Fraud Detection in Credit Cards using Logistic Regression

Alenzi, Hala Z; Aljehane, Nojood O.

doi:10.14569/ijacsa.2020.0111265

Cited by 20 publications

(10 citation statements)

References 19 publications

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“…Using unbalanced data, the study developed a classification model that is extremely resistant. New system uses LR to build the classifier proposed in Alenzi & Aljehane (2020) . Comparing the proposed LR-based classifier against the KNN and voting classifiers.…”

Section: Results and Analysismentioning

confidence: 99%

A systematic review of literature on credit card cyber fraud detection using machine and deep learning

Btoush

Zhou

Gururajan

et al. 2023

PeerJ Computer Science

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The increasing spread of cyberattacks and crimes makes cyber security a top priority in the banking industry. Credit card cyber fraud is a major security risk worldwide. Conventional anomaly detection and rule-based techniques are two of the most common utilized approaches for detecting cyber fraud, however, they are the most time-consuming, resource-intensive, and inaccurate. Machine learning is one of the techniques gaining popularity and playing a significant role in this field. This study examines and synthesizes previous studies on the credit card cyber fraud detection. This review focuses specifically on exploring machine learning/deep learning approaches. In our review, we identified 181 research articles, published from 2019 to 2021. For the benefit of researchers, review of machine learning/deep learning techniques and their relevance in credit card cyber fraud detection is presented. Our review provides direction for choosing the most suitable techniques. This review also discusses the major problems, gaps, and limits in detecting cyber fraud in credit card and recommend research directions for the future. This comprehensive review enables researchers and banking industry to conduct innovation projects for cyber fraud detection.

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Section: Results and Analysismentioning

confidence: 99%

A systematic review of literature on credit card cyber fraud detection using machine and deep learning

Btoush

Zhou

Gururajan

et al. 2023

PeerJ Computer Science

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“…Because it can classify new data using both continuous and www.ijacsa.thesai.org discrete datasets, logistic regression is a significant machine learning approach. The equation of logistic regression of straight line written as [22]:…”

Section: Logistic Regressionmentioning

confidence: 99%

Detection of Credit Card Fraud using a Hybrid Ensemble Model

Saraf¹,

Phakatkar²

2022

IJACSA

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The rising number of credit card frauds presents a significant challenge for the banking industry. Many businesses and financial institutions suffer huge losses because card users are reluctant to use their cards. A primary goal of fraud detection is to identify prior transaction patterns to detect future fraud. In this paper, a hybrid ensemble model is proposed to combine bagging and boosting techniques to distinguish between fraudulent and legitimate transactions. During the experimentation two datasets are used; the European credit card dataset and the credit card stimulation dataset which are highly imbalanced. The oversampling method is used to balance both datasets. To overcome the problem of unbalanced data oversampling method is used. The model is trained to predict output results by combining random forest with Adaboost. The proposed model provides 98.27 % area under curve score on the European credit cards dataset and the stimulation credit card dataset gives 99.3 % area under curve score.

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“…With the many challenges of infrastructure and coverage areas [4]- [6] -banks now introduce technologies and platforms such as credit cards, agent banking, point of sales, etc -as means to remain connected with their numerous customer-base, and also ensure financial inclusivity [7]. Credit cards are issued by banks today as a cornerstone to facilitate payments for goods and services [8]- [10]. It is a pocket-sized metal card that empowers its holder -consolidating their transaction prowess into a single, easily manageable form.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Random Forest Model via Synthetic Minority Oversampling Technique for Credit-Card Fraud Detection

Aghware,

Ojugo,

Adigwe

et al. 2024

J. Comput. Theor. Appl.

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Fraudsters increasingly exploit unauthorized credit card information for financial gain, targeting un-suspecting users, especially as financial institutions expand their services to semi-urban and rural areas. This, in turn, has continued to ripple across society, causing huge financial losses and lowering user trust implications for all cardholders. Thus, banks cum financial institutions are today poised to implement fraud detection schemes. Five algorithms were trained with and without the application of the Synthetic Minority Over-sampling Technique (SMOTE) to assess their performance. These algorithms included Random Forest (RF), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machines (SVM), and Logistic Regression (LR). The methodology was implemented and tested through an API using Flask and Streamlit in Python. Before applying SMOTE, the RF classifier outperformed the others with an accuracy of 0.9802, while the accuracies for LR, KNN, NB, and SVM were 0.9219, 0.9435, 0.9508, and 0.9008, respectively. Conversely, after the application of SMOTE, RF achieved a prediction accuracy of 0.9919, whereas LR, KNN, NB, and SVM attained accuracies of 0.9805, 0.9210, 0.9125, and 0.8145, respectively. These results highlight the effectiveness of combining RF with SMOTE to enhance prediction accuracy in credit card fraud detection.

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Fraud Detection in Credit Cards using Logistic Regression

Cited by 20 publications

References 19 publications

A systematic review of literature on credit card cyber fraud detection using machine and deep learning

A systematic review of literature on credit card cyber fraud detection using machine and deep learning

Detection of Credit Card Fraud using a Hybrid Ensemble Model

Enhancing the Random Forest Model via Synthetic Minority Oversampling Technique for Credit-Card Fraud Detection

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