AI In Asset Management Market 2020 and Forecast 2021-2027

  • TBI394173
  • November 05, 2020
  • Global
  • 135 pages
  • IFF Market Research

Report Overview: AI In Asset Management Market

The global AI in asset management market size was valued at USD 990.3 million in 2020 and looks set to grow at a compound annual growth rate (CAGR) of 37.0% from 2021 to 2027. Artificial intelligence (AI) has demonstrated a potential impact on the asset and wealth management industry over the past decade. For instance, AI-enabled solutions like conversational platforms or Chabot have improved customer interactions and related services. In the Business-to-Consumer (B2C) domain, Fintech organizations offer the end-use industries a wide range of AI-supported advisor services to make automated investment decisions. However, the applications in Business-to-Business (B2B) markets, such as fixed-income asset management, still rely on traditional data processing based on human interactions. Exponentially increasing data volumes, strict regulations, and low-interest rates are encouraging asset managers to reconsider their traditional business strategies. Moreover, recent technological advancements have paved the way for artificial intelligence in asset management. Connection of knowledge, domain-enriched ML (machine learning), and NLP (natural language processing) techniques are being adopted by several FinTech companies to offer improved financial and investment services. For instance, in September 2019, China Asset Management, a China-based fund management company, collaborated with Microsoft’s researchers to develop an AI model. This AI model analyzes the vast amount of real-time data of financial transactions. This partnership was done under the Microsoft’s Innovation Partnership program, under which AI expertise is shared with companies across various industries to help them derive digital transformation in their portfolio. Artificial intelligence is integrated with the asset and wealth management for several purposes, such as improving the operational efficiency, customer experience and interfaces, and investment processes. Essential applications of AI to increase operational efficiency include monitoring, quality checking, and the exception handling of the vast amount of data on financial instruments. Improving data quality is of the utmost importance as it reduces operational risks and helps in client retention. For instance, Presenso, an Israel-based start-up, offers a cloud-hosted software for predictive maintenance of industrial assets. It helps industrial manufacturers to detect abnormalities in their sensor data with the use of machine learning techniques. Furthermore, the platform offered by the company prompts alerts to asset managers in case of any equipment or asset breakdown. Researchers have made tremendous strides in developing the ultimate human-machine interaction systems in recent years. AI is used to capture audio, text, and imagery data from various vendor/internal databases and public sources by implementing computer vision, NLP, and voice recognition programs. For instance, computer vision and NLP are used for data extraction from issuer filings for valuation models and transcription of analyst conference calls. More extensive programs will further process the information gathered from various sources to generate insights into the investment decision-making process. This often requires advanced AI techniques, such as machine learning and deep learning. Considering the novel COVID-19 (coronavirus) pandemic, there has been a massive surge in relocations and deployments of devices and equipment, with millions of employees displaced to WFH (Work-From-Home) environments. The ability to procure, deploy, and manage hardware assets has become considerably complicated, with the urgency of bringing millions of remote devices on-line. However, businesses that continue to leverage the use of artificial intelligence are expected to take this pandemic as an opportunity. For instance, AI can help firms in creating actionable insights for connected devices and reducing costs with smart asset management techniques.

Technology Insights: AI In Asset Management Market

Machine learning led the market and accounted for more than 65.0% share of the global revenue in 2019. This is attributed to increasing process automation in manufacturing industries. Machine learning (ML) reflects the natural evolution of technology as machines are capable of sorting through large datasets and extract information by identifying patterns and outliers. ML is being employed in the asset management systems to increase the accuracy and efficiency of operational workflow, improve the customer experience, and enhance the system performance. Machine learning is used to detect patterns in unstructured and structured data to deliver actionable insights to enable investment-related decision-making ability. For instance, Infosys Limited, a tech giant in IT services, offers a machine learning-based AI platform, called KRTI 4.0 that provides smart decision making on all levels of the organization. KRTI 4.0 is designed as an enterprise-wide decision-making support tool, enabling seamless sharing of learnings from one facility across the whole enterprise and accelerating the analytical knowledge. Moreover, machine learning helps in searching for correlations between world events and their impact on prices of assets, consequently improving the decision-making in asset management.

Deployment Mode Insights: AI In Asset Management Market

On-premises led the market and accounted for 60.1% share of the global revenue in 2019. This is attributed to the security and privacy provided by the on-premises solutions in asset management. Moreover, on-premise solutions use edge analytics that reduces the bandwidth requirement. Integrating these solutions on-premise brings higher speed and more reliability in the results. The cloud segment is anticipated to witness substantial growth over the forecast period 2021-2027. This growth is attributed to the fact that cloud solutions remove the firewall restrictions that can hamper the users’ access to an on-premise solution. Cloud-based SaaS (software-as-a-service) solution eliminates maintenance and overhead costs. Moreover, cloud object storage services provide the virtually unlimited storage capacity that removes the scalability and storage volume restrictions of locally-placed hardware.

Application Insights: AI In Asset Management Market

The portfolio optimization segment led the market and accounted for 25.1% share of the global revenue in 2019. This is attributed to the high adoption of machine learning algorithms in asset management to facilitate portfolio management decisions. Portfolio optimization includes several use-cases, such as portfolio construction and optimization, predictive forecasting of long-term price analysis, and development of strategies for risks associated with investments. A prototype for portfolio optimization in the investment process is built based on stock selection, portfolio management process, and asset allocation optimization. The conversational platform segment is anticipated to witness substantial growth over the forecast period. This growth is attributed to the fast-gained acceptance of chatbots in the industry. The availability of mobile extension with a combination of text, speech, and touch interfaces has accelerated the introduction of new services to a customer or client. Moreover, organizations can provide differentiated and personalized experiences to their customers by using conversational AI platforms.

Vertical Insights: AI In Asset Management Market

The BFSI segment led the market and accounted for more than 20.0% share of the global revenue in 2019. This is attributed to the rapid adoption of AI in asset management systems in the financial services and banking sector. There are several applications of AI in financial services, including alpha generation and stewardship in asset management, risk management, fraud detection, relationship manager augmentation, and algorithmic trading. For instance, BlackRock, Inc., an American investment management organization, offers an asset management solution, called FutureAdvisor that digitizes the wealth management process for financial institutions and their advisors to serve clients in a scalable way. Other verticals in the industry include healthcare, retail and e-commerce, energy and utilities, media and entertainment, automotive, and others. The Conversational AI platform is one of the most used applications in every vertical. For instance, Lexalytics, a U.S.-based provider of sentiment and intent analysis, offers an NLP-based intelligence platform that extracts actionable insights from complex text data. This platform serves several industries, including retail and e-commerce, financial services, restaurants/food services, market research, marketing and advertising, pharmaceuticals, biotechnology and healthcare, and airlines and airports. Moreover, building and infrastructure, transportation, consumer goods, and travel and hospitality are other verticals where artificial intelligence is being used for asset management applications.

Regional Insights: AI In Asset Management Market

North America dominated the market and accounted for over 50.0% share of global revenue in 2019. This is attributed to favorable government initiatives to encourage the adoption of artificial intelligence (AI) across various industries. For instance, in February 2019, U.S. President Donald J. Trump launched the American AI Initiative as the nation’s strategy for promoting leadership in artificial intelligence. As part of this initiative, Federal agencies have fostered public trust in AI-based systems by establishing guidelines for its development and real-life implementation across different types of industrial sectors. Asia Pacific is anticipated to witness significant growth over the forecast period. This growth is attributed to the significantly increasing investments in AI in asset management. For instance, in July 2019, Zheshang Fund Management Co., a China-based investment advisor company that manages assets worth approximately USD 6.5 billion, has planned to use about 300 artificial intelligence-based investment models to analyze 3,000 Chinese stocks. Moreover, a growing number of AI start-ups in the region are boosting the adoption of AI in asset management to improve operational efficiency and enable process automation.

Key Companies & Market Share Insights: AI In Asset Management Market

Vendors in the market are focusing on increasing the customer base to gain a competitive edge in the industry. Therefore, key players are taking several strategic initiatives such as mergers & acquisitions, partnerships, and collaborations with other key players in the industry. For instance, in May 2019, Siemens, a German multinational conglomerate company, partnered with Presenso, an Israel-based start-up, to enable predictive asset maintenance. Under the agreement, Presenso integrates its real-time industrial analytics solution into the Siemens’ tools portfolio of remote diagnostic services to support operations and maintenance (O&M) services of Siemens. Some of the prominent players in the global artificial intelligence (AI) in asset management market include:

Key companies Profiled: AI In Asset Management Market Report

This report forecasts revenue growth at the global, regional, and country levels and provides an analysis of the latest industry trends and opportunities in each of the sub-segments from 2016 to 2027. For this study, Trusted Business Insights has segmented the global AI in asset management market report based on technology, deployment mode, application, vertical, and region:

Technology Outlook (Revenue, USD Million, 2016 - 2027)

  • Machine Learning
  • Natural Language Processing (NLP)
  • Others

Deployment Mode Outlook (Revenue, USD Million, 2016 - 2027)

  • On-premises
  • Cloud

Application Outlook (Revenue, USD Million, 2016 - 2027)

  • Portfolio Optimization
  • Conversational Platform
  • Risk & Compliance
  • Data Analysis
  • Process Automation
  • Others

Vertical Outlook (Revenue, USD Million, 2016 - 2027)

  • BFSI
  • Healthcare
  • Retail & E-commerce
  • Energy & Utilities
  • Media & Entertainment
  • Automotive
  • Others

Table of Contents 
Chapter 1 Methodology and Scope
   1.1 Information Procurement and Research Scope
   1.2 Information Analysis
   1.3 Market formulation & data visualization
   1.4 Market Scope and Assumptions
       1.4.1 Secondary Sources
       1.4.2 Primary SourcesChapter 2 Executive Summary
   2.1 AI in Asset Management - Industry Snapshot & Key Buying Criteria, 2016 - 2027
   2.2 Global
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