AI in Asset Management Market: Industry Analysis & Forecast (2024-2030) Trends, Statistics, Dynamics, and Segmentation

AI in Asset Management Market Overview:

The use of artificial intelligence (AI) technology and techniques in the asset management industry is referred to as the "AI in Asset Management Market". Asset management is monitoring and maximizing an organization's investments and assets to meet certain financial objectives, such as increasing returns, lowering risks, or adhering to legal obligations.

 

Due to the expanding use of cloud technologies AI in Asset Management Market is expected to grow in a number of industries. Monitoring, quality assurance, and handling of the enormous amount of data on financial instruments are all crucial applications of AI. The service providers are currently concentrating on software hosted in the cloud for industrial asset predictive maintenance. With the aid of machine learning algorithms, it helps organizations find any anomalies. By utilizing computer vision, NLP, and speech recognition software, AI is also utilized to extract audio, text, and images from multiple internal databases and external sources.

 

 

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AI in Asset Management Market Dynamics:

Every day, the financial sector produces enormous amounts of data. For asset managers looking for insights into market patterns, investment opportunities, and risk assessment, AI is crucial since it can process and analyse this data more quickly and correctly than humans. Advanced analytics, such as predictive and prescriptive analytics, are made possible by AI. To find patterns, predict market moves, and improve investing strategies, asset managers can employ AI models.

 

By spotting possible dangers in real-time and offering ways to mitigate them, AI can improve risk management. This is especially important in a market environment that is unstable.AI-powered automation can lower operating costs by streamlining processes, automating repetitive jobs, and reducing the need for manual involvement in trading and portfolio management.

 

AI in Asset Management Market Restraints:

The accuracy of AI models in asset management depends on the reliability and accessibility of the data. Poor projections and poor investment decisions might result from inaccurate or inadequate data. Dealing with sensitive financial data and customer personal information creates serious questions about data privacy and cybersecurity. To guard against data breaches and illegal access, asset managers must implement strong security measures.

 

Some AI models, especially deep learning neural networks, are frequently referred to as "black boxes" since it might be difficult to comprehend how they make particular judgments. Trust and regulatory acceptance might be hampered by a lack of transparency. Using AI algorithms excessively without human supervision might have unintended effects, especially in extreme market conditions. The balance between automation and human judgment must be struck by asset managers.

 

AI in Asset Management Market Opportunities:

Advanced analytics and insights from AI can be given to asset managers to help them make better investment decisions. Asset managers may enhance portfolio performance and increase returns by utilizing AI.AI-powered automation can improve processes, eliminate manual work, and cut expenses associated with running businesses. For large asset management organizations in particular, this may result in increased cost effectiveness.

 

AI algorithms are able to trade quickly and accurately, seizing chances in the market that human traders might otherwise overlook. This may result in better trading efficiency and liquidity control. Robo-advisors that are powered by AI offer a wider variety of investors automated and affordable investing options. By developing their own robo-advisory platforms or joining forces with already established ones, asset managers can take advantage of this expanding sector.

 

AI is highly adapted for putting into practice data analysis- and model-based quantitative investing strategies. Asset managers have the option to create their own original quantitative models or purchase AI-powered strategies under license.

 

AI in Asset Management Market Trends:

Cloud computing was increasingly being used for AI in asset management. For handling and analysing huge datasets, cloud-based AI solutions offered scalability, flexibility, and cost efficiency. Asset managers are launching AI-driven investment products to entice investors looking for exposure to AI-driven strategies. Examples include AI-powered exchange-traded funds (ETFs) and mutual funds.

 

Machine learning techniques were being used more and more by asset managers to forecast market trends, asset price changes, and portfolio performance. In order to make more precise investment judgments, machine learning models were being used to reveal hidden patterns and correlations in financial data.

 

To understand market patterns and make data-driven investment decisions, asset managers were looking towards alternative data sources including satellite imaging, social media sentiment, and IoT data. AI was essential in the processing and analysis of this wide-ranging data. By offering insights into various locations and asset classes, AI-powered asset management solutions were enabling businesses to enter international markets and aid in portfolio diversification.

 

AI in Asset Management Market Segment Analysis:

Machine learning, natural language processing, and other technologies are segmented into the global AI in Asset Management Market based on technology. The market for machine learning is anticipated to expand quickly. In 2019, the segment displayed a 65% revenue share. This is a result of increased automated processes in the manufacturing sector.

 

The global market is divided into portfolio optimization, conversational platforms, risk & compliance, data analysis, process automation, and others on the basis of application. It is predicted that the portfolio optimization market will expand significantly. In the year 2019, the segment's revenue share was around 25.1%. This explains the widespread adoption of machine learning systems in asset management to enable portfolio management-based judgments

 

AI in Asset Management Market Regional Analysis:

In 2023, North America led the market and took home more than 35% of worldwide revenue. This is attributable to supportive government programs that aim to promote AI adoption across numerous industries. As the advantages of AI technology become more widely understood, it is anticipated that many asset management companies in North America will continue to use it. Process automation, the application of machine learning algorithms for better decision-making, and the use of natural language processing for increased customer engagement are all examples of this.

 

The AI in Asset Management Market is expected to increase significantly in the Asia Pacific region. This expansion is attributable to the asset management industry's sharply rising AI investment. For instance, in March 2023, Accenture PLC decided to buy Flutura, a provider of AI solutions with headquarters in Bangalore. The acquisition aims to strengthen Accenture PLC's industrial AI offerings so that customers can more quickly reach their net zero goals while also enhancing the efficiency of refineries, plants, and supply chains.  

 

With the UK, Germany, and France setting the bar, the European asset management business has been increasingly adopting AI. Regulations like MiFID II have promoted the use of technology, particularly AI, for reporting and compliance. Brazil and other prominent nations in Latin America are advancing the usage of AI in asset management.AI is being used more and more by asset managers in the area to optimize portfolios and assess risk.

 

The application of AI in financial services is being accommodated by developing regulatory frameworks. To improve its asset management capabilities, the Middle East, especially the United Arab Emirates, has been investing in AI. AI is being utilized to streamline asset management processes and draw in international investment.

 

AI in Asset Management Market Scope
Market Size in 2023	USD 3075 Bn.
Market Size in 2030	USD 30.67 Bn.
CAGR (2024-2030)	35%
Historic Data	2018-2022
Base Year	2023
Forecast Period	2024-2030
Segment Scope	By Technology Machine Learning Natural Language Processing
	By Application Portfolio Optimization Conversational Platforms Risk & Compliance Data Analysis Process Automation Others
Regional Scope	North America- United States, Canada, and Mexico Europe – UK, France, Germany, Italy, Spain, Sweden, Austria, and Rest of Europe Asia Pacific – China, India, Japan, South Korea, Australia, ASEAN, Rest of APAC Middle East and Africa - South Africa, GCC, Egypt, Nigeria, Rest of the Middle East and Africa South America – Brazil, Argentina, Rest of South America

 

AI in Asset Management Market key Players:

1. Amazon Web Services, Inc.
2. BlackRock, Inc.
3. CapitalG
4. Charles Schwab & Co., Inc
5. Genpact
6. Infosys Limited
7. International Business Machines Corporation
8. IPsoft Inc.
9. Lexalytics
10. Microsoft
11. TABLEAU SOFTWARE, LLC
12. Next IT Corp.
13. S&P Global
14. Salesforce, Inc.