Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms

Aim:

         The main aim is to detect fraudulent transactions using credit cards with the help of ML algorithms and deep learning algorithms.

 Synopsis:

           A machine learning algorithm was first applied to the dataset, which improved the accuracy of detection of the frauds to some extent. Later, three architectures based on a convolutional neural network are applied to improve fraud detection performance. Further addition of layers further increased the accuracy of detection. A comprehensive empirical analysis has been carried out by applying variations in the number of hidden layers, epochs and applying the latest models. The evaluation of research work shows the improved results achieved, such as accuracy, f1-score, precision having  best optimized values  respectively. The proposed model outperforms the state-of-the-art machine learning and deep learning algorithms for credit card detection problems. In addition, we have performed experiments by balancing the data and applying deep learning algorithms to minimize the false negative rate. The proposed approaches can be implemented effectively for the real-world detection of credit card fraud.

Existing System:

         The relevant literature present many machines learning based approaches for credit card detection, such as Extreme Learning Method, Decision Tree, Random Forest, Support Vector Machine, Logistic Regression and XG Boost, which results low accuracy. In 2020, there were 393,207 cases of CCF out of approximately 1.4 million total reports of identity theft. CCF is now the second most prevalent sort of identity theft recorded as of this year, only following government documents and benefits fraud. In 2020, there were 365,597 incidences of fraud perpetrated using new credit card accounts. The number of identity theft complaints has climbed by 113% from 2019 to 2020, with credit card identity theft reports increasing by 44.6%. Payment card theft cost the global economy $24.26 billion last year. With 38.6% of reported card fraud losses in 2018, the United States is the most vulnerable country to credit theft.

Problem Definition:

       By proposing Machine learning Algorithms, based approaches for credit card detection, such as Extreme Learning Method, Decision Tree, Random Forest, Support Vector Machine, Logistic Regression and XG Boost .The model results leds to low accraucy.

Proposed System:

          Deep learning (DL) algorithms applied applications in computer network, intrusion detection, banking, insurance, mobile cellular networks, health care fraud detection, medical and malware detection, detection for video surveillance, location tracking, Android malware detection, home automation, and heart disease prediction. we explore DL Algorithms to identify credit card thefts in the banking industry in this model. It uses a number of deep learning algorithms for detecting CCF. However, in this model, we choose the CNN model and its layers to determine if the original fraud is the normal transaction of qualified datasets.

 Advantage:

            The imbalanced CCF dataset is transformed into a balanced dataset by removing non fraudulent transactions from the dataset. In a real-world transaction, fraudulent and non- fraudulent classes are not balanced due to the nature of the problem. For instance, if one million transactions are per- formed in a day, only a few can be fraudulent. The convolutional neural network model with layers architecture is applied to the balanced dataset to validate the proposed model. The model is trained over 100 epochs. The CNN layers architecture obtained above  90.00 % training and validation accuracy respectively. The accuracy and loss of CNN model using the balanced CCF dataset.