Developing a Credit Card Fraud Detection Model using Machine Learning Approaches

Khan, Shahnawaz; Alourani, Abdullah; Mishra, Bharavi; Ashraf, Ali; Kamal, Mustafa

doi:10.14569/ijacsa.2022.0130350

Cited by 17 publications

(10 citation statements)

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“…Based on the results from Table III it is evident that there is a huge variation between accuracy and recall. The research in [21,42,43,44] does not investigate SVM kernels. In [42,44] both studies evaluated their models with accuracy which is not always an accurate metric indicator.…”

Section: Resultsmentioning

confidence: 99%

Comparison of SVM kernels in Credit Card Fraud Detection using GANs

Alshawi

2024

IJACSA

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The technological evolution in smartphones and telecommunication systems have led people to be more dependent on online shopping and electronic payments, which created burdensome task of transaction validation for many financial institutions. This paper examined and evaluated the efficacy of Support vector machine (SVM) kernels on Generative Adversarial Network (GAN)-generated synthetic data to detect credit card fraud transactions. Four SVM kernels have been investigated and compared; linear, polynomial, sigmoid, and redial basis function. The accuracy results indicated that linear and polynomial kernels reached over 91%, while sigmoid and redial basis function reached 79% and 83% respectively. Linear and polynomial models received over 90% ROC and F1 score, in contrast the ROC scores were lower for sigmoid (81%) and redial basis function (83%). Both sigmoid and redial basis function achieved over 80% in terms of F1 score. The precision score demonstrated a high score for both linear and polynomial kernel reaching 99%. Additionally, sigmoid and redial basis function achieved over 80%. These results overcame the imbalance dataset issue through the generation of synthetic data by applying the SVM kernels using GANs algorithm.

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Section: Resultsmentioning

confidence: 99%

Comparison of SVM kernels in Credit Card Fraud Detection using GANs

Alshawi

2024

IJACSA

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“…Various machine learning models are considered for the analysis, including Support vector machines (SVM), Random forest, k-nearest neighbours (kNN), adaBoost, catboost, etc. [4 , 5] . Each model has its own unique strengths and uses, so it's important to evaluate its performance consistently.…”

Section: Methods Detailsmentioning

confidence: 99%

Enhancing fraud detection in banking by integration of graph databases with machine learning

Patil,

Mahajan,

Menpara

et al. 2024

MethodsX

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“…3.1. Supervised learning for fraud detection Supervised learning for fraud detection which trains predictive models by employing previously established output and input information to forecast future outcomes [5]. Indeed, to create a credit-card fraud recognition model to classify digital transactions as unlawful or lawful accurately [1] have provided three techniques for supervised machine learning: logistic regression (LR), ANN (artificial neural network), and (SVM) support vector machines.…”

Section: Related Workmentioning

confidence: 99%

Digital banking fortification: a real-time isolation forest architecture for detecting online transaction fraud

Abbassi,

E L Mendili,

Gahi

2024

Eng. Res. Express

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Since the use of the Internet has increased exponentially, numerous organizations, including the financial industry, offer services online. As a result, financial scams are expanding in quantity and complexity worldwide, resulting in massive revenue losses and making digital fraudulent transactions a severe issue. Abnormal attempts and illegal access are instances of these dangers that fraudulent activity detection systems must identify. Machine learning and data mining approaches have been extensively used to address this issue in recent years.However, these approaches must be enhanced regarding real-time detection speed, tackling enormous amounts of data, and finding undiscovered attack patterns. Consequently, the present study provides a real-time architecture for averting and identifying digital transaction fraud, which relies on the Isolation Forest (IForest) approach and big data analytic tools, including Spark Streaming, sparkling water, Kafka, and PostgreSQL. This architecture seeks to improve present detection strategies by increasing accuracy for detection when considering enormous amounts of data. Two real datasets of online transactional fraud are used to assess the proposed architecture, and the findings are compared to relevant studies. The investigation results showed that IForest performed flawlessly, achieving an accuracy of 0.99 in two datasets.

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Developing a Credit Card Fraud Detection Model using Machine Learning Approaches

Cited by 17 publications

References 0 publications

Comparison of SVM kernels in Credit Card Fraud Detection using GANs

Comparison of SVM kernels in Credit Card Fraud Detection using GANs

Enhancing fraud detection in banking by integration of graph databases with machine learning

Digital banking fortification: a real-time isolation forest architecture for detecting online transaction fraud

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