The HASC-DLCCFD was tested on a simulated Sparkov credit card dataset, achieving an accuracy of 99.5%, recall of 99.4%, and AUC of 98.5%. The problem of imbalanced data is not dealt with in the study, and there is a lack of comprehensive information on how its parameters were ...
In the case of credit card datasets, there are still insufficient fraud data to train supervised models. Further, the accuracy rate of anomaly detection in credit card transactions is often lower due to the complex and dynamic nature of the dataset. As a result, single supervised machine ...
In the specific case of the credit card clients, only about 22.1% of the data are labelled as defaulters (y=1).Number of rowsPercentage Non-defaulters (class=0) 17246 77.68 % Defaulters (class=1) 4954 22.32 %While the obvious and most desirable solution would be to collect more real ...
The feature extraction techniques used in this study are further described in the “Background information” section. Fig. 1 Feature extraction process [8] Full size image Our motivation for this work comes from the fact that there are yearly increases in the number of credit card fraud ...
The model aims to analyze familiar data, such as timely payment, low credit card balance, bank accounts, assets, etc., to evaluate the credit score. As you see, these systems have been around for a long time and are used by many financial institutions to determine whether or not to give...
In this case, they would use their own risk modeling methodologies to calculate a risk number and use their own formulas to calculate the buffer capital. This leads us to the concept of economic capital, which is the amount of capital a bank has based on its internal modeling strategy and ...
M. (2024). Credit card fraud detection using ımproved deep learning models. Computers, Materials and Continua, 78(1), 1049–1069. https://doi.org/10.32604/cmc.2023.046051 Article Google Scholar Thomas, L. C. (2000). A survey of credit and behavioural scoring: Forecasting financial risk...
To predict credit card default, this study evaluates the efficacy of a DL model and compares it to other ML models, such as Decision Tree (DT) and Adaboost. The objective of this research is to identify the specific DL parameters that contribute to the observed enhancements in the accuracy ...
Enablers and barriers of online learning during the COVID-19 pandemic: A case study of an online university course Journal of University Teaching & Learning Practice, 18 (4) (2021), p. 11 View in ScopusGoogle Scholar Kariv and Heiman, 2005 D. Kariv, T. Heiman Task-oriented versus emotion...
A consumer provides authorization to a card match system to access the consumer's credit file to match the consumer to one or more credit cards. The consumer's credit score is retri