Abstract:

Credit card is the commonly used payment mode in the recent years. As the technology is developing, the number of fraud cases are also increasing and finally poses the need to develop a fraud detection algorithm to accurately find and eradicate the fraudulent activities.

This research work proposes different machine learning based classification algorithms such as logistic regression, decision tree, svc, adaboost and xgboost for handling the heavily imbalanced dataset. Finally, this work will calculate the accuracy, precision, recall, f1 score, confusion matrix, and Roc-auc score.

Dataset is collected from the kaggle website. The dataset is trained and tested using the following techniques: logistic regression, decision trees, svc, xgboost and adaboost. If our algorithm is applied into bank credit card fraud detection systems, the probability of fraud transactions can be predicted soon after credit card transaction occurs.

Thereafter a series of anti-fraud strategies can be adopted to prevent banks from great losses and reduce risks.

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