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.
Keywords: Machine learning, Credit card, Electronic commerce, Fraud detection
? There are lots of issues that make this procedure tough to implement and one of the biggest problems associated with fraud detection is the lack of both the literature providing experimental results and of real-world data for academic researchers to perform experiments on.
? The reason behind this is the sensitive financial data associated with the fraud that has to be kept confidential for the purpose of customer’s privacy.
? Now, here we enumerate different properties a fraud detection system should have in order to generate proper results
? There are lots of issues that make this procedure tough to implement and one of the biggest problems associated with fraud detection is the lack of both the literature providing experimental results and of real-world data for academic researchers to perform experiments on.
? It improves the model's accuracy and avoids the over fitting problem
? Another problem related to this field is overlapping data. Many transactions may resemble fraudulent transactions when actually they are genuine transactions.
? For his valuable guidance for implementation of proposed system. We will forever remain a thankful for their excellent as well as polite guidance for preparation of this report. Also we would sincerely like to thank to HOD Pawar U.B. and other staff for their helpful coordination and support in project work
? have proposed a deep learning-based method for detecting fraud in credit card transactions. Using machine-learning algorithms such as support vector machine, k-nearest neighbor, and artificial neural network to predict the occurrence of fraud.
? The methods mostly used by hackers today is to attack end-to end technology and exploit human vulnerabilities.
? This algorithm is a heuristic approach used to solve high complexity computational problems.
? An algorithm that can be used for both regression and classification tasks, but it is most commonly used for classification.' Logistic Regression is used to predict categorical variables using dependent variables.
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