Macquarie University
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Towards Intelligent Risk-based Customer Segmentation in Banking

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posted on 2022-09-12, 00:52 authored by Shahabodin Khadivi Zand

Business Processes, i.e., a set of coordinated tasks and activities to achieve a business goal, and their continuous improvements are key to the operation of any organization. In banking, business processes are increasingly dynamic as various technologies have made dynamic processes more prevalent. For example, customer segmentation, i.e., the process of grouping related customers based on common activities and behaviors, could be a data-driven and knowledge-intensive process. In this thesis, we present an intelligent data driven pipeline composed of a set of processing elements to move customers’ data from one system to another, transforming the data into the contextualized data and knowledge along the way. The goal is to present a novel intelligent customer segmentation process which automates the feature engineering, i.e., the process of using (banking) domain knowledge to extract features from raw data via data mining techniques, in the banking domain. We adopt a typical scenario for analyzing customer transaction records, to highlight how the presented approach can significantly improve the quality of risk-based customer segmentation in the absence of feature engineering. As result, our proposed method is able to achieve accuracy of � compared to classical approaches in terms of detecting, identifying and classifying transaction to the right classification.


Table of Contents

1. Introduction -- 2. Background and State-of-the-Art -- 3. Methodology -- 4. Experiment and Evaluation -- 5. Conclusion and Future work -- List of Symbols -- References


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

Awarding Institution

Macquarie University

Degree Type

Thesis MRes


Thesis (MRes), Macquarie University, Faculty of Science and Engineering, Department of Computing, 2020

Department, Centre or School

Department of Computing

Year of Award


Principal Supervisor

Amin Beheshti

Additional Supervisor 1

Michael Sheng

Additional Supervisor 2

Fariborz Sobhanmanesh


Copyright: The Author Copyright disclaimer:




66 pages

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