Enhanced Anomaly Detection in Imbalanced Credit Card Transactions using Hybrid PSO

Sivakumar, N.; Balasubramanian, R.

doi:10.5120/ijca2016908520

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…The huge advancement in technology and communication systems led to the improvement of electronic payment services, such as e-commerce and mobile payments, to facilitate online money transactions and save the customer time [1,2]. Most of these e-services accept credit cards issued by a bank or non-banking financial institution to the cardholder to purchase goods [3,4]. Figure 1 depicts e-commerce as a percentage of global retail sales from 2015 to 2020, with forecasts from 2021 to 2025 [5].…”

Section: Introductionmentioning

confidence: 99%

Credit Card Fraud Detection Challenges and Solutions: A Review

Saad,

Nadher,

Hameed

2024

Iraqi Journal of Science

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Credit card fraud has become an increasing problem due to the growing reliance on electronic payment systems and technological advances that have improved fraud techniques. Numerous financial institutions are looking for the best ways to leverage technological advancements to provide better services to their end users, and researchers used various protection methods to provide security and privacy for credit cards. Therefore, it is necessary to identify the challenges and the proposed solutions to address them. This review provides an overview of the most recent research on the detection of fraudulent credit card transactions to protect those transactions from tampering or improper use, which includes imbalance classes, concept drift, and verification latency problems using machine learning and deep learning. It also provides valuable information for academic and industrial researchers and opens new avenues for research aimed at developing robust fraud detection systems.

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Section: Introductionmentioning

confidence: 99%

Credit Card Fraud Detection Challenges and Solutions: A Review

Saad,

Nadher,

Hameed

2024

Iraqi Journal of Science

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“…Outlier detection refers to the process of identifying data points that deviate significantly from normal data point clusters. Up to now, outlier detection has been wildly applied in diverse fields of science and technology, such as credit card transactions (Sivakumar & Balasubramanian, 2016;Nami & Shajari, 2018), intrusion detection (Aoudi et al, 2018), industrial control system inspection (Lin et al, 2018;Das et al, 2020), text detection (Mahapatra et al, 2012;Gorokhov et al, 2017) and outlier detection in biological data (Shetta & Niranjan, 2020;Tibshirani & Hastie, 2007;MacDonald & Ghosh, 2006). For multidimensional data points, there are various outliers.…”

Section: Introductionmentioning

confidence: 99%

The Importance of Suppressing Complete Reconstruction in Autoencoders for Unsupervised Outlier Detection

Shen¹,

Yang²

2022

Preprint

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Autoencoders are widely used in outlier detection due to their superiority in handling high-dimensional and nonlinear datasets. The reconstruction of any dataset by the autoencoder can be considered as a complex regression process. In regression analysis, outliers can usually be divided into high leverage points and influential points. Although the autoencoder has shown good results for the identification of influential points, there are still some problems when detect high leverage points. Through theoretical derivation, we found that most outliers are detected in the direction corresponding to the worst-recovered principal component, but in the direction of the well-recovered principal components, the anomalies are often ignored. We propose a new loss function which solve the above deficiencies in outlier detection. The core idea of our scheme is that in order to better detect high leverage points, we should suppress the complete reconstruction of the dataset to convert high leverage points into influential points, and it is also necessary to ensure that the differences between the eigenvalues of the covariance matrix of the original dataset and their corresponding reconstructed results in the direction of each principal component are equal. Besides, we explain the rationality of our scheme through rigorous theoretical derivation. Finally, our experiments on multiple datasets confirm that our scheme significantly improves the accuracy of outlier detection.

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“…The applications are in the credit card system if fraud happens once people can apply for a new card or change the name of the card. (Sivakumar and Balasubramanian 2016). If a transaction is done wrong due to the incorrect PIN.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Method to Enhance the Accuracy of Credit Card Fraud Detection Using Logistic Regression Algorithm by Comparing Random Forest Algorithm

Poojitha¹,

Malathi²

2022

ECS Trans.

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Aim: The objective is to develop a model to predict which transactions could be fraud with a high accuracy rate by using machine learning(ML) methods, such as the innovative Random Forest Algorithm. Materials and Methods: F1 score and accuracy rate are performed on fraud rates transactions in a dataset. The two groups (LR) the sample size is (N=20) and (RF) is (N=20) with G power value = 80. Results: To perform the transactions in the credit card by utilizing the card number, financial balance and client details, and if it is offline, the fraud has to steal the user's details in online transactions, which is not easily recognized by the client and bank authority, which prompts loss of sensitive data. (p<0.05) statistically significant value is compared to the amount and time since last purchase with a confidence level of 95% (=.519). (RF) algorithm is used for detecting credit card fraud. The accuracy is analyzed based on the dataset of 99.3%, where the (LR) has the accuracy of 99.1%. Conclusion: The performance of strategy is measured based on accuracy, F1 score, Precision, Recall. The results seem to be better accuracy for (RF) is 93.3%.

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Enhanced Anomaly Detection in Imbalanced Credit Card Transactions using Hybrid PSO

Cited by 4 publications

References 19 publications

Credit Card Fraud Detection Challenges and Solutions: A Review

Credit Card Fraud Detection Challenges and Solutions: A Review

The Importance of Suppressing Complete Reconstruction in Autoencoders for Unsupervised Outlier Detection

An Innovative Method to Enhance the Accuracy of Credit Card Fraud Detection Using Logistic Regression Algorithm by Comparing Random Forest Algorithm

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