Detecting Credit Card Fraud using Machine Learning

Abstract

Credit card is getting increasingly more famous in budgetary exchanges, simultaneously frauds are likewise expanding. Customary techniques use rule-based master frameworks to identify fraud practices, ignoring assorted circumstances, the outrageous lopsidedness of positive and negative examples. In this paper, we propose a CNN-based fraud detection system, to catch the natural examples of fraud practices gained from named information. Bountiful exchange information is spoken to by an element lattice, on which a convolutional neural organization is applied to recognize a bunch of idle examples for each example. Trials on true monstrous exchanges of a significant business bank show its boss presentation contrasted and some best-in-class strategies. The aim of this paper is to merge between Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and Auto-encoder (AE) to increase credit card fraud detection and enhance the performance of the previous models. By using these four models; CNN, AE, LSTM, and AE&LSTM. each of these models is trained by different parameter values highest accuracy has been achieved where the AE model has accuracy =0.99, the CNN model has accuracy =0.85, the accuracy of the LSTM model is 0.85, and finally, the AE&LSTM model obtained an accuracy of 0.32 by 400 epoch. It is concluded that the AE classifies the best result between these models.

Detecting Fraudulent Transactions Using Stacked Autoencoder Kernel ELM Optimized by the Dandelion Algorithm

“…Hierarchical neural networks, such as multilayer perception and deep learning techniques, are currently very effective, but the learning process takes a long time [3,5,29,30]. As a result, our proposition that incorporates the ELM into deep architecture as an autoencoder leads to a high rate and quick learning speed.…”

“…Artificial intelligence (AI) is a possible way to combat credit card fraud. In contrast, machine learning algorithms play a crucial role in addressing credit card fraud using linear regression, KNN, SVM, naïve Bayes, random forest, and ANN [2,3]. The design of these algorithms is to learn continuously from previous transaction data and adapt to new fraud patterns.…”

Financial revolution: a systemic analysis of artificial intelligence and machine learning in the banking sector

“…Based on the experimental findings of the different supervised models, naive bayes and SVM are 97% accurate, and logistic regression is 99% accurate in detecting credit card fraud. Likewise, Almuteer et al [20] combine the convolutional neural network (CNN), auto-encoder (AE), and long short-term memory (LSTM) models for improved performance and detection of credit card fraud. Converting into four models that can be used: CNN, AE, LSTM, and AE&LSTM.…”

Utilizing Machine Learning to Enhance Risk Assessment at Tamara

“…In this regard (Sun and Vasarhalyi 2021) develop a prediction system to help the credit card issuer model the credit card delinquency risk. Credit card is getting increasingly more famous in budgetary exchanges, simultaneously frauds are likewise expanding (Almuteer et al 2021).…”