Data Avalanche

Author:

Kolog Emmanuel Awuni1,Owusu Acheampong1ORCID,Devine Samuel Nii Odoi2,Entee Edward1

Affiliation:

1. Business School, University of Ghana, Ghana

2. Presbyterian University College, Ghana

Abstract

Globalizing businesses from developing countries require a thoughtful strategy and adoption of state-of-the-art technologies to meet up with the rapidly changing society. Mobile money payment service is a growing service that provides opportunities for both the formal and informal sectors in Ghana. Despite its importance, fraudsters have capitalized on the vulnerabilities of users to defraud them. In this chapter, the authors have reviewed existing data mining techniques for exploring the detection of mobile payment fraud. With this technique, a hybrid-based machine learning framework for mobile money fraud detection is proposed. With the use of the machine learning technique, an avalanche of fraud-related cases is leveraged, as a corpus, for fraud detection. The implementation of the framework hinges on the formulation of policies and regulations that will guide the adoption and enforcement by Telcos and governmental agencies with oversight responsibilities in the telecommunication space. The authors, therefore, envision the implementation of the proposed framework by practitioners.

Publisher

IGI Global

Reference45 articles.

1. Akomea-Frimpong, I., Andoh, C., Akomea-Frimpong, A., & Dwomoh-Okudzeto, Y. (2019). Control of Fraud on Mobile Money Services in Ghana: an exploratory study. Journal of Money Laundering Control, 300-317.

2. Business Applications of Data Mining.;C.Apte;Communications of the ACM,2002

3. Aseidu-Addo, S. (2019, November 11). Cyber fraud: Ghanaians lose $200m in 3 years. Retrieved from https://www.graphic.com.gh/business/business-news/ghana-news-momo-fraud-threatens-emerging-payment-technologies.html

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1. Detecting Mobile Payment Fraud: Leveraging Machine Learning for Rapid Analysis;2023 Tenth International Conference on Social Networks Analysis, Management and Security (SNAMS);2023-11-21

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