Chapter One — 1.1 Background to the Study

Organizations that depend on mobile banking applications are under increasing pressure to modernize, and Machine Learning has emerged as one of the more promising avenues for doing so, given its demonstrated impact in related domains.

Despite this potential, many existing mobile banking applications were not originally designed with machine learning in mind, resulting in persistent gaps in data security that limit their overall effectiveness. This study examines how Machine Learning can be applied to help close that gap.

1.2 Statement of the Problem

Existing approaches to data security within mobile banking applications remain largely reactive and fragmented, with little systematic use of machine learning despite its demonstrated value elsewhere. This study addresses the resulting gap by designing and evaluating a solution built specifically around machine learning.

1.3 Objectives of the Study

1. To design and implement a machine learning-based approach to improving data security in mobile banking applications.
2. To evaluate the effectiveness of Machine Learning in enhancing data security within mobile banking applications.
3. To identify the key requirements and constraints relevant to deploying machine learning in this context.
4. To assess user and stakeholder perception of the resulting system.

1.4 Research Questions

1. How can machine learning be applied to improve data security in mobile banking applications?
2. How effective is Machine Learning at enhancing data security within mobile banking applications?
3. What requirements and constraints are relevant to deploying machine learning in this context?
4. How do users and stakeholders perceive the resulting system?

1.5 Significance of the Study

Beyond its immediate technical contribution, this study offers value to organizations evaluating whether to invest in machine learning for their own mobile banking applications, and contributes to the broader literature on applied software technology / IT by documenting a concrete implementation and evaluation case.

1.6 Scope of the Study

As a study of this kind, its scope is confined to designing and evaluating a machine learning-based solution for mobile banking applications, focused specifically on data security; broader deployment considerations fall outside this scope.