Various machine learning methods, such as supervised and unsupervised learning algorithms, can be used to identify fraud using artificial intelligence. Machine learning’s rule-based algorithms assist in analyzing transaction trends and preventing fraudulent transactions.
The steps for utilizing machine learning to identify fraud are as follows:
Data extraction:
Data extraction is the initial stage. A survey or web scraping technologies are used to collect information. The data collecting process is determined by the sort of model we want to build. It usually contains transaction information, personal information, purchasing information, and so forth.
Data Cleaning:
This phase removes any data that is unnecessary or redundant. The data’s inconsistency might lead to incorrect forecasts.
Data exploration & analysis:
This is one of the most important phases in determining the relationship between various predictor variables.
Building Models:
Finally, based on the business requirement, construct the model using various machine learning techniques. Regression or classification, for example.