Fraud Detection Using Machine Learning and Deep Learning

Frauds are notoriously challenging to detect because of their constant change and lack of trends.Fraudsters take advantage of recent technological advancements.They somehow manage to get over security procedures, which results in millions of dollars being lost.Analyzing and identifying unusual activity with the aid of data mining technologies is one way to locate fraudulent transactions.transactions.This paper evaluates a number of deep learning and machine learning methods, such as auto encoders, convolutional neural networks, restricting Boltzmann machines, and deep belief networks.All three databases-the German, Australian, and European-will be used.ROC curve, Matthews Correlation Coefficient, & area under the curve measure.