Machine Learning Based on Resampling Approaches and Deep Reinforcement Learning for Credit Card Fraud Detection Systems

The problem of imbalanced datasets is a significant concern when creating reliable credit card fraud (CCF) detection systems. In this work, we study and evaluate recent advances in machine learning (ML) algorithms and deep reinforcement learning (DRL) used for CCF detection systems, including fraud...

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Vydáno v:Applied sciences Ročník 11; číslo 21; s. 10004
Hlavní autoři: Dang, Tran Khanh, Tran, Thanh Cong, Tuan, Luc Minh, Tiep, Mai Viet
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.11.2021
Témata:
Algorithms
Classification
Credit card fraud
Credit card processing
Data mining
Datasets
Deep learning
deep reinforcement learning
Feature selection
imbalanced data
Machine learning
resampling techniques
ISSN:2076-3417, 2076-3417
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Shrnutí:The problem of imbalanced datasets is a significant concern when creating reliable credit card fraud (CCF) detection systems. In this work, we study and evaluate recent advances in machine learning (ML) algorithms and deep reinforcement learning (DRL) used for CCF detection systems, including fraud and non-fraud labels. Based on two resampling approaches, SMOTE and ADASYN are used to resample the imbalanced CCF dataset. ML algorithms are, then, applied to this balanced dataset to establish CCF detection systems. Next, DRL is employed to create detection systems based on the imbalanced CCF dataset. The diverse classification metrics are indicated to thoroughly evaluate the performance of these ML and DRL models. Through empirical experiments, we identify the reliable degree of ML models based on two resampling approaches and DRL models for CCF detection. When SMOTE and ADASYN are used to resampling original CCF datasets before training/test split, the ML models show very high outcomes of above 99% accuracy. However, when these techniques are employed to resample for only the training CCF datasets, these ML models show lower results, particularly in terms of logistic regression with 1.81% precision and 3.55% F1 score for using ADASYN. Our work reveals the DRL model is ineffective and achieves low performance, with only 34.8% accuracy.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app112110004