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

Abstract

The usage of credit cards for online and regular purchases is exponentially increasing and so is the fraud related with it. A large number of fraud transactions are made every day. Various modern techniques like artificial neural network Different machine learning algorithms are compared, including Logistic Regression, Decision Trees, Random Forest, Artificial Neural Networks, Logistic Regression, K-Nearest Neighbors, and K-means clustering etc. are used in detecting fraudulent transactions. This paper uses genetic algorithm, and neural network which comprises of techniques for finding optimal solution for the problem and implicitly generating the result of the fraudulent transaction. The main aim is to detect the fraudulent transaction and to develop a method of generating test data. This algorithm is a heuristic approach used to solve high complexity computational problems. The implementation of an efficient fraud detection system is imperative for all credit card issuing companies and their clients to minimize their losses.