Artificial Intelligence (AI) in Asset Management Global Market Report 2024

The artificial intelligence (AI) in asset management market size has grown exponentially in recent years. It will grow from $3.28 billion in 2023 to $4.27 billion in 2024 at a compound annual growth rate (CAGR) of 30.2%. The growth observed in the historical period in the field of artificial intelligence in asset management can be attributed to several factors. These include advancements in data analytics, the development of quantitative analysis, the increasing complexity of financial markets, growing needs in risk management, and the rise of algorithmic trading. These factors collectively contributed to the evolution and expansion of AI applications in managing assets during the specified historical timeframe.

The artificial intelligence (AI) in asset management market size is expected to see exponential growth in the next few years. It will grow to $12.34 billion in 2028 at a compound annual growth rate (CAGR) of 30.4%. The anticipated growth in the forecast period for artificial intelligence in asset management can be attributed to various factors. These include the adoption of AI-powered investment decision-making, improvements in portfolio optimization, integration of alternative data sources, solutions for regulatory compliance, and the impact of global economic volatility. Key trends expected during this period encompass the development of explainable AI models, the integration of AI in environmental, social, and governance (ESG) considerations, the utilization of predictive analytics, the application of AI in wealth management, and the expansion of robo-advisors in the asset management landscape.

The ascendant adoption of cloud solutions across diverse industries stands as a pivotal driver for the forthcoming expansion of artificial intelligence (AI) within the asset management sphere. Cloud solutions encompass a range of services delivered via the Internet, encompassing networking, servers, databases, software, and data storage. Asset management firms leverage cloud solutions to access AI software, aiming to amplify asset utilization and refine asset distribution strategies. For instance, Eurostat, a Luxembourg-based government agency, reported that in 2021, 41% of companies in the European Union embraced cloud computing services. This surge in cloud solution adoption across industries is poised to propel the integration of AI within the asset management sector.

The mounting demand for sophisticated fraud detection solutions is anticipated to drive the trajectory of artificial intelligence (AI) in the asset management market. Fraud detection involves the identification and prevention of fraudulent activities or behaviors within systems or organizations. Employing AI in asset management fortifies the domain with enhanced efficiency, precision, early anomaly detection, real-time monitoring, analysis of user behavior and transaction patterns, and adaptability to the constantly evolving landscape of financial security. For instance, according to research by Check Point, an Israel-based cybersecurity threat intelligence team, there was a 50% surge in corporate hacking attacks per week in 2021, peaking at 925 cyber-attacks per week per organization globally. Consequently, the escalating necessity for advanced fraud detection solutions is steering the growth trajectory of artificial intelligence within the asset management domain.

Product innovation stands as the predominant trend gaining traction within the artificial intelligence (AI) in asset management sector. Notably, major industry players are directing their efforts towards crafting advanced AI platforms capable of automating essential asset management functions to reinforce their market standing. For instance, in May 2023, Aisot Technologies, a Switzerland-based software company, unveiled a fully automated platform tailored for active asset management. This tool empowers asset managers to curate portfolios that dynamically reflect consumer preferences across stock and cryptocurrency markets. Moreover, it streamlines the creation of fully automated financial products from investment decisions while harnessing artificial intelligence for optimization. Distinguished by its utilization of AI, neuro-linguistic programming, and quantitative finance, this platform marks a pioneering move by offering services catering to both traditional assets and the cryptocurrency domain.

Significant entities within the AI-driven asset management arena are actively developing novel products, such as next-generation AI supercomputing services, to carve a competitive edge in the market. AI supercomputing services encompass cloud-based or specialized computing services optimized specifically for high-performance artificial intelligence workloads. For example, in March 2023, NVIDIA Corporation, a prominent US-based technology company, introduced DGX Cloud. DGX Cloud presents businesses with seamless access to NVIDIA's AI supercomputing capabilities via a web browser from various global cloud platforms. Functioning as an AI-training-as-a-service platform, DGX Cloud furnishes enterprise developers with a serverless environment specifically tailored for generative AI tasks. Offering businesses the ability to access their personalized AI supercomputer via standard web browsers, DGX Cloud instances boast eight NVIDIA 80GB Tensor Core GPUs, delivering 640GB of GPU memory per instance.

In April 2023, Alarm.com, a prominent US-based home automation company, successfully acquired Vintra for an undisclosed amount. Through this acquisition, Alarm.com aims to enhance its deep learning program and accelerate the implementation of advanced video analytics solutions for both the Alarm.com and OpenEye platforms. Vintra, the acquired company, is based in the United States and specializes in artificial intelligence (AI) video analytics. It provides solutions for home asset management through the application of video analytics technologies.

Major companies operating in the artificial intelligence (AI) in asset management market report are Alphabet Inc., Microsoft Corporation, JPMorgan Chase & Co., Amazon Web Services Inc., International Business Machines Corporation, Deloitte Touche Tohmatsu Ltd., The Goldman Sachs Group Inc., UBS Group AG, Salesforce Inc., FMR LLC, BlackRock Inc., Infosys Limited, S&P Global Inc., Franklin Templeton Distributors Inc., Invesco Ltd., Genpact LLC, Schroders plc, Man Group Ltd., Wellington Management Company LLP, Tableau Software LLC, Janus Henderson Group Plc, Robeco B.V., Bridgewater Associates LP, InMoment Inc., Winton Group Limited, D.E. Shaw & Co., AQR Capital Management LLC, Renaissance Technologies LLC, Dimensional Fund Advisors LP, Eaton Vance Corp.

North America was the largest region in the artificial intelligence (AI) in asset management market in 2023. The regions covered in the artificial intelligence (AI) in asset management market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa. The countries covered in the artificial intelligence (AI) in asset management market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Russia, South Korea, UK, USA, Italy, Spain, Canada.

The primary technologies associated with artificial intelligence in asset management include machine learning, deep learning, natural language processing, predictive analytics, and others. Machine learning is an AI application comprising algorithms that analyze data, learn from it, and apply acquired knowledge to make intelligent decisions. Deep learning, on the other hand, is a subset of machine learning that involves organizing algorithms in layers to create an artificial neural network capable of independent learning and decision-making. These AI technologies are deployed in both on-premises and cloud infrastructure, serving various applications such as portfolio optimization, risk and compliance management, process automation, conversational platforms, data analysis, and more. Industries benefiting from AI in asset management include BFSI (banking, financial services, and insurance), healthcare, retail and e-commerce, media and entertainment, energy and utilities, automotive, among others.

The artificial intelligence (AI) in asset management research report is one of a series of new reports that provides artificial intelligence (AI) in asset management market statistics, including the artificial intelligence (AI) in asset management industry's global market size, regional shares, competitors with artificial intelligence (AI) in asset management market share, detailed artificial intelligence (AI) in asset management market segments, market trends and opportunities, and any further data you may need to thrive in the artificial intelligence (AI) in asset management industry. This artificial intelligence (AI) in asset management market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenarios of the industry.

The artificial intelligence (AI) in asset management market consists of revenues earned by entities by providing services such as portfolio distribution services, asset tracking services and asset analysis services. The market value includes the value of related goods sold by the service provider or included within the service offering. The artificial intelligence (AI) in asset management market also includes sales of workstations, drives, routers, and servers, which are used in providing artificial intelligence (AI) in asset management services. Values in this market are ‘factory gate’ values, that is the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.

The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD, unless otherwise specified).


The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.