The artificial intelligence can be broadly helpful in fraud detection using different machine learning algorithms, such as supervised and unsupervised learning algorithms. The rule-based algorithms of Machine learning helps to analyze the patterns for any transaction and block the fraudulent transactions.

Below are the steps used in fraud detection using machine learning:

Data extraction: The first step is data extraction. Data is gathered through a survey or with the help of web scraping tools. The data collection depends on the type of model, and we want to create. It generally includes the transaction details, personal details, shopping, etc.
Data Cleaning: The irrelevant or redundant data is removed in this step. The inconsistency present in the data may lead to wrong predictions.
Data exploration & analysis: This is one of the most crucial steps in which we need to find out the relation between different predictor variables.
Building Models: Now, the final step is to build the model using different machine learning algorithms depending on the business requirement. Such as Regression or classification.