Credit Card Fraud Detection Based on Hyperparameters ‎Optimization Using the Differential Evolution

Tayebi, Mohammed; Kafhali, Said El

doi:10.4018/ijisp.314156

Cited by 11 publications

(6 citation statements)

References 33 publications

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“…Additionally, Vengatesan et al [ 43 ] tested the performance of LR and KNN on an unbalanced credit card fraud dataset, and Hema [ 44 ] used RF, LR, and category boosting (CatBoost) to identify credit card fraud. Additionally, Asha and KR [ 45 ] proposed an approach utilizing SVM, KNN, and ANN models to identify credit card fraud. However, none of the authors (Khatri et al, [ 41 ]; Taha and Malebary, [ 42 ]; Vengatesan et al, [ 43 ]; Hema, [ 44 ]; Asha and KR, [ 45 ]) mentioned the issue of class imbalance.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Additionally, Asha and KR [ 45 ] proposed an approach utilizing SVM, KNN, and ANN models to identify credit card fraud. However, none of the authors (Khatri et al, [ 41 ]; Taha and Malebary, [ 42 ]; Vengatesan et al, [ 43 ]; Hema, [ 44 ]; Asha and KR, [ 45 ]) mentioned the issue of class imbalance. Some researchers used the differential evolution hyperparameter optimization approach to identify fraudulent credit card transactions, differential evolution (DE) algorithm to address the issue of data imbalance, and optimized XGBoost algorithm to categorize fraudulent transactions [ 46 ].…”

Section: Literature Reviewmentioning

confidence: 99%

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A soft voting ensemble learning approach for credit card fraud detection

Azim Mim,

Majadi,

Mazumder

2024

Heliyon

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A soft voting ensemble learning approach for credit card fraud detection

Azim Mim,

Majadi,

Mazumder

2024

Heliyon

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“…This solution is benchmarked on a real credit card dataset, and it shows higher performance in stopping abnormal transactions. Furthermore, the authors proposed a credit card fraud transaction detection method based on hyperparameter optimization [22]. They used a differential evolution algorithm for selecting the performing hyperparameters of the XGboost algorithm.…”

Section: Related Workmentioning

confidence: 99%

An Optimized Deep Learning Approach for Detecting Fraudulent Transactions

El Kafhali,

Tayebi,

Sulimani

2024

Information

Self Cite

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The proliferation of new technologies and advancements in existing ones are altering our perspective of the world. So, continuous improvements are needed. A connected world filled with a vast amount of data was created as a result of the integration of these advanced technologies in the financial sector. The advantages of this connection came at the cost of more sophisticated and advanced attacks, such as fraudulent transactions. To address these illegal transactions, researchers and engineers have created and implemented various systems and models to detect fraudulent transactions; many of them produce better results than others. On the other hand, criminals change their strategies and technologies to imitate legitimate transactions. In this article, the objective is to propose an intelligent system for detecting fraudulent transactions using various deep learning architectures, including artificial neural networks (ANNs), recurrent neural networks (RNNs), and long short-term memory (LSTM). Furthermore, the Bayesian optimization algorithm is used for hyperparameter optimization. For the evaluation, a credit card fraudulent transaction dataset was used. Based on the many experiments conducted, the RNN architecture demonstrated better efficiency and yielded better results in a shorter computational time than the ANN LSTM architectures.

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“…Tere are also some relevant scholars who use the diferential evolution superparameter optimization method to detect credit card fraud, use the diferential evolution algorithm to deal with the data imbalance problem, and use the optimized XG-Boost algorithm to classify fraudulent transactions. Te model has high accuracy after evaluation [16]. Some scholars also analyze the detection performance of fraudulent transactions based on a meta-heuristic algorithm and use meta-heuristic technology to optimize the superparameters, which can validly enhance the usefulness of fraud examination systems, simplify the detection process, and shorten the detection time [17].…”

Section: Research Progress On Prevention Of Digital Financial Risk In...mentioning

confidence: 99%

Research on Supply Chain Financial Risk Prevention Based on Machine Learning

Lei

Qiaoming

Zhao

2023

Computational Intelligence and Neuroscience

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Artificial intelligence (AI) proves decisive in today’s rapidly developing society and is a motive force for the evolution of financial technology. As a subdivision of artificial intelligence research, machine learning (ML) algorithm is extensively used in all aspects of the daily operation and development of the supply chain. Using data mining, deductive reasoning, and other characteristics of machine learning algorithms can effectively help decision-makers of enterprises to make more scientific and reasonable decisions by using the existing financial index data. At present, globalization uncertainties such as COVID-19 are intensifying, and supply chain enterprises are facing bankruptcy risk. In the operation process, practical tools are needed to identify and opportunely respond to the threat in the supply chain operation promptly, predict the probability of business failure of enterprises, and take scientific and feasible measures to prevent a financial crisis in good season. Artificial intelligence decision-making technology can help traditional supply chains to transform into intelligent supply chains, realize smart management, and promote supply chain transformation and upgrading. By applying machine learning algorithms, the supply chain can not only identify potential risks in time and adopt scientific and feasible measures to deal with the crisis but also strengthen the connection and cooperation between different enterprises with the advantage of advanced technology to provide overall operation efficiency. On account of this, the paper puts forward an artificial intelligence-based corporate financial-risk-prevention (FRP) model, which includes four stages: data preprocessing, feature selection, feature classification, and parameter adjustment. Firstly, relevant financial index data are collected, and the quality of the selected data is raised through preprocessing; secondly, the chaotic grasshopper optimization algorithm (CGOA) is used to simulate the behavior of grasshoppers in nature to build a mathematical model, and the selected data sets are selected and optimized for features. Then, the support vector machine (SVM) performs classification processing on the quantitative data with reduced features. Empirical risk is calculated using the hinge loss function, and a regular operation is added to optimize the risk structure. Finally, slime mould algorithm (SMA) can optimize the process to improve the efficiency of SVM, making the algorithm more accurate and effective. In this study, Python is used to simulate the function of the corporate business finance risk prevention model. The experimental results show that the CGOA-SVM-SMA algorithm proposed in this paper achieves good results. After calculation, it is found that the prediction and decision-making capabilities are good and better than other comparative models, which can effectively help supply chain enterprises to prevent financial risks.

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Credit Card Fraud Detection Based on Hyperparameters ‎Optimization Using the Differential Evolution

Cited by 11 publications

References 33 publications

A soft voting ensemble learning approach for credit card fraud detection

A soft voting ensemble learning approach for credit card fraud detection

An Optimized Deep Learning Approach for Detecting Fraudulent Transactions

Research on Supply Chain Financial Risk Prevention Based on Machine Learning

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