The global automated machine learning market size estimated to reach USD 17,291.08 million by 2031, growing at a CAGR of 42.77% during the forecast period (2023–2031). As businesses continue to move towards online credit card payments, there is an increasing need for an effective fraud detection solution capable of real-time, actionable alerts.
As per the Federal Trade Commission's (FTC) Annual Data Book, the most frequent payment method identified out of all fraud reports was credit cards, totaling 459,297 reported instances of fraud and identity theft combined during 2020. These models can then be applied to enhance performance, automate decision-making, and streamline procedures. AutoML can also assist businesses in identifying previously obscure opportunities for optimization and improvement, thereby driving market growth.
|Market Size||USD 17,291.08 million by 2031|
|Fastest Growing Market||Europe|
|Largest Market||North America|
|Report Coverage||Revenue Forecast, Competitive Landscape, Growth Factors, Environment & Regulatory Landscape and Trends|
Financial fraud is one of the most significant concerns in financial security. Machine learning is actively applied to fraud detection applications to fight the growing risk of financial fraud. Several financial services industry players are increasingly adopting AI and ML into their ecosystems to leverage the vast data available from newly acquired digital channels.
Thus, the increasing demand for efficient fraud detection solutions drives market growth.
As businesses rely increasingly on data to drive decision-making and enhance operational efficiency, the demand for intelligent business processes has increased. These processes utilize machine learning algorithms to automate decision-making and optimize business operations, enhancing performance and increasing profits. By leveraging AutoML, businesses can streamline operations, reduce costs, and improve performance, ultimately resulting in a competitive advantage. According to a report by industry experts, Al-driven automation can increase productivity by as much as 40%.
By automating the creation and deployment of machine learning models, the automated machine learning market can assist businesses in achieving these outcomes. Using AutoML, businesses can develop predictive models that can be integrated into existing business processes quickly and effectively.
The limited adoption of machine learning tools is also a key factor for the slow adoption of Automated machine learning solutions in the market. The need for domain specialists in the field of machine learning is substantial, posing a challenge for firms seeking individuals capable of effectively deploying machine learning systems. The utilization of AutoML, as opposed to direct engagement with ML, might reduce the level of expertise necessary for such tasks.
The reluctance to adopt AutoML tools may also depend on the end-user types. For instance, government organizations may resist adopting automated machine e-learning solutions as they handle citizen data. Therefore, privacy and data sensitivity issues may discourage them from adopting such solutions, impeding the market's growth.
The growing acceptability of cloud-based AutoML systems is a significant factor driving the global AutoML market. As SaaS solutions, these platforms offer users access to machine learning tools and resources from any location with an internet connection. Cloud-based AutoML platforms are initially less expensive, easier to scale, and require less maintenance than on-premise solutions.
In addition, they promote innovation in the AutoML market, where vendors continually introduce previously unavailable features and capabilities. Cloud-based AutoML platforms are compelling for businesses and organizations without the resources or expertise to maintain their infrastructure. As cloud computing acquires prominence, the demand for cloud-based AutoML platforms will grow, creating market growth opportunities.
North America Dominates the Global Market
Based on region, the global automated machine learning market is bifurcated into North America, Europe, Asia Pacific, and the Rest of the World.
North America is the most significant global automated machine learning market shareholder and is anticipated to exhibit a CAGR of 42.17% during the forecast period. North America is anticipated to hold a substantial share of the market due to its robust innovation ecosystem, which is fueled by strategic federal investments in advanced technology and complemented by the presence of visionary scientists and entrepreneurs from across the globe, as well as renowned research institutions, which have propelled the development of AutoML. Increased commercial applications for federatedML is further expected to drive demand for autoML. For instance, as per Helpnetsecurity, 73% of businesses in the U.S. had plans to employ more artificial intelligence and machine learning in cybersecurity devices in 2022.
Further, in January 2021, the FDA released an action plan to build a coordinated approach to enhance focus on AI and ML. This was mainly fueled by strategically advancing science and evidence for digital health technologies. In order to do so, the FDA outlined plans to build supporting developments for ML to evaluate and improve algorithms for the healthcare sector. Thus, all these aspects are expected to boost market growth.
Europe is estimated to exhibit a CAGR of 42.47% over the forecast period. Europe has some of the world's largest pharmaceutical companies and a growing number of healthcare AI startups working on everything from drug development to hospital workforce logistics. The integration of artificial intelligence and machine learning is becoming increasingly prevalent, leading to a growing demand for automated machine learning (AutoML) techniques. AutoML plays a crucial role in automating the creation of diverse data models generated by AI, particularly in healthcare. For instance, Merantix, a Germany-based AI research and incubator lab, is developing a cloud-based, on-demand platform that will put its cancer detection AI at the disposal of radiologists worldwide.
The growth in expenditure on digital marketing across the region also provides new opportunities for automated machine learning. For instance, during a January 2021 survey conducted by CMO Survey in the United Kingdom, the digital marketing expenditure of profit companies increased by 9.57% during the twelve months leading up to the study period compared to the previous twelve months.
The Asia-Pacific is considered the fastest-growing market region in the coming years. This is due to increased investment in information technology (IT) and increased adoption of FinTech in the area. In addition, growing government interest in integrating AI into multiple industries is helping to develop regional markets. As per the International Data Corporation (IDC), 60% of Indian companies combine human expertise with machine learning, natural language processing, artificial intelligence, and pattern recognition to improve the foresight of the entire company. This will increase employee productivity and effectiveness by 20% by 2026. In addition, companies operating across the region are getting investments from various sources that aid the market's growth. For instance, in October 2021, Fount, a Robo-advisor startup in South Korea, announced raising USD 33.4 million in its Series C funding round to beef up its machine learning-based platform development and hire more employees.
The rest of the world market consists of South America, the Middle East, and Africa. The Middle East and Africa have witnessed rapid growth in ML hiring in the automotive industry. Advances in machine learning as a subset of artificial intelligence, robotics, and other technologies are profoundly impacting the global economy. Modern businesses are realizing the importance of AI for future growth and prosperity and are investing heavily in digital technologies. Such factors expedite regional market growth over the forecast period.
The global automated machine learning market is segmented by solution, automation type, and end-user.
Based on the solution, the global automated machine learning market is divided into standalone, on-premise, and cloud.
The standalone or on-premise segment is the largest revenue contributor to the market and is expected to exhibit a CAGR of 41.24% throughout the forecast period. The most important feature of on-premise software is data protection because the data is housed locally in user facilities; customers have complete control over it and its security. Sensitive information does not have to leave the organization. This can be a significant advantage, particularly regarding compliance difficulties. These deployments, structured in annual or multi-layer plans, eliminate the need for monthly expenses. Similarly, they can be highly customized to an organization's processes and regulatory requirements. Cloud solutions, which are being increasingly adopted, still face the challenge of security concerns, and hence, on-premise solutions will play a significant role in the market.
The growing recognition among organizations of the need to save money and resources by shifting their data to the cloud rather than developing and maintaining new data storage is boosting demand for cloud-based solutions. The companies are developing products for different end users like BFSI, Healthcare, etc. For example, Google debuted Cloud AutoML. Cloud AutoML enables enterprises with less ML knowledge to generate high-quality custom models by utilizing advanced techniques such as learning2learn and transfer learning from Google.
Based on automation type, the global automated machine learning market is bifurcated into data processing, feature engineering, modeling, and visualization.
The visualization segment dominates the global market and is projected to exhibit a CAGR of 41.88% over the forecast period. Visualization in ML is the process of understanding the data. It helps the users see how the data looks and what kind of correlation is held by the attributes of the data. It is the quickest way to see if the features correspond to the output. Visualization automation is one of the primary aspects of automated machine learning. Most of the AutoML solutions provided by companies in the market, such as DataRobot, Complellon, Tazi.ai, and H2O.ai, among others, offer a high percentage of automation for visualization.
The modeling process involves training a machine-learning algorithm to predict labels from features, optimizing it for business requirements, and validating it on holdout data. The output received from modeling is a trained model that can be used to predict new data points. Automated model building has become increasingly crucial to machine learning, as it creates accurate and dynamic models that require less time to develop and can adapt to altering conditions without requiring a human in the loop at every step. There are four key steps in automated model building: cleaning, feature generation, feature selection, and constructing either a supervised or unsupervised model.
Based on the end-user, the global automated machine learning market is segmented into BFSI, retail and e-commerce, healthcare, and manufacturing.
The BFSI segment owns the highest market share and is estimated to exhibit a CAGR of 41.51% during the forecast period. Recently, AI and machine technologies have been increasingly adopted in the BFSI industry to enhance operational efficiency and improve the consumer experience. As data gain more attention, the demand for machine learning BFSI applications grows. Automated machine learning can produce accurate and rapid results with enormous data, affordable processing power, and economical storage. By collaborating with other fintech services, businesses can adapt to contemporary demands and regulations while enhancing safety and enabling security, thanks to the machine learning-led approach to system modernization.
AI and machine learning technologies can make retailers more competitive by maximizing resources, improving customer service, and offering enhanced product offerings. These also help retailers gain a significant advantage in the industry. This is expected to increase the demand for the market in the retail sector. Retailers are also successfully integrating automated machine learning with in-store environments to make customers' shopping experiences more enjoyable and efficient. For instance, Sephora deployed Color IQ technology to scan a customer's face and provide personalized foundation and concealer shade recommendations.