Data analytics has created a great impact in the banking and financial services industry, for example, by providing insights of global financial trends and financial modelling etc. Among them, fraud prevention and detection are one of the applications. This article applied predictive data analytics and supervised machine learning (ML) methods for card-not-present (CNP) fraud detection, and demonstrated modelling using EvalML, an auto machine learning library. This article also identified that both Decision Tree (DT) and XGBoost models work better than Linear models (LM), Random Forest (RF) and LightGBM models. The dataset used to demonstrate modelling is a large-scale dataset from Vesta which is available on Kaggle .

Machine Learning is one of the fastest-growing technology in the modern era. New innovations in the field of ML and AI are made each and every day which supports the world to leap forward. Earlier for a person entering into the ML field finds it difficult to create accurate machine learning models, but now AutoML Libraries are created which helps the beginners to create an accurate model with less work involved. Many AutoML libraries take the data as input and provide a good model with better accuracy for the given data. In today's article, we are discussing one of the commonly used AutoML library EvalML Automated Machine Learning or AutoML is simply the process of automating real-world machine learning tasks.

Featuretools is a framework to perform automated feature engineering. Compose is a tool for automated prediction engineering. It allows you to structure prediction problems and generate labels for supervised learning. We've seen Featuretools and Compose enable users to easily combine multiple tables into transformed and aggregated features for machine learning, and to define time series supervised machine learning use-cases. The question we then asked was: what happens next?