How Machine Learning helps in Fraud Detection?

 How Machine Learning helps in Fraud Detection?

A machine learning (ML) model is used in Fraud Detection Using Machine Learning, together with an example dataset of credit card transactions, to train the model to spot fraud trends. The model is self-learning, allowing it to adjust to fresh, uncharted fraud trends. You can use Fraud Detection Using Machine Learning to automate transaction processing on either your own or an example dataset. The integrated machine learning model identifies possibly fraudulent activities and marks it for examination. You can create the architecture shown in the diagram below by using the GitHub example code.

Machine learning for fraud detection is based on the idea that fraudulent transactions have unique characteristics that are absent from legitimate transactions. Using this presumption as a guide, machine learning algorithms look for patterns in financial transactions and determine the legitimacy of a given transaction. 

The main distinction between machine learning and Artificial Intelligence is the concept of "learning." With machine learning, we may provide a computer with a lot of data so that it can learn how to make judgments about the information, much like a human does.