Advancement of management information system for discovering fraud in master card based intelligent supervised machine learning and deep learning during SARS-CoV2. Issue 2 (March 2023)

• Record Type: Journal Article
• Authors: Wu, Banghua; Lv, Xuebin; Alghamdi, Abdullah; Abosaq, Hamad; Alrizq, Mesfer
• Is Part Of: Information processing & management. Volume 60:Issue 2(2023)
• Publication Date: 2023-03
• Subjects: Supervised machine learning -- Fraud discovering -- Master card -- Long short-term memory LSTM -- SARS-CoV2; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2022.103231
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: Model outperforms the competition in terms of detecting fraudulent transactions. Customers won't torment by financial disputes by preventing unlawful transactions. LSTM algorithms are used to identify fraudulent transactions during shutdown period. ML used to identify types of transactions that are most likely to be fraudulent. AI was used because it can constantly modify & update its rules for new transactions. Abstract: During coronavirus (SARS-CoV2) the number of fraudulent transactions is expanding at a rate of alarming (7, 352, 421 online transaction records). Additionally, the Master Card (MC) usage is increasing. To avoid massive losses, companies of finance must constantly improve their management information systems for discovering fraud in MC. In this paper, an approach of advancement management information system for discovering of MC fraud was developed using sequential modeling of data depend on intelligent forecasting methods such as deep Learning and intelligent supervised machine learning (ISML). The Long Short-Term Memory Network (LSTM), Logistic Regression (LR), and Random Forest (RF) were used. The dataset is separated into two parts: the training and testing data, with a ratio of 8:2. Also, the advancement of management information system has been evaluated using 10-fold cross validation depend on recall, f1-score, precision, Mean Absolute Error (MAE), Receiver Operating Curve (ROC), and Root Mean Square Error (RMSE). Finally various techniques of resampling used to forecast if a transaction of MC is genuine/fraudulent. Performance for without re-sampling, with under-sampling, and with over-sampling is measured for each Algorithm. Highest performance of without re-sampling was 0.829 for RF algorithm-F score. While for under-sampling, it was 0.871 for LSTM algorithm-RMSE. Further, for over-sampling, it was 0.921 for both RF algorithm-Precision and LSTM algorithm-F score. The results from running advancement of management information system revealed that using resampling technique with deep learning LSTM generated the best results than intelligent supervised machine learning.