Is a big data that can computationally be analysed to reveal patterns, trends, and associations.
Published in Chapter:
Data Avalanche: Harnessing for Mobile Payment Fraud Detection Using Machine Learning
Emmanuel Awuni Kolog (Business School, University of Ghana, Ghana), Acheampong Owusu (Business School, University of Ghana, Ghana), Samuel Nii Odoi Devine (Presbyterian University College, Ghana), and Edward Entee (Business School, University of Ghana, Ghana)
Copyright: © 2020
|Pages: 22
DOI: 10.4018/978-1-7998-2610-1.ch002
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.