The global AI in asset management market size was valued at USD 2.78 Billion in 2022 and is projected to reach USD 47.58 Bn by 2031, expanding at a CAGR of 37.1% during the forecast period, 2023–2031. The growth of the market is attributed to the increasing adoption of Artificial intelligence (AI) in the wealth management and asset management industry.
Asset management is a process of controlling the assets for the company to achieve efficient management of resources and obtain good results. The use of AI has made the process advanced and produces effective results with less time and effort. The integration of artificial intelligence with wealth and asset management offers several benefits, such as better customer experience and interfaces, improved operational efficiency, and investment processes.
The AI model analyzes the large quantity of real-time data of financial transactions of the company to produce efficient reports. Major applications of AI to improve operational efficiency include quality checking, monitoring, and exception handling of the large quantity of data on financial instruments.
Optimizing data quality is of the highest importance as it decreases operational risks and assists in client retention. Researchers have made incredible progress in achieving the ultimate human-machine interaction systems in the past few years.
AI is used to capture text, audio, and imagery data from several vendor/internal databases and public sources by applying NLP (natural language processing), computer vision, and voice recognition programs. More advanced programs are expected to process the information gathered from different sources to produce insights for investment decision-making methods. This usually needs advanced AI techniques, such as deep learning and machine learning.
The COVID-19 pandemic, demonstrated a tremendous rise in deployments and relocations of equipment and devices, as millions of employees were forced by companies to work from home environments. The ability to deploy, procure, and manage hardware assets has become substantially complex, with the firmness of bringing thousands of remote devices online. However, the businesses that persist in influencing the use of artificial intelligence are estimated to take this pandemic as an opportunity.
AI in Asset Management Market Dynamics
Major Drivers
AI-enabled solutions, such as Chabot or conversational platforms have enhanced customer interactions and related services and are driving the market for fast pace growth in the coming future. Favorable government initiatives for the implementation of AI are propelling the market in the forecast period. Constantly rising data volumes, low interest rates, and strict regulations are stimulating asset managers to modify their traditional business strategies, which is propelling the market in the forecast period.
Exiting Restraints
The connection of domain-enriched ML (machine learning), knowledge, and NLP techniques is being adopted by various organizations to provide enhanced investment and financial services pushing the market for considerable growth in the projected period.
Emerging Opportunities
The increasing acceptance of AI technology in the world demonstrates huge opportunities for the market in the coming years. Artificial intelligence is revolutionizing asset management solutions with its decision-making, improved data analysis, and other solutions. Moreover, the increasing adoption of AI by several technology companies to maintain their asset-related concerns and launch solutions to tackle them is expected to create opportunities for the market.
- For instance, in February 2023, EagleView, an aerial imagery, software, and analytics provider, announced the launch of its next-generation asset management solutions. The company is expected to solve several challenges related to commercial organizations and local governments.
Scope of AI in Asset Management Market Report
The report on the global AI in asset management market includes an assessment of the market, trends, segments, and regional markets. Overview and dynamics have also been included in the report.
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Attributes
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Details
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Report Title
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AI in Asset Management Market - Global Industry Analysis, Growth, Share, Size, Trends, and Forecast
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Base Year
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2022
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Historic Data
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2016–2021
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Forecast Period
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2023–2031
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Segmentation
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Technology (Machine Learning, Natural Language Processing, and Others), Deployment Mode (On-premise and Cloud), Application (Portfolio Optimization, Conversational Platform, Risk & Compliance, Data Analysis, Process Automation, and Others), Vertical (BFSI, Healthcare, Retail & E-commerce, Energy & Utilities, Media & Entertainment, Automotive, and Others),
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Regional Scope
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Asia Pacific, North America, Latin America, Europe, and Middle East & Africa
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Report Coverage
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Company Share, Market Analysis and Size, Competitive Landscape, Growth Factors, and Trends, and Revenue Forecast
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Key Players Covered in the Report
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Amazon Web Services, Inc.; BlackRock, Inc.; CapitalG; Charles Schwab & Co., Inc.; Genpact; Infosys Limited; International Business Machines Corporation; IPsoft Inc.; Lexalytics; Microsoft; Narrative Science; Next IT Corp.; S&P Global; Salesforce.com, Inc.; Niricson Software Inc; Accenture.; C3.ai, Inc.
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AI in Asset Management Market Segment Insights
Technology Segment Analysis
Based on technology, the global AI in asset management market is divided into machine learning, natural language processing, and others. The machine learning segment is expected to grow at a rapid pace. The segment showed a revenue share of 65% in the year 2019. This is because of the rising automation processes in manufacturing industries.
Machine learning (ML) is the natural evolution of technology as machines have the ability to sort huge datasets as well as extract information by detecting outliers and patterns. Machine learning is used to identify patterns in structured and unstructured data to provide actionable insights to allow investment-related decision-making capability.
Moreover, machine learning assists in identifying the correlations between world events along with their impact on the costs of assets, resulting in enhanced decision-making in asset management. ML is applied in asset management systems to increase the efficiency and accuracy of operational workflow, improve the system performance, and enhance the customer experience.
Deployment Modes Segment Analysis
On the basis of deployment mode, the global market is segregated into on-premise and cloud. The on-premise segment is projected to expand at a considerable CAGR. The segment generated a revenue share of greater than 60.1% in 2019. This is attributed to the privacy and security offered by the on-premise solutions in asset management.
Furthermore, on-premise solutions utilize edge analytics that decline the bandwidth need. Collaborating these solutions on-premise offers more reliability and higher speed in the results.
The cloud segment is estimated to gain significant growth in the near future. The development is attributed to the benefits offered by cloud solutions to eliminate firewall restrictions, which can impact the users’ access to an on-premise solution. The cloud-based software-as-a-service solution excludes the overhead costs and maintenance. Furthermore, cloud object storage services offer virtually unrestricted storage that eliminates the storage and scalability volume restrictions of locally placed hardware.
Applications Segment Analysis
On the basis of application, the global market is segmented into portfolio optimization, conversational platform, risk & compliance, data analysis, process automation, and others. The portfolio optimization segment is estimated to grow at a significant rate. The segment recorded a revenue share of more than 25.1% in the year 2019. This is accounted for the great acceptance of machine learning applications in asset management to enable decisions based on portfolio management.
The conversational platform segment is estimated to generate substantial revenue in the near future. The growth is accounted to the quick adoption of chatbots. The extensive accessibility of mobile extensions along with an integration of speech, text, and touch interfaces has boosted the launch of new services to a client or consumers. Furthermore, companies can offer personalized and differentiated customer responses with the use of conversational AI platforms.
The portfolio optimization comprises several use cases, such as portfolio optimization and construction, development of strategies for risks associated with investments, and predictive forecasting of long-term price analysis. For portfolio optimization in the investment process, a prototype is prepared based on the portfolio management process, stock selection, and asset allocation optimization.
Verticals Segment Analysis
Based on vertical, the global AI in asset management market is divided into BFSI, healthcare, retail & e-commerce, energy & utilities, media & entertainment, automotive, and others. The BFSI segment is expected to grow at a rapid pace. The segment showed a revenue share of 20% in the year 2019. This is accounted to the fast acceptance of AI in asset management systems in the banking sector and financial services.
There are various applications of AI in financial services, including stewardship and alpha generation in risk management, asset management, relationship manager augmentation, fraud detection, and algorithmic trading.
The platform serves several industries, such as market research, retail and e-commerce, financial services, marketing and advertising, biotechnology and healthcare, restaurants/food services, pharmaceuticals, and airlines and airports. Besides, infrastructure and building, consumer goods, transportation, and hospitality and travel are other sectors in which artificial intelligence is used for asset management applications.
Regional Analysis
In terms of region, the global AI in asset management market is classified as Asia Pacific, North America, Latin America, Europe, and Middle East & Africa.
North America is anticipated to constitute a key market share. The market of the region showed a revenue share of greater than 50% in the year 2019. The favorable government policies stimulate the acceptance of AI in several industries. For example, In February 2019, President Donald J. Trump of the U.S. introduced the American AI initiative as a policy to increase the use of AI in the region and emerge as a leader in the world for AI use.
Asia Pacific market of the region is expected to grow at a considerable rate. This is because of the heavy investment by the major market players in the region. The increasing number of start-ups in the region is a key driving factor for the market in the region.
Segments
The global AI in asset management market has been segmented on the basis of
Technology
- Machine Learning
- Natural Language Processing
- Others
Deployment Mode
Application
- Portfolio Optimization
- Conversational Platform
- Risk & Compliance
- Data Analysis
- Process Automation
- Others
Vertical
- BFSI
- Healthcare
- Retail & E-commerce
- Energy & Utilities
- Media & Entertainment
- Automotive
- Others
Region
- Asia Pacific
- North America
- Latin America
- Europe
- Middle East & Africa
Key Players
- Amazon Web Services, Inc.
- BlackRock, Inc.
- CapitalG
- Charles Schwab & Co., Inc
- Genpact
- Infosys Limited
- International Business Machines Corporation
- IPsoft Inc.
- Lexalytics
- Microsoft
- Narrative Science
- Next IT Corp.
- S&P Global
- Salesforce.com, Inc.
- Niricson Software Inc
- Accenture.
- C3.ai, Inc.
Competitive Landscape
The key players in the global AI in asset management market are Amazon Web Services, Inc.; BlackRock, Inc.; Microsoft; CapitalG; Infosys Limited; Lexalytics; Charles Schwab & Co., Inc.; International Business Machines Corporation; IPsoft Inc.; Genpact; Next IT Corp.; Narrative Science; S&P Global; and Salesforce.com, Inc.
These key players have adopted a series of market strategies including new product launching, entering into partnerships, collaboration, and production expansion to enhance their market position and expand their consumer base.
- In February 2023, In February 2023, Arcadis, a leading organization for natural and built assets, started a collaboration with digital technology provider Niricson. Niricson works in using robotics, computer vision, and acoustic technology combined with AI, to provide predictive asset management and condition assessments for bridges and other concrete infrastructure. The collaboration focuses on allowing Arcadis to use the technology for bridge inspections in several key markets, including the US, Canada, the UK, and Australia.
- In March 2022, Energy technology company Baker Hughes collaborated with C3 AI, Accenture, and Microsoft on industrial asset management (IAM) solutions for clients in the energy and industrial sectors. The collaboration aims at creating and installing Baker Hughes IAM solutions that use digital technologies to help improve the safety, efficiency, and emissions profile of industrial machines, field equipment, and other assets.
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