Credit Card Fraud Detection using Deep Learning Techniques

Oona Voican ()

Informatica Economica, 2021, vol. 25, issue 1, 70-85

Abstract: The objective of this paper is to identify credit card fraud and this topic can be solved with the help of advanced machine learning and deep learning techniques. Due to the fact that credit card fraud is a serious worldwide problem, we have chosen to create a model for detecting im-poster scams by using deep neural networks. The purpose is to understand, determine and learn the normal behavior of the user and more precisely the detection of identity fraud. Each person has a trading pattern, uses certain operating systems, has a specific time to complete the transaction and spends large amounts of money usually within a certain time range. Transac-tions made by a certain user have a certain pattern, which can be identified with the help of neural networks. Machine learning involves teaching computers to recognize patterns in data in the same way as our brains do. Deep learning is just a subfield of machine learning that deals with algorithms inspired by the structure and function of the brain. Deep learning at the core is the ability to form higher and higher level of abstractions of representations in data and raw patterns. The data used to train the model is real, and it will be processed using the one-hot encoding method, so that categorical data/variables can be used by the machine learning algorithm.

Keywords: Artificial intelligence; Credit card fraud; Deep Learning; Neural network model; User behavior. (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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