An Empirical Study of AML Approach for Credit Card Fraud Detection–Financial Transactions

Singh, Ajeet; Jain, Anurag

doi:10.15837/ijccc.2019.6.3498

Cited by 17 publications

(14 citation statements)

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“…Singh and Jain, ref. [ 32 ] enumerated and analysed work that applies adaptive machine learning (AML) techniques for credit card fraud detection. They analysed their performance with regard to sensitivity, specificity, and accuracy.…”

Section: Fraud Detection In the Fintech Domainmentioning

confidence: 99%

Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications

Stojanović

Božić

Hofer-Schmitz

et al. 2021

Sensors

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Financial technology, or Fintech, represents an emerging industry on the global market. With online transactions on the rise, the use of IT for automation of financial services is of increasing importance. Fintech enables institutions to deliver services to customers worldwide on a 24/7 basis. Its services are often easy to access and enable customers to perform transactions in real-time. In fact, advantages such as these make Fintech increasingly popular among clients. However, since Fintech transactions are made up of information, ensuring security becomes a critical issue. Vulnerabilities in such systems leave them exposed to fraudulent acts, which cause severe damage to clients and providers alike. For this reason, techniques from the area of Machine Learning (ML) are applied to identify anomalies in Fintech applications. They target suspicious activity in financial datasets and generate models in order to anticipate future frauds. We contribute to this important issue and provide an evaluation on anomaly detection methods for this matter. Experiments were conducted on several fraudulent datasets from real-world and synthetic databases, respectively. The obtained results confirm that ML methods contribute to fraud detection with varying success. Therefore, we discuss the effectiveness of the individual methods with regard to the detection rate. In addition, we provide an analysis on the influence of selected features on their performance. Finally, we discuss the impact of the observed results for the security of Fintech applications in the future.

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Section: Fraud Detection In the Fintech Domainmentioning

confidence: 99%

Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications

Stojanović

Božić

Hofer-Schmitz

et al. 2021

Sensors

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“…The principle of this method works based on a calculation between two data points, when the Euclidean distance is used as a measurement technique for all instances in the dataset. Next, these distances are arranged in an increasing order [18]. The performance of the KNN algorithm is determined by the following three main factors: 1) the distance used for the location of the nearest neighbours; 2) the distance rule used to deliver a classification from the nearest neighbour; and 3) the k number of neighbours used for classification of the outcome variable [15].…”

Section: K-nearest Neighbourmentioning

confidence: 99%

Efficient Resampling for Fraud Detection During Anonymised Credit Card Transactions with Unbalanced Datasets

Mrozek

Panneerselvam

Bagdasar

2020

2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)

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The rapid growth of e-commerce and online shopping have resulted in an unprecedented increase in the amount of money that is annually lost to credit card fraudsters. In an attempt to address credit card fraud, researchers are leveraging the application of various machine learning techniques for efficiently detecting and preventing fraudulent credit card transactions. One of the prevalent common issues around the analytics of credit card transactions is the highly unbalanced nature of the datasets, which is frequently associated with the binary classification problems. This paper intends to review, analyse and implement a selection of notable machine learning algorithms such as Logistic Regression, Random Forest, K-Nearest Neighbours and Stochastic Gradient Descent, with the motivation of empirically evaluating their efficiencies in handling unbalanced datasets whilst detecting credit card fraud transactions. A publicly available dataset comprising 284807 transactions of European cardholders is analysed and trained with the studied machine learning techniques to detect fraudulent transactions. Furthermore, this paper also evaluates the incorporation of two notable resampling methods, namely Random Under-sampling and Synthetic Majority Oversampling Techniques (SMOTE) in the aforementioned algorithms, in order to analyse their efficiency in handling unbalanced datasets. The proposed resampling methods significantly increased the detection ability, the most successful technique of combination of Random Forest with Random Under-sampling achieved the recall score of 100% in contrast to the recall score 77% of model without resampling technique. The key contribution of this paper is the postulation of efficient machine learning algorithms together with suitable resampling methods, suitable for credit card fraud detection with unbalanced dataset.

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“…However, the main problems are that the datasets are not available due to security concerns, and that the datasets are extremely unstable. Many machine learning‐based solutions have been proposed for credit card fraud detection 3,4,6,7,9‐12 …”

Section: Introductionmentioning

confidence: 99%

Predicting credit card fraud using multipurpose classification based on evolutionary rules

Dirik

2022

Security and Privacy

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Due to advances in Internet technology, credit card transactions are increasing faster than ever before. This has led to a fraud problem that affects businesses, cooperating institutions, and government agencies. Credit card fraud occurs when a third party uses your credit card or credit account for an unauthorized transaction. Given the increased security of credit card transactions, fraudsters are developing new tricks or exploiting new vulnerabilities. Thanks to evolving technology, it is possible to analyze the use of maliciously obtained data by studying the time and cost associated with account switching transactions. In this study, we propose the ENORA and NSGA-II methods to create rule-based classifiers that can be easily interpreted using the credit card fraud diagnosis dataset created by analyzing the time intervals of individuals' credit card usage. In this experiment, credit card payments are classified as fraudulent or nonfraudulent based on several variables. The experiments were conducted in full training mode and 10-fold cross-validation mode. The performance of the algorithm was measured by accuracy, area under the receiver operating characteristic (ROC) curve, mean square error, proportion of true positives, proportion of false positives, precision, recall, F-measure, area under the curve (AUC), andMatthews correlation coefficient. The classification performance of the model using the correct classification ratio (ACC) is as follows: NSGA-II = 94.244%, ENORA = 93.236%. The performance of the other metrics is also discussed in detail in the results section. These results illustrate the significant predictive power of the proposed credit card theft models. After a thorough statistical analysis of our results, we found that the proposed strategy is capable of producing accurate and easy-to-understand categorization models.

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An Empirical Study of AML Approach for Credit Card Fraud Detection–Financial Transactions

Cited by 17 publications

References 0 publications

Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications

Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications

Efficient Resampling for Fraud Detection During Anonymised Credit Card Transactions with Unbalanced Datasets

Predicting credit card fraud using multipurpose classification based on evolutionary rules

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