On the benefits of machine learning classification in cashback fraud detection

Karunachandra, Bryan; Putera, Nathaniel; Wijaya, Stephen Rian; Suryani, Dewi; Wesley, Julian; Purnama, Yudy

doi:10.1016/j.procs.2022.12.147

Cited by 12 publications

(2 citation statements)

References 3 publications

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“…Ni et al [25] proposed a model for identifying credit card fraud that incorporates a spiral oversampling balancing technique (SOBT) and a method for boosting fraud attributes. In order to identify fraudulent cashback transactions in Indonesian e-commerce, Karunachandra et al [26] employed machine learning. They used transaction data from a prominent e-commerce platform in the nation to train their model and employed supervised classification techniques like k-NN, CNN, and LSTM.…”

Section: Related Workmentioning

confidence: 99%

A rule-based machine learning model for financial fraud detection

Islam,

Haque,

Rezaul Karim

2024

IJECE

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Financial fraud is a growing problem that poses a significant threat to the banking industry, the government sector, and the public. In response, financial institutions must continuously improve their fraud detection systems. Although preventative and security precautions are implemented to reduce financial fraud, criminals are constantly adapting and devising new ways to evade fraud prevention systems. The classification of transactions as legitimate or fraudulent poses a significant challenge for existing classification models due to highly imbalanced datasets. This research aims to develop rules to detect fraud transactions that do not involve any resampling technique. The effectiveness of the rule-based model (RBM) is assessed using a variety of metrics such as accuracy, specificity, precision, recall, confusion matrix, Matthew’s correlation coefficient (MCC), and receiver operating characteristic (ROC) values. The proposed rule-based model is compared to several existing machine learning models such as random forest (RF), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbor (KNN), naive Bayes (NB), and logistic regression (LR) using two benchmark datasets. The results of the experiment show that the proposed rule-based model beat the other methods, reaching accuracy and precision of 0.99 and 0.99, respectively.

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Section: Related Workmentioning

confidence: 99%

A rule-based machine learning model for financial fraud detection

Islam,

Haque,

Rezaul Karim

2024

IJECE

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“…Long Short-Term Memory (LSTM) yielded satisfactory precision, recall, and f1-score values [3]. The use of K-Nearest Neighbors (K-NN), CNN, LSTM indicated that the KNN algorithm performed the best among the machine learning algorithms in this case, achieving an accuracy of 83.82% [4]. Supervised machine learning models, including linear, non-linear, and ensemble models classified harmful and non-harmful activities.…”

mentioning

confidence: 99%

K-Means Clustering and Multilayer Perceptron for Categorizing Student Business Groups

Walid,

Sahbaniya,

Hozairi

et al. 2023

Knowl. Eng. Data Sci.

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The research conducted in this study was driven by the East Java provincial government's requirement to assess the transaction levels of the Student Business Group (KUS) in the SMA Double Track program. These transaction levels are a basis for allocating supplementary financial aid to each business group. The system's primary objective is to assist the provincial government of East Java in making well-informed choices pertaining to the distribution of supplementary capital to the KUS. The classification technique employed in this study is the multilayer perceptron. However, the K-Means Clustering method is utilised to generate target data due to the limited availability during the classification process, which involves dividing the transaction level attributes into three distinct groups: (0) low transactions, (1) medium transactions, and (2) high transactions. The clustering process encompasses three distinct features: (1) income, (2) spending, and (3) profit. These three traits will be utilized as input data throughout the categorization procedure. The classification procedure employing the Multilayer Perceptron technique involved processing a dataset including 1383 data points. The training data constituted 80% of the dataset, while the remaining 20% was allocated for testing. In order to evaluate the efficacy of the constructed model, the training error was assessed using K-Fold cross-validation, yielding an average accuracy score of 0.92. In the present study, the categorization technique yielded an accuracy of 0.96. This model aims to classify scenarios when the dataset lacks prior target data.

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Improving credit card fraud detection using machine learning and GAN technology

Thair Ali,

Jasim Hasan,

Ghandour

et al. 2024

BIO Web Conf.

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The motivation behind this study stems from identifying contemporary challenges associated with prosecuting electronic financial crimes. Highlights ongoing efforts to identify and address credit card fraud and fraud as there are many credit card fraud issues in the financial industry. Traditional methods are no longer able to keep up with modern methods of tracking the behavior of credit card users and detecting suspicious cases. Artificial intelligence technology offers promising solutions to quickly detect and prevent future fraud by credit card users. Datasets used to detect financial anomalies are affected by imbalances in financial transactions, and this study aims to address the imbalance of financial fraud datasets using adversarial algorithm techniques and compare them with the most commonly used methods in the scientific literature.The results showed that the function of the adversarial algorithm is consistent in several ways, including allowing researchers and interested parties to determine data growth rates, which helps bring the dataset closer to real-time data from financial markets and banks. This study proposes a hybrid machine learning model consisting of three machine learning algorithms: decision trees, logistic regression, and Naive Bayes algorithm, and calculates performance metrics such as accuracy, specificity, precision, and F1 score. Experimental results reveal varying degrees of accuracy in fraud detection. Model testing using the SMOTE method recorded an accuracy of 98.1% and an F-score of 98.3%. On the other hand, the oversampling and under sampling test methods showed similar performance, with the two methods recording an accuracy of 94.3 and 95.3 and an F-score of 94.7 and 95.1, respectively. Finally, the GAN method excelled, receiving a test score and accuracy of 99.9%, as well as exceptional precision, recall, and F1 score. As a result, we conclude that the GAN method is able to balance the data set, which in turn is reflected in the performance of the model in training and the accuracy of predictions when tested. Historical transaction analysis identifies behavioral patterns and adapts to evolving fraud techniques. This approach enhances transaction security and protects against potential financial losses due to fraud. This contribution allows financial institutions and companies to proactively combat fraudulent activities.

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On the benefits of machine learning classification in cashback fraud detection

Cited by 12 publications

References 3 publications

A rule-based machine learning model for financial fraud detection

A rule-based machine learning model for financial fraud detection

K-Means Clustering and Multilayer Perceptron for Categorizing Student Business Groups

Improving credit card fraud detection using machine learning and GAN technology

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