Experimental Evaluation of Smart Credit Card Fraud Detection System using Intelligent Learning Scheme

There has been an uptick in the incidence of fraudulent financial activity. The expansion of e-commerce and online payment systems has been connected to an increase in the number of instances of financial fraud, such as fraudulent use of credit cards. Because of this, it is extremely vital to put in place systems that are able to detect the theft of credit cards. The use of stolen credit cards is on the rise all across the world, and immediate action is required to tackle this growing problem. Because the major goal of the system is to protect users from being charged for products and services that they did not approve, setting a limit on this kind of conduct is in the users' best interests. It is essential to pick the characteristics of fraudulent transactions very carefully when employing machine learning for the purpose of detecting fraudulent credit card activity. Over the course of time, the fraudulent acts do not follow any identifiable pattern. Criminals are able to successfully commit acts of online fraud because to the utilization of newly developed technology. Because fraud is an ever-evolving crime, an effective fraud detection model has to be able to adjust to new circumstances and get better over time. We offer the Intelligent Learning Scheme for Digital Fraud Detection (ILSDFD), in this research. ILSDFDD is an engine for the detection of credit card fraud that is based on deep learning and makes use of the feature selection process and classification principles. The suggested remedy is deep learning, which is an unsupervised learning process that uses back-propagation and is based upon auto encoder network. This is accomplished by keeping the inputs and outputs equal to one another. Deep learning was included into the Auto Encoder that was utilized for this research using a Python-based development platform. The accuracy, precision, and recall of a model are the criteria that are used to determine how successful it is. The proposed algorithm is cross-validated with the conventional Artificial Neural Network (ANN) algorithm to prove the efficiency of the proposed scheme ILSDFD. The outcomes demonstrated that our suggested approach outperforms the other contestants' strategies and that the previously discussed techniques improve prediction accuracy.