DEVELOPMENT OF CREDIT CARDS FRAUD DETECTION MODEL

The evaluation of fraud is of significant importance A credit card contains a compact, thin plastic card that carries information about the individual, such as a photograph or signature, and allows the person make charges on products and services connected to his account, which is deducted regularly. Financial institutions analyzes whether or not transactions are genuine in the future. This research develops a model for fraud detection. As a result of the importance of this study, the goal of this study is to develop a machine learning technique for predicting credit card fraud in the financial sector. The objectives of this research are to investigate machine learning techniques for detecting and analyzing online transactions and investigate credit card fraud cases involving the stealing of physical cards or fraudulently acquiring a victim's card information. The methods applied in this paper include collecting data from Kaggle, an online data collection tool. The Kaggle application programming interface was used in importing the dataset. In this study, cardholders spending behavior are input into the multilayer perceptron and used to train and test the system. This was determined on the training dataset (70%) and evaluated on the testing dataset (30%). The model is developed using a multilayer perceptron (MLP), together with an algorithm. The confusion matrix technique is used for evaluation. The evaluation of results is done by comparing its performance with the classifier using accuracy metrics. The model implementation was done using the python programming language. The data was passed into MLP with an algorithm classifier and the results were obtained with an accuracy of 93% and 99% respectively. This work is advantageous to the banking sector in predicting fraudulent transactions. The model is developed to improve the solution to fraud issues in the financial institutions secto