[HTML][HTML] A comparative analysis of K-nearest neighbor, genetic, support vector machine, decision tree, and long short term memory algorithms in machine learning

M Bansal, A Goyal, A Choudhary - Decision Analytics Journal, 2022 - Elsevier
Abstract Machine learning (ML) is a new-age thriving technology, which facilitates
computers to read and interpret from the previously present data automatically. It makes use …

[HTML][HTML] Credit card fraud detection in the era of disruptive technologies: A systematic review

A Cherif, A Badhib, H Ammar, S Alshehri… - Journal of King Saud …, 2023 - Elsevier
Credit card fraud is becoming a serious and growing problem as a result of the emergence
of innovative technologies and communication methods, such as contactless payment. In …

Prediction and assessment of credit risk using an adaptive Binarized spiking marine predators' neural network in financial sector

V Amarnadh, NR Moparthi - Multimedia Tools and Applications, 2024 - Springer
The rapid advancement of technologies has pushed for additional enhancements to banking
and other credit platforms. While assisting small and medium sized business in lowering …

Comprehensive review of different artificial intelligence-based methods for credit risk assessment in data science

V Amarnadh, NR Moparthi - Intelligent Decision …, 2023 - journals.sagepub.com
Credit risk is the critical problem faced by banking and financial sectors when the borrower
fails to complete their commitments to pay back. The factors that could increase credit risk …

A deep learning ensemble with data resampling for credit card fraud detection

ID Mienye, Y Sun - Ieee Access, 2023 - ieeexplore.ieee.org
Credit cards play an essential role in today's digital economy, and their usage has recently
grown tremendously, accompanied by a corresponding increase in credit card fraud …

[HTML][HTML] Recursive feature elimination with cross-validation with decision tree: Feature selection method for machine learning-based intrusion detection systems

M Awad, S Fraihat - Journal of Sensor and Actuator Networks, 2023 - mdpi.com
The frequency of cyber-attacks on the Internet of Things (IoT) networks has significantly
increased in recent years. Anomaly-based network intrusion detection systems (NIDSs) offer …

Review of machine learning approach on credit card fraud detection

R Bin Sulaiman, V Schetinin, P Sant - Human-Centric Intelligent Systems, 2022 - Springer
Massive usage of credit cards has caused an escalation of fraud. Usage of credit cards has
resulted in the growth of online business advancement and ease of the e-payment system …

Fraud detection in banking data by machine learning techniques

SK Hashemi, SL Mirtaheri, S Greco - Ieee Access, 2022 - ieeexplore.ieee.org
As technology advanced and e-commerce services expanded, credit cards became one of
the most popular payment methods, resulting in an increase in the volume of banking …

Unbalanced credit card fraud detection data: A machine learning-oriented comparative study of balancing techniques

P Gupta, A Varshney, MR Khan, R Ahmed… - Procedia Computer …, 2023 - Elsevier
The number of individuals who use credit cards has increased dramatically in recent
decades, as has the volume of credit card fraud transactions. Consequently, banks and …

Class balancing framework for credit card fraud detection based on clustering and similarity-based selection (SBS)

H Ahmad, B Kasasbeh, B Aldabaybah… - International Journal of …, 2023 - Springer
Credit card fraud is a growing problem nowadays and it has escalated during COVID-19 due
to the authorities in many countries requiring people to use cashless transactions. Every …