Implementation of Credit Card Fraud Detection System with Concept Drifts Adaptation

Jog, Anita; Chandavale, Anjali

doi:10.1007/978-981-10-7245-1_46

Cited by 6 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 5 shows a performance comparison between the proposed approach and other research outcomes based on achieved accuracy for the same data set. The proposed approach obtained the highest Accuracy (98.40%), while the Concept Drifts Adaptation [31] achieved the lowest Accuracy (80%).…”

Section: Resultsmentioning

confidence: 97%

An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine

2020

View full text Add to dashboard Cite

New advances in electronic commerce systems and communication technologies have made the credit card the potentially most popular method of payment for both regular and online purchases; thus, there is significantly increased fraud associated with such transactions. Fraudulent credit card transactions cost firms and consumers large financial losses every year, and fraudsters continuously attempt to find new technologies and methods for committing fraudulent transactions. The detection of fraudulent transactions has become a significant factor affecting the greater utilization of electronic payment. Thus, there is a need for efficient and effective approaches for detecting fraud in credit card transactions. This paper proposes an intelligent approach for detecting fraud in credit card transactions using an optimized light gradient boosting machine (OLightGBM). In the proposed approach, a Bayesian-based hyperparameter optimization algorithm is intelligently integrated to tune the parameters of a light gradient boosting machine (LightGBM). To demonstrate the effectiveness of our proposed OLightGBM for detecting fraud in credit card transactions, experiments were performed using two real-world public credit card transaction data sets consisting of fraudulent transactions and legitimate ones. Based on a comparison with other approaches using the two data sets, the proposed approach outperformed the other approaches and achieved the highest performance in terms of accuracy (98.40%), Area under receiver operating characteristic curve (AUC) (92.88%), Precision (97.34%) and F1-score (56.95%).

show abstract

Section: Resultsmentioning

confidence: 97%

An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine

2020

View full text Add to dashboard Cite

show abstract

“…Due to this, some researchers have included the detection of concept drifts as part of their solution. [9] handled concept drift both as an active solution by identifying changes in statistics and a passive solution by continuously updating the model using new records. [14] used face to face transactions in order to compute the distance in concept drift between consecutive transactions and added this as a new feature to the model.…”

Section: Related Workmentioning

confidence: 99%

Framework for Credit Card Fraud Detection Using Benefit-Based Learning and Periodic Features

Sooklal

Hosein

2023

Preprint

View full text Add to dashboard Cite

Online credit card fraud is an ongoing problem and with the recent COVID-19 pandemic, there has been a surge of merchants moving their businesses online. It is therefore crucial to identify fraudulent activities before it causes loss to both the bank and its customers. Due to the dynamic nature of fraudsters as well as customer spending behavior, machine learning algorithms are appropriate for this task. However, credit card fraud data is typically imbalanced, favoring the positive class (legitimate transactions), causing traditional machine learning algorithms to err on the side of this majority class; since they consider equal costs and benefits for different decision outcomes when training. Nevertheless, it is more beneficial to correctly identify fraudulent transactions. Therefore, in this paper, we propose a technique for identifying credit card fraud that first accounts for customer spending patterns by aggregating transactions to creative new features based on periodic data. Then, we consider benefits and costs when training an XGBoost classifier in order to achieve maximum benefits. We also evaluate the performance of the classifier using benefits and costs. We demonstrate the effectiveness of our approach using data provided by a bank.

show abstract

Importance of Self-Learning Algorithms for Fraud Detection Under Concept Drift

Shamitha¹,

Ilango

2022

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Implementation of Credit Card Fraud Detection System with Concept Drifts Adaptation

Cited by 6 publications

References 8 publications

An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine

An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine

Framework for Credit Card Fraud Detection Using Benefit-Based Learning and Periodic Features

Importance of Self-Learning Algorithms for Fraud Detection Under Concept Drift

Contact Info

Product

Resources

About