Electronic fraud is a problem that has become a concern for businesses of all sizes. Electronic fraud increases the loss margin as criminals go beyond brick-and-mortar enterprises to target firms with an online presence and e-payment methods. The purpose of this research is to propose a framework which decision-makers can use to anticipate threats, provide preventive measures and calculate percentage gain in income upon the implementation of the preventive measures. This proposed framework will assist in safeguarding businesses and consumers from electronic fraud on selected e-payment channels. Similarly, the framework will help minimize e-payment fraud and increase customer adoption of e-payments. The data used was obtained from the Central Bank of Nigeria. The framework’s output offers decision makers in banks and financial technology firms with historical data on the amount of e-payment fraud on each of the selected channels. The framework also allows decision-makers to forecast cyber fraud on various e-payment channels. Similarly, the framework offers methods for implementing preventive measures as well as a percentage gain in income following execution.
Inhaltsverzeichnis (Table of Contents)
- Chapter 1: Introduction
- 1.1 Background of the Study
- 1.2 Problem Statement
- 1.3 Research Questions
- 1.4 Objectives of the Study
- 1.5 Scope of the Study
- 1.6 Significance of the Study
- 1.7 Limitations of the Study
- 1.8 Definition of Terms
- Chapter 2: Literature Review
- 2.1 Electronic Fraud
- 2.2 E-payment Channels
- 2.3 Predictive Analysis
- 2.4 Logistic Regression
- Chapter 3: Research Methodology
- 3.1 Research Design
- 3.2 Data Collection Method
- 3.3 Data Analysis Method
- Chapter 4: Results and Discussion
- 4.1 Data Analysis
- 4.2 Model Development
- 4.3 Model Evaluation
- 4.4 Discussion of Findings
- Chapter 5: Conclusion and Recommendations
- 5.1 Conclusion
- 5.2 Recommendations
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This research aims to develop a framework for anticipating threats, implementing preventive measures, and calculating potential income gains from fraud prevention in the context of e-payment channels in Nigeria. The study utilizes logistic regression as a decision-making tool to detect fraud at the point of sale in e-commerce platforms.
- Electronic fraud and its impact on businesses
- E-payment channels and the prevalence of fraud
- Predictive analysis techniques for fraud detection
- The application of logistic regression for decision-making
- Framework development and evaluation for fraud prevention
Zusammenfassung der Kapitel (Chapter Summaries)
- Chapter 1: Introduction: This chapter introduces the research problem, highlighting the increasing prevalence of electronic fraud and its impact on e-commerce platforms in Nigeria. It outlines the research questions, objectives, scope, and significance of the study.
- Chapter 2: Literature Review: This chapter provides a comprehensive overview of electronic fraud, e-payment channels, predictive analysis techniques, and the use of logistic regression for fraud detection. It reviews existing literature and research related to the topic.
- Chapter 3: Research Methodology: This chapter details the research design, data collection methods, and data analysis techniques employed in the study. It explains the process of acquiring and analyzing data from the Central Bank of Nigeria.
- Chapter 4: Results and Discussion: This chapter presents the findings of the research, including the development and evaluation of the proposed framework for fraud detection and prevention. It discusses the results of data analysis, model development, and model evaluation.
Schlüsselwörter (Keywords)
The key focus areas of this research include decision-making, electronic fraud, e-payment, e-commerce, fraud detection, logistic regression, and predictive analysis. The study explores the application of these concepts to mitigate electronic fraud in the context of small and medium scale enterprises (SMEs) in Nigeria.
- Citation du texte
- Olubunmi Alabi (Auteur), 2023, Predictive Analysis in Economic Intelligence. Case Studies of SMEs in Nigeria, Munich, GRIN Verlag, https://www.grin.com/document/1382733
