Global AI In Asset Management Market Size By Technology (Machine Learning, Natural Language Processing (NLP)), By Deployment Mode (On-Premises, Cloud), Application (Portfolio Optimization, Conversational Platform, Risk & Compliance, Data Analysis, Process Automation), By Geographic Scope And Forecast

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 2031, growing at a CAGR of 34.37% from 2024 to 2031.

• 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.

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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.

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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.
• According 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.

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