The global AutoML market attained a value of $269.6 million in 2019 and is predicted to generate a revenue of $14,511.9 million by 2030. Furthermore, the market is predicted to progress at a CAGR of 43.7% between 2020 and 2030. The rising requirement for efficient fraud detection technologies, the growing demand for personalized product recommendations, and the increasing necessity of predictive lead scoring are the major factors driving the progress of the market.

The increasing prevalence of fraudulent incidents is making the prevention and detection of frauds a huge challenge for companies operating in different industries. Currently, various organizations are using conventional machine learning techniques and big data for detecting and preventing frauds. 

However, this dataset can be very imbalanced and extremely skewed in terms of both negative and positive classes. Therefore, in order to get precise and accurate results, automated machine learning solutions are increasingly being used. These solutions optimize processes such as data exploration, data reprocessing, missing data handling, feature engineering, training, evaluation, and model selection without requiring any human intervention.