Secure Internet Financial Transactions - A Framework Integrating Multi-Factor Authentication and Machine Learning

Aug 2, 2026

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Securing online financial transactions is a critical challenge as financial services continue to digitize daily payment flows. This study proposes a two-layer framework combining multi-factor authentication with machine learning to reduce exposure to cybercriminal fraud. The authentication layer uses multiple factors to verify users, while the ML layer activates upon detection of suspicious activity and applies facial recognition for additional protection. Four supervised classifiers are evaluated, achieving accuracies of 97.938%, 97.881%, 96.717%, and 92.354% and supporting usability and efficacy goals.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Introduction

Authentication and Multi-Factor Authentication (MFA)

Machine Learning for Fraud Detection

Proposed Secure Framework

Multi-Layer Security Design

Facial Recognition Triggered by Suspicion

Experimental Setup and Classifiers

Logistic Regression

Decision Trees

Random Forest

Naive Bayes

Article Secure Internet Financial Transactions: A Framework Integrating Multi-Factor Authentication and Machine Learning AlsharifHasan Mohamad Aburbeian 1, * and Manuel Fern¡ndez-Veiga 2 1 Department of Natural, Engineering, and Technology Sciences, Arab American University, Ramallah P600, Palestine 2 AtlanTTic Research Center, Universidade de Vigo, 36310 Vigo, Spain; mveiga@det.uvigo.es * Correspondence: [a.aburbeian@student.aaup.edu](a.aburbeian@student.aaup.edu) Abstract: Securing online 􀀂nancial transactions has become a critical concern in an era where 􀀂nancial services are becoming more and more digital. The transition to digital platforms for conducting daily transactions exposed customers to possible risks from cybercriminals. This study proposed a framework that combines multi-factor authentication and machine learning to increase the safety of online 􀀂nancial transactions. Our methodology is based on using two layers of security. The 􀀂rst layer incorporates two factors to authenticate users. The second layer utilizes a machine learning component, which is triggered when the system detects a potential fraud. This machine learning layer employs facial recognition as a decisive authentication factor for further protection. To build the machine learning model, four supervised classi􀀂ers were tested: logistic regression, decision trees, random forest, and naive Bayes. The results showed that the accuracy of each classi􀀂er was 97.938%, 97.881%, 96.717%, and 92.354%, respectively. This study's superiority is due to its methodology, which integrates machine learning as an embedded layer in a multi-factor authentication framework to address usability, ef􀀂cacy, and the dynamic nature of various e-commerce platform features. With the evolving 􀀂nancial landscape, a continuous exploration of authentication factors and datasets to enhance and adapt security measures will be considered in future work. Citation: Aburbeian, A.M.; Fernández-Veiga, M. Secure Internet Financial Transactions: A Framework Integrating Multi-Factor Authentication and Machine Learning. AI 2024, 5, 177􀂖194 . [https://doi.org/10.3390/ai5010010](https://doi.org/10.3390/ai5010010) Academic Editor: Ke-Lin Du Received: 8 December 2023 Revised: 5 January 2024 Accepted: 7 January 2024 Published: 10 January 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . Keywords: multi-factor authentication; fraud detection; machine learning; face recognition; user-friendly system 1. Introduction FinTech is described as a new 􀀂nancial development that enhances and automates 􀀂nancial services [1] . Mobile wallets, online banking, and payment gateways that offer quick and easy services are examples of 􀀂nancial technologies [2] . The increasing use of such technologies has led to a rise in fraudulent transactions, which makes securing these transactions an issue [3] . Authentication is a procedure in which a user submits some form of credentials to prove identity [4] . The authentication techniques can be one of three categories: something you know (password), something you have (tokens, cards), and something you are (biometrics) [5] . A password has been widely used as a single-factor authentication technique to secure communication between two entities [6] . Although it is a straightforward and easy-to-implement mechanism, it is not suf􀀂cient because of its high ability to be revealed [7] . Sharing the password immediately compromises the account. Furthermore, unauthorized access can be gained using a rainbow table [8], a dictionary attack [9], or social engineering approaches [10] . Following the demonstration that authentication with one f

FAQs

What framework does the study propose for securing online financial transactions?

It proposes a two-layer framework that combines multi-factor authentication with a machine learning component for fraud detection and response.

How does the machine learning layer improve transaction security?

It activates when the system detects potential fraud and uses facial recognition as an additional decisive authentication factor.

Which supervised classifiers are tested, and what accuracy ranges are reported?

The study evaluates logistic regression, decision trees, random forest, and naive Bayes, reporting accuracies of 97.938%, 97.881%, 96.717%, and 92.354% respectively.

Aug 2, 2026

Upload by Maeve