Facilitating User Authorization from Imbalanced Data Logs of Credit Cards Using Artificial Intelligence
Joint Authors
Arora, Vinay
Leekha, Rohan Singh
Lee, Kyungroul
Kataria, Aman
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-30
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Telecommunications Engineering
Abstract EN
An effective machine learning implementation means that artificial intelligence has tremendous potential to help and automate financial threat assessment for commercial firms and credit agencies.
The scope of this study is to build a predictive framework to help the credit bureau by modelling/assessing the credit card delinquency risk.
Machine learning enables risk assessment by predicting deception in large imbalanced data by classifying the transaction as normal or fraudster.
In case of fraud transaction, an alert can be sent to the related financial organization that can suspend the release of payment for particular transaction.
Of all the machine learning models such as RUSBoost, decision tree, logistic regression, multilayer perceptron, K-nearest neighbor, random forest, and support vector machine, the overall predictive performance of customized RUSBoost is the most impressive.
The evaluation metrics used in the experimentation are sensitivity, specificity, precision, F scores, and area under receiver operating characteristic and precision recall curves.
Datasets used for training and testing of the models have been taken from kaggle.com.
American Psychological Association (APA)
Arora, Vinay& Leekha, Rohan Singh& Lee, Kyungroul& Kataria, Aman. 2020. Facilitating User Authorization from Imbalanced Data Logs of Credit Cards Using Artificial Intelligence. Mobile Information Systems،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1192581
Modern Language Association (MLA)
Arora, Vinay…[et al.]. Facilitating User Authorization from Imbalanced Data Logs of Credit Cards Using Artificial Intelligence. Mobile Information Systems No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1192581
American Medical Association (AMA)
Arora, Vinay& Leekha, Rohan Singh& Lee, Kyungroul& Kataria, Aman. Facilitating User Authorization from Imbalanced Data Logs of Credit Cards Using Artificial Intelligence. Mobile Information Systems. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1192581
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1192581