、予測

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
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英文 202 Pages
納期
2～3営業日
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価格表記: USDを日本円(税抜)に換算
本日の銀行送金レート: 1USD=143.83円
資産管理におけるAIの世界市場規模：技術別、展開モード別、用途別、地域範囲別、予測
出版日: 2025年05月13日
発行: Verified Market Research
ページ情報: 英文 202 Pages
納期: 2～3営業日
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概要

資産管理におけるAI市場規模と予測

資産管理におけるAI市場規模は、2024年に27億8,000万米ドルと評価され、2026～2032年にかけて34.37%のCAGRで成長し、2032年には475億8,000万米ドルに達すると予測されます。

資産管理におけるAIは、金融資産の管理と最適化のための先進的アルゴリズムと機械学習技術の応用です。

この技術は、意思決定プロセスを強化し、予測分析を改善し、より効率的なポートフォリオ管理を促進すると予想されます。

資産管理におけるAIの用途は多岐にわたり、急速に拡大しています。自動取引システム、リスク評価ツール、ポートフォリオ最適化モデルなどは、AIが活用されている主要セグメントのひとつです。

AIを活用することで、資産管理会社は市場動向の予測精度を高め、投資戦略を顧客の目標により合致させ、運用効率を効率化することが期待されています。

資産管理におけるAIの成長は、いくつかの要因によって牽引されると予想されます。金融市場の複雑化と個別化された投資ソリューションに対する需要の高まりが、AI技術の採用を促進すると予想されます。

さらに、AI機能の進歩とビッグデータの利用可能性の高まりが、このセグメントでのAI用途の拡大にさらに拍車をかける可能性が高いです。

世界の資産管理におけるAI市場力学

世界の資産管理におけるAI市場を形成している主要市場力学は以下の通りです。

主要市場促進要因

金融市場の複雑化：金融市場の複雑化：金融市場の複雑化は、資産管理におけるAIの需要を促進すると予想されます。AI技術は、複雑な金融商品や多様な資産クラスを管理するためにますます統合され、それによって意思決定プロセスが強化されると予想されます。

パーソナライズされた投資ソリューションへの需要：パーソナライズされた投資ソリューションに対する需要の高まりが、資産管理におけるAIの採用を後押しすると予測されます。AIツールは、個々の顧客の嗜好やリスクプロファイルに合わせて投資戦略を調整し、顧客満足度とポートフォリオのパフォーマンスを向上させるために活用される可能性が高いです。デロイトが2023年に実施した調査では、資産管理会社の72％が、よりパーソナライズされた投資ソリューションを提供するためにAIや機械学習に投資していることが分かりました。さらに、AIに大きく依存するロボアドバイザリー市場の2023年の市場規模は184億米ドルで、2024～2030年のCAGRは31.8%で成長すると予想されています。

ビッグデータの利用可能性：ビッグデータの利用可能性の高まりは、資産管理におけるAIアプリケーションの成長を促進すると予想されます。データソースの強化により、より正確な予測分析とリスク評価が可能になり、より的確な情報による投資判断につながると期待されます。

AI技術の進歩：AI技術の継続的な進歩は、資産管理におけるAIの拡大に貢献すると予想されます。改良された機械学習アルゴリズムや先進的分析ツールなどのイノベーションは、資産管理業務の効率性と有効性を促進すると考えられます。

主要課題

データセキュリティの懸念：データセキュリティへの懸念は、資産管理におけるAIの採用を妨げると予想されます。データ漏洩やサイバー攻撃に関連するリスクは、機密性の高い金融情報の管理におけるAI技術の広範な導入を阻害すると予想されます。

高い導入コスト：AI技術の導入コストが高いため、資産管理への導入が抑制されると予測されます。先進的なAIシステムの開発、統合、維持には多額の投資が必要となる可能性が高く、中小企業への導入が制限される可能性があります。

規制とコンプライアンスの課題：規制とコンプライアンスの課題は、資産管理におけるAIの成長を阻害すると予想されます。厳しい金融規制やデータプライバシー法の遵守の必要性は、このセグメントにおけるAIソリューションの展開と運用を複雑にすると予想されます。

限られたAI専門知識：AIの専門知識が限られていることが、資産管理におけるAIの効果的な統合を抑制すると予想されます。AIシステムを開発・管理できる熟練した専門家の不足が、こうした技術の採用と最適化を妨げると予想されます。

主要動向

機械学習アルゴリズムを発表機械学習アルゴリズムの採用拡大が、資産管理におけるAI市場の重要な動向になると予想されます。これらのアルゴリズムは、予測分析と意思決定能力を強化し、より正確な投資洞察と戦略を提供すると予想されます。

自然言語処理（NLP）の利用：自然言語処理（NLP）の利用の増加は、資産管理における顧客とのやり取りやデータ分析を変革すると予測されます。NLP技術は、金融ニュースやレポート、市場センチメントの解釈を改善し、投資戦略を洗練させるために統合される可能性が高いです。

規制技術（RegTech）への注力：規制技術（RegTech）への高い注目が、資産管理におけるAIの状況を形成すると予想されます。規制遵守のために設計されたAIソリューションは、企業が複雑な規制をナビゲートし、コンプライアンスリスクを軽減するのに役立ち、より普及すると予想されます。

ロボアドバイザーの導入：ロボアドバイザーの導入拡大が市場の主要動向になると予想されます。ロボアドバイザーは、自動化されたアルゴリズム主導のファイナンシャル・プランニングサービスを提供し、幅広い顧客層にとって投資管理をより身近で費用対効果の高いものにすると予想されます。

目次

第1章 イントロダクション

  • 市場の定義
  • 市場セグメンテーション
  • 調査手法

第2章 エグゼクティブサマリー

  • 主要調査結果
  • 市場概要
  • 市場ハイライト

第3章 市場概要

  • 市場規模と成長の可能性
  • 市場動向
  • 市場促進要因
  • 市場抑制要因
  • 市場機会
  • ポーターのファイブフォース分析

第4章 資産管理におけるAI市場：技術別

  • 機械学習
  • 自然言語処理（NLP）

第5章 資産管理におけるAI市場：展開モード別

  • オンプレミス
  • クラウド

第6章 資産管理におけるAI市場：用途別

  • ポートフォリオ最適化
  • 会話プラットフォーム
  • リスクコンプライアンス
  • データ分析
  • プロセス自動化

第7章 地域分析

  • 北米
  • 米国
  • カナダ
  • メキシコ
  • 欧州
  • 英国
  • ドイツ
  • フランス
  • イタリア
  • アジア太平洋
  • 中国
  • 日本
  • インド
  • オーストラリア
  • ラテンアメリカ
  • ブラジル
  • アルゼンチン
  • チリ
  • 中東・アフリカ
  • 南アフリカ
  • サウジアラビア
  • アラブ首長国連邦

第8章 市場力学

  • 市場促進要因
  • 市場抑制要因
  • 市場機会
  • COVID-19の市場への影響

第9章 競合情勢

  • 主要企業
  • 市場シェア分析

第10章 企業プロファイル

  • 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 市場展望と機会

  • 新興技術
  • 今後の市場動向
  • 投資機会

第12章 付録

  • 略語リスト
  • 供給源と参考文献
目次
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