Predictive Analysis in Economic Intelligence. Case Studies of SMEs in Nigeria

Table of Contents

1.1 INTRODUCTION

1.1.2 E-PAYMENT FRAUD

1.2 STATEMENT OF THE PROBLEM

1.3 SIGNIFICANCE OF STUDY

1.3.1 SIGNIFICANCE OF THE STUDY: MODEL 1

1.3.2 SIGNIFICANCE OF THE STUDY: MODEL 2

1.4 RESEARCH QUESTION AND MOTIVATION

1.5 RESEARCH CONTRIBUTION

1.6 AIM AND OBJECTIVES

1.7 SCOPE OF WORK

2.1.1 THE EVOLUTION OF FRAUD

2.2 OVERVIEW OF E-PAYMENT FRAUD DETECTION AND FORECASTING

2.2.1 TYPES OF E-PAYMENT

2.2.1.1 Credit Card

2.2.1.2 Debit Card

2.2.1.3 Smart Card

2.2.1.4 E-Wallet

2.2.1.5 Online banking

2.2.1.6 Mobile-payment

2.2.1.7 Digital Wallet Payments

2.2.1.8 Direct Debits

2.3 ELECTRONIC FRAUD

2.4.1 True (classic) fraud

2.4.2 Triangulation fraud

2.4.3 Interception fraud:

2.4.4 Card validity testing fraud:

2.4.5 Chargeback fraud:

2.5 DETECTION OF CARD FRAUD

2.5.1 E-PAYMENT FRAUD DETECTION TECHNIQUES

2.5.1.1 RULE-BASED APPROACHES

2.6 REQUIREMENT FOR A FRAMEWORK THAT CAN DETECT AND FORECAST ELECTRONIC FRAUD

2.7 PREDICTIVE ANALYSIS

2.7.1 Benefits of predictive analysis for organizations

2.8 ECONOMIC INTELLIGENCE

2.9 SMALL AND MEDIUM SCALED ENTERPRISE (SME)

2.10 HACKING

2.10.1 Major business hacking types

2.10.1.1 Key logger

2.10.1.2 Denial of Service (DoS\DDoS)

2.10.1.3 Waterhole assaults

2.10.1.4 Fake WAP

2.10.1.5 Eavesdropping (Passive Attacks)

2.10.1.6 Phishing

2.10.1.7 Virus and Trojan

2.10.1.8 Click Jacking Assaults

2.10.1.9 Cookie theft

2.10.1.10 Bait and switch

3.1 INTRODUCTION

3.1.1 HISTORY OF ECONOMIC INTELLIGENCE

3.1.2 DECISION MAKING IN EI

3.1.3 BUILDING A FORECAST MODEL

3.1.4 REGRESSION ANALYSIS

3.2 FRAMEWORK FOR FORECASTING ELECTRONIC FRAUD THREATS

3.3 FRAMEWORK FOR DETECTION OF FRAUD AT POINT OF SALE ON ELECTRONIC COMMERCE SITES

3.3.1 Dataset

3.3.2 Indicators of card validity fraud

RESULT AND DISCUSSION

4.1 INTRODUCTION

4.2 QUESTIONNAIRE RESULTS

4.3 RESULT FOR FRAMEWORK FOR FORECASTING ELECTRONIC FRAUD THREATS

4.4 DISCUSSION

5.1 CONCLUSION

5.2 CONTRIBUTION TO KNOWLEDGE

5.3 RECOMMENDATION FOR FUTURE WORK

Research Objectives and Themes

The primary objective of this research is to develop comprehensive frameworks for businesses, specifically SMEs, to identify, anticipate, and mitigate electronic fraud across various payment channels. By employing predictive analytics and machine learning techniques, the research seeks to provide decision-makers with actionable insights to safeguard their operations, increase customer trust, and ultimately foster sustainable economic growth. The study addresses the challenge of e-payment security, which has become increasingly critical as digital transaction volumes rise globally and locally in Nigeria.

• Investigation and categorization of prevalent e-payment fraud techniques, including phishing, keylogging, and clickjacking.
• Development of a forecasting framework for calculating potential fraud threats and associated revenue impacts.
• Application of logistic regression models to detect fraudulent transactions at digital and physical points of sale.
• Analysis of the economic intelligence process to integrate data-driven decision support into business management.
• Formulation of effective preventive and countermeasures for SMEs to enhance transaction security.

Excerpt from the Book

Summary of Chapters

CHAPTER 1 INTRODUCTION: This chapter provides an overview of the global rise of e-payments and the accompanying security challenges, defining the background of the study and research objectives.

CHAPTER 2 LIERATURE REVIEW: This chapter examines existing scholarly work on e-payment methods, fraud detection mechanisms, and the theoretical underpinnings of economic intelligence.

CHAPTER 3 EVALUATION OF THE ECONOMIC INTELLIGENCE SYSTEM: This chapter details the architectural design and methodological approaches, including regression analysis and forecasting frameworks, used to evaluate the economic intelligence system.

CHAPTER 4 RESULT AND DISCUSSION: This chapter presents the findings from the questionnaire and the developed fraud forecasting and detection frameworks, alongside a discussion on their practical implications.

CHAPTER 5 CONCLUSION AND FUTURE WORK RECOMMENDATIONS: This chapter summarizes the contributions of the research and provides guidance for future studies in related fields.

Keywords

Decision-maker, electronic fraud, e-payment, developing countries, E-commerce, point of sale, decision making, fraud detection, logistic regression, machine learning, economic intelligence, SMEs, cybercrime, data analysis, predictive analytics.

Frequently Asked Questions

What is the primary scope of this dissertation?

This work focuses on the predictive analysis of electronic fraud within the context of small and medium-sized enterprises (SMEs) in Nigeria, proposing frameworks to forecast and detect threats across major e-payment channels.

What are the main thematic areas covered?

The study covers the evolution of various electronic fraud types, such as phishing and keylogging, methods for e-payment fraud detection, the application of predictive analytics, and the role of economic intelligence in business decision-making.

What is the core research question driving this study?

The research explores whether frameworks based on historical data and machine learning can effectively forecast cyber fraud on e-payment channels and assist in identifying fraudulent activity at points of sale to safeguard revenue.

Which scientific methodology does the author employ?

The author uses a hybrid methodology involving structural equation modeling (SEM), linear regression for forecasting fraud threats, and logistic regression for binary classification to detect fraudulent versus legitimate e-commerce transactions.

What topics are specifically addressed in the primary chapters?

The chapters treat the history and theoretical principles of economic intelligence, the technical categorization of e-payment hacking strategies, and the implementation of specific frameworks to improve security for SMEs.

Which keywords define this research?

The work is characterized by terms like electronic fraud, logistic regression, e-payment, economic intelligence, and fraud detection within the framework of developing nations.

How is triangulation fraud specifically analyzed?

The research describes triangulation fraud as a complex, three-party scheme involving a fraudster, an unwitting shopper, and a legitimate merchant, and explores its mitigation through behavioral and network data analysis.

What is the importance of the reported accuracy in this study?

The proposed framework for fraud detection at the point of sale achieved an accuracy of 97.8 percent, providing a reliable tool for decision-makers to rapidly identify and forestall fraudulent attempts.