Chapter One — 1.1 Background to the Study

The rapid evolution of Explainable AI has transformed the way organizations design, deploy, and manage mobile banking applications. As institutions seek to modernize legacy processes, Explainable AI offers new opportunities to improve service delivery, reduce manual overhead, and respond more effectively to user needs.

Despite this potential, many existing mobile banking applications were not originally designed with explainable AI in mind, resulting in persistent gaps in real-time monitoring capability that limit their overall effectiveness. This study examines how Explainable AI can be applied to help close that gap.

1.2 Statement of the Problem

Current mobile banking applications in many organizations struggle with inadequate real-time monitoring capability, often relying on manual processes or outdated architectures that were not designed for today's operating environment. Without a structured approach to integrating explainable AI, these limitations are likely to persist, exposing organizations to inefficiency, risk, and a poor user experience. This study is motivated by the need to design and evaluate a explainable AI-based approach to addressing this problem.

1.3 Objectives of the Study

1. To design and implement a explainable AI-based approach to improving real-time monitoring capability in mobile banking applications.
2. To evaluate the effectiveness of Explainable AI in enhancing real-time monitoring capability within mobile banking applications.
3. To identify the key requirements and constraints relevant to deploying explainable AI in this context.
4. To assess user and stakeholder perception of the resulting system.

1.4 Research Questions

1. How can explainable AI be applied to improve real-time monitoring capability in mobile banking applications?
2. How effective is Explainable AI at enhancing real-time monitoring capability within mobile banking applications?
3. What requirements and constraints are relevant to deploying explainable AI 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 explainable AI 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 explainable AI-based solution for mobile banking applications, focused specifically on real-time monitoring capability; broader deployment considerations fall outside this scope.