Detection of Credit Card Fraud with Optimized Deep Neural Network in Balanced Data Condition - Shome, Nirupam; Sarkar, Devran Dey; Kashyap, Richik; Lasker, Rabul Hussain (Computer Science, 2024)
Due to the huge number of financial transactions, it is almost impossible for humans to manually detect fraudulent transactions. In previous work, the datasets are not balanced and the models suffer from overfitting problems. In this paper, we tried to overcome the problems by tuning hyperparameters and balancing the dataset by hybrid approach using under-sampling and over-sampling techniques. In this study, we have observed that these modifications are effective to get better performance in comparison to the existing models. The MCC score is considered an important parameter in binary classification since it ensures the correct prediction of the majority of positive data instances and negative data instances. So, we emphasize on MCC score and our method achieved MCC score of 97.09%, which is far more (16 % approx.) than other state of art methods. In terms of other performance metrics, the result of our proposed model is also improved significantly.