The global AI in asset management market size was estimated at USD 1.48 billion in 2020 and is anticipated to expand at a CAGR of 37.1% during the forecast period, 2021–2028. The growth of the market is attributed to the increasing adoption of Artificial intelligence (AI) in the wealth management industry and asset management.
The 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 AI model analyzes the large quantity of real-time data of financial transactions of the company to produce efficient reports. The integration of artificial intelligence with the wealth and asset management offers several benefits, such as better customer experience and interfaces, improving the operational efficiency, and investment processes.
Major applications of AI to incline the operational efficiency include quality checking, monitoring, and the exception handling of the large quantity of data on financial instruments. Optimizing data quality is of the highest importance as it decreases the operational risks and assists in client retention. Researchers have made incredible progress in achieving the ultimate human-machine interaction systems in 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 will process the information gathered from different sources to produce insights for the investment decision-making methods. This usually needs advanced AI techniques, such as deep learning and machine learning.
The COVID-19 pandemic, demonstrated tremendous rise in deployments and relocations of equipment and devices, as millions of employees forced by the 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 on-line. Though, the businesses that persists to influence the use of artificial intelligence are estimated to take this pandemic as an opportunity.
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.
Attributes |
Details |
Report Title |
AI in Asset Management Market - Global Industry Analysis, Growth, Share, Size, Trends, and Forecast |
Base Year |
2020 |
Historic Data |
2018–2019 |
Forecast Period |
2021–2028 |
Segmentation |
Technology (Machine Learning, Natural Language Processing, and Others), Deployment Modes (On-premises and Cloud), Applications (Portfolio Optimization, Conversational Platform, Risk & Compliance, Data Analysis, Process Automation, and Others), Verticals (BFSI, Healthcare, Retail & E-commerce, Energy & Utilities, Media & Entertainment, Automotive, and Others), |
Regional Scope |
Asia Pacific, North America, Latin America, Europe, and Middle East & Africa |
Report Coverage |
Company Share, Market Analysis and Size, Competitive Landscape, Growth Factors, and Trends, and Revenue Forecast |
Key Players Covered in the Report |
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. |
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 revenue share of 65% in year 2019. This is because of the rising automation of process in manufacturing industries. Machine learning (ML) the natural evolution of technology as machines has the ability to sort the huge datasets as well as extract information by detecting outliers and patterns.
ML is applied in the asset management systems to incline the efficiency and accuracy of operational workflow, improve the system performance, and enhance the customer experience. Machine learning is used to identify patterns in structured and unstructured data to provide actionable insights to allow the investment-related decision-making capability. Moreover, machine learning assist in identifying the correlations between world events along with their impact on costs of assets, resulting into enhanced decision-making in asset management.
On the basis of deployment modes, the global market is segregated into on-premises and cloud. The on-premises segment is projected to expand at a considerable CAGR. The segment generated revenue share of greater than 60.1% in 2019. This is attributed to the privacy and security offered by the on-premises solutions in asset management. Furthermore, on-premise solutions utilize edge analytics that declines 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 the firewall restrictions, which can impact the users’ access to an on-premise solution. The cloud-based software-as-a-service solution exclude the overhead costs and maintenance. Furthermore, the cloud object storage services offers the virtually unrestricted storage that eliminates the storage and scalability volume restrictions of locally-placed hardware.
On the basis of applications, 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 the revenue share of more than 25.1% in year 2019. This is accounted for the great acceptance of machine learning applications in asset management to enable decisions based on portfolio management.
The portfolio optimization comprises of 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 portfolio management process, stock selection, and asset allocation optimization.
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 accessability of mobile extension along with a 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.
Based on verticals, 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 revenue share of 20% in 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.
In terms of regions, 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 the revenue share of greater than 50% in year 2019. The favorable government policies to stimulate the acceptance of AI around several industries. For example, In February 2019, President Donald J. Trump of the U.S. introduced the American AI initiative as the policy to increase the use of AI in the region and emerge as a leader in the world for the AI use.
Asia Pacific market of the region is expected to growth 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 Covered in the Report
The global AI in asset management market has been segmented on the basis of
Technology
Deployment Modes
Applications
Verticals
Regions
Key Players
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 partnership, collaboration, and production expansion to enhance their market position and expand their consumer base.
Segments Covered in the Report
The global AI in asset management market has been segmented on the basis of
Technology
Deployment Modes
Applications
Verticals
Regions
Key Players
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 partnership, collaboration, and production expansion to enhance their market position and expand their consumer base.
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