Email this article Credit Card Fraud Detection Using Deep Learning Based on Auto-LSTM Algorithms: A Comprehensive Review
Abstract
In recent years, credit card fraud has become one of the growing problems. A large financial loss has greatly affected individual person using credit card and also the merchants and banks. It became very rampant and easy mode of payment. People choose online payment and e-shopping; because of time convenience, transport convenience, as a result of huge amount of e-commerce use, there is a vast increment in credit card fraud also. Fraudsters try to misuse the card and transparency of online payments. Thus, to overcome with fraudster’s activity become very essential. Charge card extortion can be done from multiple points of view. By lost or taken cards, by delivering phony or fake cards, by cloning the first site, by eradicating or adjusting the attractive strip present at the card which contains the client’s data, by phishing, by skimming or by taking information from a dealer’s side. With proceeded with headway in fake systems it is critical to create powerful models to battle these fakes in their underlying stage, just before they can take to fruition. Fraud detection involves monitoring the activities of populations of users in order to estimate, perceive or avoid objectionable behavior, which consist of fraud, intrusion, and defaulting. This is a very relevant problem that demands the attention of communities such as machine learning and data science where the solution to this problem can be automated. Our work is based on the basic idea of the deep learning technique to correctly determine whether a transaction pattern is fraudulent or non-fraudulent. The focus is on improving the classification performance in the existing work, using autoencoder-LSTM using credit card datasets obtained from ULB Machine Learning Group. The research scope will be limited to only decision support accuracy measure as an evaluation index while ignoring the computationally capacity of the algorithms as an index for evaluating the quality of the models.
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