The Factors Influencing the Adoption of Machine Learning for Regulation by Central Banks in SADC

Aug 4, 2026

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The study investigates SADC central banks' readiness to adopt machine learning technologies using raw data collected through an online survey. The raw data is transformed into modellable data via principal component analysis and estimated through a logistic regression model design. Reliability and validity tests verify that construct measures are consistent and represent intended concepts. Correlation analysis evaluates model hypotheses, while multiple and stepwise regression provide additional model testing. Results indicate that IT infrastructure is critical for operationalizing ML capabilities, with top management playing a key but not exclusive role.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages88

Reading Time3 h 42 min

AccessFree to download

Chapter 1. Introduction

Background of the Study

Problem Statement

Aim and Objectives of the Study

Justification of the Study

Delimitations of the Study

Definition of Terms

Assumptions

Chapter Outline

Introduction

Literature Review

Research Methodology

Presentation of Results

Interpretation of Results

Conclusion and Recommendations

Chapter 2. Literature Review

Introduction

Conceptual Understanding of the Key Terms

History of ML

Applications of ML in Central Banks

Information Technology Adoption

Technology-Organisation-Environment (TOE) Framework

Theoretical Framework

Research Empirical Methodology

Conclusion of Literature Review

Chapter 3. Research Methodology

Introduction

The factors influencing the adoption of Machine Learning for regulation by central banks in SADC by Sibusiso Kunene 2506503 (Research Report) [2506503@students.wits.ac.za / 072](2506503@students.wits.ac.za / 072) 089 0628 Supervisor: Dr Jacques Totowa A research report submitted to the Faculty of Commerce, Law and Management, University of the Witwatersrand, in fulfillment of the requirements for the degree of master’s in business administration Johannesburg, 2024 DEDICATION The paper is dedicated to my family wife, son, grandmother and mother, whose unwavering love, support, and encouragement have been my guiding light throughout this journey. Your belief in me has fuelled my perseverance and determination to reach this milestone. I am forever grateful for your endless sacrifices and boundless encouragement. This achievement is as much yours as it is mine. DECLARATION I, Sibusiso Kunene , declare that this research report is my own work apart from as indicated in the references and acknowledgments. This report is submitted as per the requirements in partial fulfillment for the degree of master’s in business administration through the University of the Witwatersrand, Business School Johannesburg. Sibusiso Kunene Centurion Signed at 29 February On the Day of 2024 ABSTRACT The study investigates SADC central banks' readiness to adopt machine learning technologies with raw data collected through an online survey. Subsequently, the raw data was transformed into modellable data using principal component analysis and further fitted into the proposed logistic regression model design. The data underwent reliability and validity tests, which confirmed that the measurements of the constructs were consistent, reliable, and appropriately represented the intended constructions. Correlation analysis was employed to examine the hypotheses of the model, and multiple and stepwise regression were performed as additional tests of the model. The results show that IT infrastructure is instrumental in enabling SADC central banks to implement machine learning capabilities. Top management is crucial for implementing ML, but adequate IT infrastructure is also essential. The regulatory environment and IT infrastructure indirectly influence SADC central banks'readiness to adopt ML capabilities, despite top management's direct impact. The derivable policy implication from these results is that working groups among the sampled SADC central banks need to be formed to address the noted shortcomings within IT infrastructure and regulatory-related aspects of this adoption holistically. KEYWORDS Machine Learning , TOE Framework, Central banks , Artificial Intelligence. TABLE OF CONTENTS DEDICATION 2 DECLARATION 3 ABSTRACT 4 KEYWORDS 5 LIST OF ACRONYMS..................................................................... 9 LIST OF FIGURES.........................................................................10 LIST OF TABLES 11 CHAPTER 1. INTRODUCTION ....................................................12 1.1 BACKGROUND OF THE STUDY .................................................................... 12 1.2 PROBLEM STATEMENT .............................................................................. 13 1.3 AIM AND OBJECTIVES OF THE STUDY .............................................. 15 1.4 JUSTIFICATION OF THE STUDY ......................................................... 15 1.5 DELIMITATIONS OF THE STUDY .................................................................. 17 1.6 DEFINITION OF TERMS ............................................................................... 17 1.7 ASSUMPTIONS.......................................................................................... 18 1.8 CHAPTER OUTLINE ................................................................................... 18 1.8.1 INTRODUCTION ..............................................................

FAQs

How is machine learning readiness in SADC central banks assessed in the study?

Readiness is measured using survey data, transformed with principal component analysis, and analyzed using a logistic regression model.

Which factors most strongly enable machine learning adoption for regulation?

The findings show IT infrastructure is instrumental, while top management is crucial for implementation, alongside regulatory environment effects.

What policy implication does the study derive from the results?

Form cross-bank working groups to address shortcomings in IT infrastructure and regulatory-related aspects holistically.

Aug 4, 2026

Upload by Miles