The rising prevalence of fraudulent incidents is a major factor fuelling the progress of the automated machine learning market all over the globe. Many companies are increasingly adopting advanced technologies and solutions such as conventional machine learning techniques and big data for detecting and preventing frauds. However, because of the high generation of data and high imbalance and skewed nature of the datasets, in terms of negative and positive classes, organizations are incorporating AutoML solutions for data preprocessing, evaluation, missing data management, data analysis, and various other processes.

The major reasons behind the growth of the automated machine learning (autoML) market are the rising demand for advanced fraud detection solutions and personalized recommendations on various products and increasing awareness about predictive lead scoring. Due to these reasons, the market size is expected to increase from $269.6 million in 2019 to $14,511.9 million by 2030, at a 43.7% CAGR during 2020–2030 (forecast period), as per market research firm PS Intelligence.

One of the major reasons for the increasing demand for autoML solutions is the rising number of cyber frauds, which has put the limelight on fraud detection and prevention solutions. Traditional ML and big data technologies require extensive domain knowledge, while data exploration, data preprocessing, training, missing-data handling, model selection, feature engineering, and evaluation require numerous processes. Additionally, with the imbalanced and askew datasets, in terms of the negative and positive classes, the use of autoML for optimizing all the above-mentioned process, without the involvement of humans, is growing.