Credit Card Fraud Detection: A Systematic Review

Priscilla, C. Victoria; Prabha, D. Padma

doi:10.1007/978-3-030-38501-9_29

Cited by 9 publications

(6 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They also identified the normal usage pattern of credit card users based on their past transactions. The readers are referred to the review paper (C. V. Priscilla & Prabha, 2020) for a detailed description of the methods used by several researchers in the field of credit card fraud detection, like supervised, unsupervised, and ensemble learning.…”

Section: Related Workmentioning

confidence: 99%

A combined framework based on LSTM autoencoder and XGBoost with adaptive threshold classification for credit card fraud detection

Prabha,

Priscilla

2024

The Scientific Temper

View full text Add to dashboard Cite

The digital invasion of the banking and financial sectors made life simple and easy. Traditional machine learning models have been studied in credit card fraud detection, but these models are often difficult to find effective for unseen patterns. This study proposes a combined framework of deep learning and machine learning models. The long short term memory autoencoder (LSTMAE) with attention mechanism is developed to extract high-level features and avoid overfitting of the model. The extracted features serve as input to the powerful ensemble model XGBoost to classify legitimate and fraudulent transactions. As the focus of fraud detection is to increase the recall rate, an adaptive threshold technique is proposed to estimate an optimal threshold value to enhance performance. The experiment was done with the IEEE-CIS fraud detection dataset available in Kaggle. The proposed model with optimal threshold has an increase in predicting fraudulent transactions. The research findings are compared with conventional ensemble techniques to find the generalization of the model. The proposed LSTMAE-XGB w/ attention method attained a good precision and recall of 94.2 and 90.5%, respectively, at the optimal threshold of θ = 0.22. The experimental results proved that the proposed approach is better at finding fraudulent transactions than other cutting-edge models

show abstract

Section: Related Workmentioning

confidence: 99%

A combined framework based on LSTM autoencoder and XGBoost with adaptive threshold classification for credit card fraud detection

Prabha,

Priscilla

2024

The Scientific Temper

View full text Add to dashboard Cite

show abstract

“…CCFD is one of the challenging ongoing research from the research community as the fraudsters change their pattern of conduct during the transaction. Hence, it is difficult for a bank to fix a solution since fraud is detected after the occurrence [2]. Another challenge confronted by researchers is the imbalance in data.…”

Section: Related Workmentioning

confidence: 99%

“…Progressive research are been carried out predominantly in credit card fraud detection (CCFD) through machine learning (ML) and deep learning (DL) models. Many approaches [2]- [6] have been proposed for CCFD in the literature. A systematic survey paper on CCFD [2] is suggested to the readers for the detailed research works done with Machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

“…Many approaches [2]- [6] have been proposed for CCFD in the literature. A systematic survey paper on CCFD [2] is suggested to the readers for the detailed research works done with Machine learning methods. The literature identifies the challenges met by the researchers in CCFD are concept driftchange in behavioral patterns of the customers during their purchase [3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Probabilistic XGBoost Threshold Classification with Autoencoder for Credit Card Fraud Detection

Prabha,

Priscilla

2023

IJRITCC

View full text Add to dashboard Cite

Due to the imbalanced data of outnumbered legitimate transactions than the fraudulent transaction, the detection of fraud is a challenging task to find an effective solution. In this study, autoencoder with probabilistic threshold shifting of XGBoost (AE-XGB) for credit card fraud detection is designed. Initially, AE-XGB employs autoencoder the prevalent dimensionality reduction technique to extract data features from latent space representation. Then the reconstructed lower dimensional features utilize eXtreame Gradient Boost (XGBoost), an ensemble boosting algorithm with probabilistic threshold to classify the data as fraudulent or legitimate. In addition to AE-XGB, other existing ensemble algorithms such as Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), Random Forest, Categorical Boosting (CatBoost), LightGBM and XGBoost are compared with optimal and default threshold. To validate the methodology, we used IEEE-CIS fraud detection dataset for our experiment. Class imbalance and high dimensionality characteristics of dataset reduce the performance of model hence the data is preprocessed and trained. To evaluate the performance of the model, evaluation indicators such as precision, recall, f1-score, g-mean and Mathews Correlation Coefficient (MCC) are accomplished. The findings revealed that the performance of the proposed AE-XGB model is effective in handling imbalanced data and able to detect fraudulent transactions with 90.4% of recall and 90.5% of f1-score from incoming new transactions.

show abstract

“…1 The most prominent ML model for CCFD in research and practice nowadays is Random Forests [4,8]. Several studies have reported 1 [6,19,20] have carried out systematic literature reviews. their superior performance when compared to others [7,14,21].…”

Section: Credit Card Fraud Detectionmentioning

confidence: 99%

False Positives in Credit Card Fraud Detection: Measurement and Mitigation

Wallny¹

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

Credit Card Fraud Detection is a classification problem where different types of classification errors cause different costs. Previous works quantified the financial impact of data-driven fraud detection classifiers using a cost-matrix based evaluation approach, however, none of them considered the significant financial impact of false positives. Analysts reported that fraud prediction in e-commerce still has to deal with false positive rates of 30-70%, and many cardholders reduce card usage after being wrongly declined. In our paper, we propose a new method for assessing the cost of false positives and evaluate several state-of-the-art fraud detection classifiers using this method. Further, we investigate the effectiveness of ensemble learning as previous work supposed that a combination of diverse, individual classifiers can improve performance. Our results show that cost-based evaluation yields valuable insights for practitioners and that our ensemble learning strategy indeed cuts fraud cost by almost 30%.

show abstract

Credit Card Fraud Detection: A Systematic Review

Cited by 9 publications

References 37 publications

A combined framework based on LSTM autoencoder and XGBoost with adaptive threshold classification for credit card fraud detection

A combined framework based on LSTM autoencoder and XGBoost with adaptive threshold classification for credit card fraud detection

Probabilistic XGBoost Threshold Classification with Autoencoder for Credit Card Fraud Detection

False Positives in Credit Card Fraud Detection: Measurement and Mitigation

Contact Info

Product

Resources

About