Anomaly Detection in Credit Card Transaction using Deep Learning Techniques

One of the most convenient ways to pay is by using a credit card. For both online and offline transactions, it is a handy tool. Credit card numbers are used extensively in online purchases, and there is a danger associated with this practice. There are different systems to identify fraudulent transactions, but they only catch on when there are many of them. The regulations and most of the difficulties are included in the green area’s layout. To detect credit card fraud, this article recommends the use of an algorithm called autoencoder, which uses deep learning to identify transactions that relate to certain coverage groups. In this neural net study, fraud on credit cards is detected using neural nets. Random forest and long short-term memory (LSTM) are that may be used to solve the VAE problem, and they are effective for learning order dependency in sequence prediction tasks. To compress data and preserve its original structure when decoding it, the LSTM autoencoder utilizes LSTM encoder-decoder architecture. This research study demonstrate the efficiency and effectiveness of the proposed method via user testing and extensive experimentation on real datasets by using LS TM to solve Machine Learning [ML] techniques in time sequence. The proposed algorithms outperform existing algorithms, such as the LS TM formulation.