Agyan Panda - Department of Computer Science and Engineering, OEC Engineering College, OD, India.
Bharath Yadlapalli - Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vijayawada, AP, India.
Zhi Zhou - Government Information Headquarters Inspur Software Group Company Ltd, Jinan, China.

Every year, millions of dollars are lost due to fraudulent credit card transactions. To help fraud investigators, more algorithms are turning to powerful machine learning methodologies. Designing fraud detection algorithms is particularly difficult because to the non-stationary distribution of data, excessively skewed class distributions, and continuous streams of transactions. At the same time, due to confidentiality considerations, public data is uncommon, leaving many questions unanswered about the best technique for dealing with them. We present some replies from the practitioners in this publication. Un balanced ness, non- stationarity and assessment. Our industrial partner provided us with an actual credit card dataset, which we used to do the analysis. In this project, we attempt to develop and evaluate a model for the imbalanced credit card fraud dataset.

Credit card fraud, machine learning applications, data science, Automated fraud detection