AI in Asset Management Market

AI in Asset Management Market Size

             2020                         2024-2032      

Report	Details
Market Size Value	USD 2.6 billion by 2024
Market Size Value	USD 18.5505491776678 billion by 2032
CAGR	24.4%
Forecast Period	2024-2032
Base Year	2023
Historic Data	2020
Segments Covered	Technology, Deployment Model, Application,End User and Region
Geographics Covered	North America, Europe, Asia Pacific, and RoW

Major vendors in the global AI in asset management market: Amazon Web Services, Inc., BlackRock, Inc., CapitalG, Charles Schwab & Co., Inc., Genpact, Infosys Limited, International Business Machines Corporation, IPsoft Inc., Lexalytics, Microsoft, TABLEAU SOFTWARE, LLC, Next IT Corp., S&P Global, Salesforce, Inc. and Others.

Market Overview

AI in Asset Management, Artificial intelligence is being seamlessly integrated into asset and wealth management to enhance operational efficiency, customer experience, and investment processes. In the realm of operational efficiency, AI plays a crucial role in monitoring, quality checking, and handling exceptions related to the vast amount of data on financial instruments. Improving data quality is paramount, reducing operational risks and contributing to client retention. Advanced AI techniques like machine learning and deep learning are applied to process information extensively, providing valuable insights for investment decision-making. Amid the novel COVID-19 pandemic, substantial shifts and deployments of devices and equipment have occurred, with a vast number of employees transitioning to work-from-home (WFH) environments. Managing the procurement, deployment, and maintenance of hardware assets in this context has become notably complex. However, businesses embracing artificial intelligence during this challenging period can turn it into an opportunity. AI can aid companies in generating actionable insights for connected devices and implementing smart asset management techniques, thereby reducing costs.

High investments by enterprises in AI services

Enterprises are making high investments in AI services, reflecting the recognition of AI's transformative power in asset management. Increased investment signifies a commitment to leveraging AI technologies for data-driven insights, predictive analytics, and enhanced decision-making. Enterprises across diverse sectors are allocating significant resources to deploy and integrate AI solutions into their asset management practices, aiming to gain a competitive edge, streamline operations, and achieve better returns on investments. This investment trend underscores the growing acknowledgment of AI as a strategic enabler in modern asset management practices.

Market Dynamics

Drivers:

• Growing adoption of cloud-based artificial intelligence services in asset management
• The growing importance of asset tracking in BFSI sector
• Strong government initiatives to promote AI-based infrastructure
• High investments by enterprises in AI services

Opportunities:

• The development of personalized and customer-centric services
• AI-driven automation provides an opportunity to streamline operational processes in asset management.

AI solutions AI-driven automation provides an opportunity to streamline operational processes in asset management

The development of personalized and customer-centric services is a significant opportunity facilitated by AI in asset management. AI technologies enable the creation of tailored investment solutions based on individual client profiles, preferences, and financial goals. This personalized approach enhances the client experience, fostering client satisfaction and loyalty. Asset management firms can utilize AI-driven insights to offer bespoke investment recommendations, risk management strategies, and transparent communication, thereby attracting and retaining clients in a competitive market landscape. This customer-centric approach positions AI as a transformative force in reshaping how asset management services are delivered and perceived by clients.

The market for AI in Asset Management is dominated by North America.

In 2022, North America asserted its dominance in the market, commanding highest revenue share. This can be attributed to supportive government initiatives aimed at promoting the widespread adoption of AI technologies across various industries. Numerous asset management firms in North America have already initiated the integration of AI into their operations, a trend projected to persist as the manifold benefits become more universally acknowledged. These advantages encompass process automation, the incorporation of machine learning algorithms for enhanced decision-making, and the utilization of natural language processing to elevate customer engagement.

The Asia Pacific region is poised to undergo substantial growth in the AI in Asset Management market, primarily due to the escalating investments in AI technologies within the asset management sector. A notable example is the March 2023 agreement where Accenture PLC committed to acquiring Flutura, an AI solutions company based in Bangalore. This strategic acquisition aims to fortify Accenture PLC’s industrial AI services, enhancing the efficiency of refineries, plants, and supply chains, and facilitating customers in achieving their net-zero goals more expeditiously.

The Machine Learning segment is anticipated to hold the largest market share during the forecast period

Based on technology market is divide into machine learning and natural language processing (NLP). The machine learning segment took the lead in the market, contributing to highest global revenue in 2022. This substantial share can be attributed to the escalating adoption of process automation in manufacturing industries. Machine learning (ML) represents a natural progression in technology, enabling machines to sift through extensive datasets, identify patterns, and extract information effectively. In asset management systems, ML is harnessed to elevate the accuracy and efficiency of operational workflows, enhance the customer experience, and optimize overall system performance. A noteworthy example is the announcement made by EagleView Technologies, Inc. in February 2023, introducing new asset management solutions. By integrating machine learning with high-resolution aerial imagery, the company aims to address various asset management challenges faced by commercial organizations and local governments.

Major Segmentations Are Distributed as follows:

• Technology
  • Machine Learning
  • Natural Language Processing (NLP)
• Deployment Model
  • On-premise
  • Cloud
• Application
  • Portfolio Optimization
  • Conversational Platform
  • Risk & Compliance
  • Data Analysis
  • Process Automation
  • Others
• End Use
  • BFSI
  • Retail & E-commerce
  • Healthcare
  • Energy & Utilities
  • Others
• By Region
  • North America
    • U.S.
    • Canada
  • Latin America
    • Brazil
    • Mexico
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of Latin America
  • Europe
    • Germany
    • France
    • Italy
    • Spain
    • U.K.
    • BENELUX
    • CIS & Russia
    • Nordics
    • Austria
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • South Korea
    • India
    • Thailand
    • Indonesia
    • Malaysia
    • Vietnam
    • Australia & New Zealand
    • Rest of Asia Pacific
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • South Africa
    • Nigeria
    • Egypt
    • Israel
    • Turkey
    • Rest of MEA

 Recent Developments

• In February 2023, EagleView Technologies, Inc., a leading provider of aerial imagery, software, and analytics, has introduced innovative asset management solutions. Through the incorporation of Machine Learning (ML) with high-resolution aerial imagery, the company seeks to address diverse asset management challenges faced by commercial organizations and local governments.
• In March 2022, Baker Hughes Company, an energy technology firm, joined forces with Microsoft, Accenture PLC, and C3.ai, Inc. to work on industrial asset management (IAM) solutions tailored for customers in the industrial and energy sectors. The collaboration is focused on creating and implementing solutions that leverage digital technologies to enhance the efficiency, safety, and emissions performance of field equipment, industrial machinery, and other tangible assets.

Answers to Following Key Questions:

• What will be the AI in Asset Management Market’s Trends & growth rate? What analysis has been done of the prices, sales, and volume of the top producers in the AI in Asset Management Market?
• What are the main forces behind worldwide AI in Asset Management Market? Which companies dominate AI in Asset Management Market?
• Which companies dominate AI in Asset Management Market? Which business possibilities, dangers, and tactics did they embrace in the market?
• What are the global Insight Engines industry's suppliers' opportunities and dangers in AI in Asset Management Market?
• What is the Insight Engines industry's regional sales, income, and pricing analysis? In the AI in Asset Management Market, who are the distributors, traders, and resellers?
• What are the main geographic areas for various trades that are anticipated to have astounding expansion over the AI in Asset Management Market?
• What are the main geographical areas for various industries that are anticipated to observe astounding expansion for AI in Asset Management Market?
• What are the dominant revenue-generating regions for AI in Asset Management Market, as well as regional growth trends?
• By the end of the forecast period, what will the market size and growth rate be?
• What are the main AI in Asset Management Market trends that are influencing the market's expansion?
• Which key product categories dominate AI in Asset Management Market? What is AI in Asset Management Market’s main applications?
• In the coming years, which AI in Asset Management Market technology will dominate the market?

Reason to purchase this AI in Asset Management Market Report:

• Determine prospective investment areas based on a detailed trend analysis of the global AI in Asset Management Market over the next years.
• Gain an in-depth understanding of the underlying factors driving demand for different AI in Asset Management Market segments in the top spending countries across the world and identify the opportunities each offers.
• Strengthen your understanding of the market in terms of demand drivers, industry trends, and the latest technological developments, among others.
• Identify the major channels that are driving the global AI in Asset Management Market, providing a clear picture of future opportunities that can be tapped, resulting in revenue expansion.
• Channelize resources by focusing on the ongoing programs that are being undertaken by the different countries within the global AI in Asset Management Market.
• Make correct business decisions based on a thorough analysis of the total competitive landscape of the sector with detailed profiles of the top AI in Asset Management Market providers worldwide, including information about their products, alliances, recent contract wins, and financial analysis wherever available.