A number of factors, such as the rising need for effective fraud detection solutions, surging demand for personalized product commendations, and growing importance of projecting lead scoring, are expected to drive the automated machine learning (AutoML) market in the foreseeable period (2020–2030). According to PS Intelligence, the market generated revenue of $346.2 million in 2020, which is expected to reach $14,830.8 million by 2030, growing at a CAGR of 45.6% in the coming years.
Moreover, the market is witnessing a trend of rising fondness toward cloud-based AutoML platforms. One of the major factors boosting the demand for AutoML solutionsis the rising need for effective fraud detection solutions. The prevention and detection of frauds are a massive challenge for all companies across the world.
For example, according to the U.S. Government Accountability Office, payments that were completed in an inappropriate amount and not have been made (improper payment) totaled around $151 billion in 2018, as compared to $141 billion in 2017. Moreover, according to the Reserve Bank of India, over 6,800 incidences of bank frauds were reported in 2018–2019, which cost around $10,079 million.
Geographically, the North American AutoML market accounted for the largest share in 2020, and it is also expected to lead the global market during the forecast period. This is ascribed to the rising venture capital (VC) funding of AI organizations in research and development (RD) activities to improve their offerings. For example, U.S. companies raised $99.5 billion in 2018 through VC funding for AI.
Moreover, technological advancement, presence of key AutoML platform providers, developed IT infrastructure, and well-developed BFSI, healthcare, and IT telecom industries are the key reasons for increasing the demand for AutoML solutions in the region. Thus, the surging need for effective fraud detection solutions and the rising demand for personalized product commendations across the globe are expected to propel the market growth during the forecast period.
