Call for Paper - Upcoming Issues

Enhancing Security in Mobile Wallet Payments: Machine Learning-Based Fraud Detection Across Prominent Wallet Platforms

International Journal of Electronics and Communication Engineering
© 2024 by SSRG - IJECE Journal
Volume 11 Issue 3
Year of Publication : 2024
Authors : Abdullahi Ahmed Abdirahman, Abdirahman Osman Hashi, Ubaid Mohamed Dahir, Mohamed Abdirahman Elmi, Octavio Ernest Romo Rodriguez
10.14445/23488549/IJECE-V11I3P110
pdf
How to Cite?

Abdullahi Ahmed Abdirahman, Abdirahman Osman Hashi, Ubaid Mohamed Dahir, Mohamed Abdirahman Elmi, Octavio Ernest Romo Rodriguez, "Enhancing Security in Mobile Wallet Payments: Machine Learning-Based Fraud Detection Across Prominent Wallet Platforms," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 3, pp. 96-105, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I3P110

Abstract:

This paper presents a novel approach to enhancing the security of financial transactions within mobile wallet applications through the implementation of machine learning-based fraud detection models. This study implements four machine learning models: Random Forest, Logistic Regression, Support Vector Machine and Artificial Neural Networks (ANN), and it evaluates the effectiveness of these models in detecting fraudulent activities within four prominent mobile wallet platforms: EVC Plus, Premier Wallet, Dahabshil Wallet, and IBS Wallet. The evaluation encompasses a comprehensive analysis of model performance metrics, including accuracy, precision, and recall, to assess the efficacy of fraud detection across different wallet ecosystems. The results demonstrated that the ANN-based model exhibits promising accuracy and effectiveness in identifying fraudulent transactions by achieving an accuracy of 91.39%, thereby providing users with enhanced security and confidence in their digital financial transactions. By integrating these fraud detection capabilities into mobile wallet applications, users can proactively mitigate fraud risks and safeguard their financial assets, fostering trust and reliability in the digital financial ecosystem. This research contributes valuable insights and solutions to the ongoing efforts to combat fraud in mobile wallet payments, paving the way for more secure and resilient financial transactions in the digital era.

Keywords:

Fraud detection, Mobile wallet, Machine Learning, Logistic Regression, Support Vector Machine, Random Forest.

References:

[1] T. Karthikeyan, M. Govindarajan, and V. Vijayakumar, “An Effective Fraud Detection Using Competitive Swarm Optimization Based Deep Neural Network,” Measurement: Sensors, vol. 27, pp. 1-12, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Can Iscan, and Fatma Patlar Akbulut, “Fraud Detection Using Recurrent Neural Networks for Digital Wallet Security,” 2023 8th International Conference on Computer Science and Engineering (UBMK), Burdur, Turkiye, pp. 538-542, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Victor Chang et al., “Digital Payment Fraud Detection Methods in Digital Ages and Industry 4.0,” Computers and Electrical Engineering, vol. 100, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Dennis Ng et al., “Can we Classify Cashless Payment Solution Implementations at the Country Level?,” Electronic Commerce Research and Applications, vol. 46, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Leonid Garin, and Vladimir Gisin, “Machine Learning in Classifying Bitcoin Addresses,” The Journal of Finance and Data Science, vol. 9, pp. 1-10, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Can Iscan et al., “Wallet-Based Transaction Fraud Prevention through LightGBM with the Focus on Minimizing False Alarms,” IEEE Access, vol. 11, pp. 131465-131474, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Imane Sadgali, Nawal Sael, and Fouzia Benabbou, “Fraud Detection in Credit Card Transactions Using Machine Learning Techniques,” 2019 1st International Conference on Smart Systems and Data Science (ICSSD), Rabat, Morocco, pp. 1-4, 2019.
[CrossRef] [Publisher Link]
[8] Vivek Ghai, and Sandeep Singh Kang, “Role of Machine Learning in Credit Card Fraud Detection,” 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India, pp. 939-943, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Junhuan Zhang, Kewei Cai, and Jiaqi Wen, “A Survey of Deep Learning Applications in Cryptocurrency,” Iscience, vol. 27, no. 1, pp. 1- 40, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Bipin Bihari Jayasingh, and G. Bhagya Sri, “Online Transaction Anomaly Detection Model for Credit Card Usage Using Machine Learning Classifiers,” 2023 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, pp. 1-5, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Maram A. Alamri, and Mourad A. Ykhlef, “A Machine Learning-Based Framework for Detecting Credit Card Anomalies and Fraud,” 2023 27th International Conference on Information Technology (IT), Zabljak, Montenegro, pp. 1-7, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Manish Thakral et al., “Rigid Wrap ATM Debit Card Fraud Detection Using Multistage Detection,” 2021 6th International Conference on Signal Processing, Computing and Control (ISPCC), Solan, India, pp. 774-778, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Yu-li Liu, Wenjia Yan, and Bo Hu, “Resistance to Facial Recognition Payment in China: The Influence of Privacy-Related Factors,” Telecommunications Policy, vol. 45, no. 5, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Eray Arda Akartuna, Shane D. Johnson, and Amy Thornton, “Preventing the Money Laundering and Terrorist Financing Risks of Emerging Technologies: An International Policy Delphi Study,” Technological Forecasting and Social Change, vol. 179, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Orăştean Ramona, “Financial Markets-Structural Changes and Recent Developments,” Reference Module in Social Sciences, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Simron Padhi, and Devi Priya Battina, “Automating Root Cause Analysis of Anomalies in Ericsson Wallet Platform Using Machine Learning,” M.Sc. Thesis, Faculty of Computing, Blekinge Institute of Technology, Sweden, pp. 1-75, 2023.
[Google Scholar] [Publisher Link]
[17] Messay Asgedom Gobena, “Money Laundering in Ethiopia: An Analysis of Typologies and Techniques,” Journal of Money Laundering Control, vol. 26, no. 4, pp. 696-708, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Aakriti Singla, and Hitesh Jangir, “A Comparative Approach to Predictive Analytics with Machine Learning for Fraud Detection of Realtime Financial Data,” 2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3), Lakshmangarh, India, pp. 1-4, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Khaled Gubran Al-Hashedi, and Pritheega Magalingam, “Financial Fraud Detection Applying Data Mining Techniques: A Comprehensive Review from 2009 to 2019,” Computer Science Review, vol. 40, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Indrajani, Harjanto Prabowo, and Meyliana, “Learning Fraud Detection from Big Data in Online Banking Transactions: A Systematic Literature Review,” Journal of Telecommunication, Electronic and Computer Engineering (JTEC), vol. 8, no. 3, pp. 127-131, 2016.
[Google Scholar] [Publisher Link]
[21] Jarrod West, and Maumita Bhattacharya, “Intelligent Financial Fraud Detection: A Comprehensive Review,” Computers & Security, vol. 57, pp. 47-66, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Sara Makki et al., “Fraud Analysis Approaches in the Age of Big Data-A Review of State of the Art,” 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS* W), Tucson, AZ, USA, pp. 243-250, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Mario Bojilov, “Methods for Assisting in Detection of Synthetic Identity Fraud in Credit Applications in Financial Institutions,” Thesis, Central Queensland University, 2023.
[Google Scholar] [Publisher Link]
[24] Damion Gordon, The Digital Identity Revolution: Assessing the Opportunities and Challenges for Developing Countries, Rethinking Democracy and Governance, 1st ed., pp. 124-145, 2023.
[Google Scholar] [Publisher Link]
[25] Anna Visvizi, Higinio Mora, and Erick G. Varela-Guzman, “The Case of rWallet: A Blockchain-based Tool to Navigate some Challenges Related to Irregular Migration,” Computers in Human Behavior, vol. 139, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Chimuka Moonde, “Secure Mobile Payment System based on Blockchain Technology for Higher Learning Institutions,” Doctoral Dissertation, The University of Zambia, pp. 1-207, 2023.
[Google Scholar] [Publisher Link]
[27] Qasim Umer et al., “Ensemble Deep Learning Based Prediction of Fraudulent Cryptocurrency Transactions,” IEEE Access, vol. 11, pp. 95213-95224, 2023.
[CrossRef] [Google Scholar] [Publisher Link]