Visiongain has published a new report on Artificial Intelligence (AI) in Asset Management Market Report to 2031: Forecasts By Technology (Machine Learning & Deep Learning, Natural Language Processing, Predictive Analytics, Others), By Deployment (On-Premise, On-Cloud), By Application (Portfolio Optimization, Risk & Compliance, Process Automation, Conversational Platform, Data Analysis, Others), By Industry Vertical (BFSI, Healthcare, Retail & E-Commerce, Media & Entertainment, Energy & Utilities, Automotive, Others). PLUS, Profiles of AI in Asset Management Companies and Regional and Leading National Market Analysis. PLUS COVID-19 Recovery Scenarios.

Global AI in Asset Management market was valued at US$ XX billion in 2019 and is projected to reach a market value of US$ XX billion by 2031.

Growing demand for automating financial services and demand for process automation in manufacturing industry are the factors driving the growth of AI in asset management market. The market seeks growth opportunities from growing adoption of cognitive systems for boosting operational performance and changing consumer preference. However, variation in application-specific requirements, lack of technical expertise, and lack of robust AI systems are some of the factors hampering the market growth during the forecast period.

COVID-19 Impact on AI In Asset Management Market
The coronavirus outspread has affected the businesses and livelihood of the people at an unprecedented rate. The nationwide lockdown imposed by the governments of all the countries led to shutting down of the on-premise business process and manufacturing facilities. The economic downturn which followed the pandemic further had a significant impact on the revenue. The demand for AI-powered asset management solution witnessed a rise during the epidemic owing to the need among enterprises to manage the financial as well as other assets to help them sustain during the economic crisis. Moreover, the cloud-based solution witnessed a higher demand owing to the cost-effective features the solution offers. Thus, the COVID-19 outbreak is expected to have a steady but positive impact on the market.

Market Drivers

Growing Demand for Automating Financial Services
BFSI sector is amongst the early adopters of digitalization technology to automate the operational processes. Automation of functional process and services helps in increasing the productivity, cost savings, and enhances the customer experience driving the sales. Moreover, it also frees the employees from repetitive tasks to focus on high-value projects. Automation of financial asset management improves the user experience, operational efficiency and the investment process, which is expected to drive the growth of AI in asset management market.

Growing Demand for Process Automation in Manufacturing Industry
AI technology has paved its way across all the industry verticals, and manufacturing is no exception. Rising penetration of smart & connected technologies such as IoT and M2M massive amount of operational data can be gathered, which is offers business insights. AI helps in analyzing the captured data to provide data-driven insights increasing the operational efficiency by providing continuous monitoring and performing quality checks regularly to minimize the downtime due to potential breakdown issues. It also prompts alerts in case of breakdown and offers details regarding the issues and the probable cause behind. Thus, growing demand for process automation in the manufacturing industry is expected to drive the growth of AI in asset management market.

Market Opportunities

Growing Adoption of Cognitive Systems for Boosting Operational Performance
AI & ML technologies have been increasingly adopted, owing to the need to boost operational performance in financial asset management. AI and ML technologies help in ensuring transparency in the investment process and risk operations, thereby increasing efficiency. The AI & ML algorithms also help in monitoring and analyzing the robust production data helping in reducing errors and mitigating the operational risks. Moreover, the intelligence offered by the cognitive technologies further helps in robust testing of applications and business continuity management, and disaster recovery & planning. Thus the growing adoption of cognitive systems for boosting operational performance is expected to create growth opportunities for the AI in asset management market.

Changing Consumer Preference
Consumer preferences have been changing over the years. The consumers today are demanding personalized services. AI technology helps in analyzing consumer data and extract valuable insights from the data to curate personalized services for its users. Thus, changing consumer preference and the need among enterprises to obtain a competitive advantage in the market is expected to drive the demand for AI for asset management to enhance customer service.

Competitive Landscape
The AI in the asset management market is highly competitive. The key market players are highly focused on organic as well as inorganic growth strategies to strengthen its product capabilities and strengthen its position in the market. Some of the companies profiled in the report include Lexalytics, Next IT, Narrative Science, IPsoft, Genpact, IBM Corporation, Infosys, Amazon Web Services, Inc., Microsoft Corporation, Synechron.

April 2020, Microsoft Corporation formed a strategic partnership with BlackRock, Inc. The partnership aimed at hosting BlackRock’s Aladdin infrastructure on the Microsoft Azure cloud platform, for enhancing the capabilities to BlackRock and its Aladdin clients which includes institutional investors and wealth managers.

November 2019, IBM Corporation launched Maximo Asset Monitor. The new AI-powered monitoring solution is designed to help the maintenance and operations leaders in better understanding and improving the performance of their high-value physical assets. The solutions also aid in discovering essential insights with AI-powered anomaly detection and provide enterprise-wide visibility into critical equipment performance, thereby enhancing problem identification to make data-driven better decisions and reduce downtime.

Notes for Editors
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