Macquarie University
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Towards Explainable Artificial Intelligence in Banking and Financial Services

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posted on 2022-11-04, 04:28 authored by Ambreen Hanif

Artificial intelligence (AI) enables machines to learn from human experience, adjust to new inputs, and perform human-like tasks. AI is progressing rapidly and is transforming the way businesses operate, from process automation to cognitive augmentation of tasks and intelligent process/data analytics. However, the main challenge for human users would be to understand and appropriately trust the result of AI algorithms and methods. In this thesis, to address this challenge, we study and analyze the recent work done in Explainable Artificial Intelligence (XAI) methods and tools. We introduce a novel XAI process, which facilitates producing explainable models while maintaining a high level of learning performance. We present an interactive evidence-based approach to assist human users in comprehending and trusting the results and output created by AI-enabled algorithms. We adopt a typical scenario in the Banking domain for analyzing customer transactions. We develop a digital dashboard to facilitate interacting with the algorithm results and discuss how the proposed XAI method can significantly improve the confidence of data scientists in understanding the result of AI-enabled algorithms.


Table of Contents

1 Introduction -- 2 Background and State-of-the-Art -- 3 Proposed Model -- 4 Experiments and Evaluations -- 5 Conclusion and Future Directions -- A Appendix -- List of Symbols -- References


A thesis submitted to Macquarie University for the degree of Masters of Research

Awarding Institution

Macquarie University

Degree Type

Thesis MRes


Thesis (MRes), Macquarie University, Faculty of Science and Engineering, 2021

Department, Centre or School

Department of Computing

Year of Award


Principal Supervisor

Amin Beheshti

Additional Supervisor 1

Xuyun Zhang

Additional Supervisor 2

Noman Javed


Copyright: The Author Copyright disclaimer:




85 pages