scholarly journals Credit Card Fraud Detection Model Based on LSTM Recurrent Neural Networks

2021 ◽  
Vol 12 (2) ◽  
pp. 113-118
Author(s):  
Ibtissam Benchaji ◽  
◽  
Samira Douzi ◽  
Bouabid El Ouahidi
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Credit Card ◽  
Fraud Detection ◽  
Credit Card Fraud ◽  
Detection Model ◽  
Model Based

scholarly journals Credit Card Fraud Detection using Classification, Unsupervised, Neural Networks Models

2020 ◽  
Vol V9 (04) ◽  
Author(s):  
L. Bhavya ◽  
V. Sasidhar Reddy ◽  
U. Anjali Mohan ◽  
S. Karishma ◽  
Keyword(s):  
Neural Networks ◽  
Credit Card ◽  
Fraud Detection ◽  
Credit Card Fraud ◽  
Unsupervised Neural Networks

scholarly journals Emerging Approach for Detection of Financial Frauds Using Machine Learning

2021 ◽  
pp. 9-22
Author(s):  
Upasana Mukherjee ◽  
Vandana Thakkar ◽  
Shawni Dutta ◽  
Utsab Mukherjee ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Credit Card ◽  
Fraud Detection ◽  
Loan Default ◽  
Credit Card Fraud ◽  
Detection Model ◽  
Default Prediction ◽  
Automated Process

The growth of regularly generated data from many financial activities has significant implications for every corner of financial modelling. This study has investigated the utilization of these continuous growing data by a means of an automated process. The automated process can be developed by using Machine learning based techniques that analyze the data and gain experience from the underlying data. Different important domains of financial fields such as Credit card fraud detection, bankruptcy detection, loan default prediction, investment prediction, marketing and many more can be modelled by implementing machine learning methods. Among several machine learning based techniques, the use of parametric and non-parametric based methods are approached by this research. Two parametric models namely Logistic Regression, Gaussian Naive Bayes models and two non-parametric methods such as Random Forest, Decision Tree are implemented in this paper. All the mentioned models are developed and implemented in the field of Credit card fraud detection, bankruptcy detection, loan default prediction. In each of the aforementioned cases, the comparative study among the classification techniques is drawn and the best model is identified. The performance of each classifier on each considered domain is evaluated by various performance metrics such as accuracy, F1-score and mean squared error. In the credit card fraud detection model the decision tree classifier performs the best with an accuracy of 99.1% and, in the loan default prediction and bankruptcy detection model, the random forest classifier gives the best accuracy of  97% and 96.84% respectively.

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