Towards Intelligent Risk-based Customer Segmentation in Banking

• URL: http://arxiv.org/abs/2009.13929v1
• Date: Tue, 29 Sep 2020 11:22:04 GMT
• Title: Towards Intelligent Risk-based Customer Segmentation in Banking
• Authors: Shahabodin Khadivi Zand
• Abstract summary: We present an intelligent data-driven pipeline composed of a set of processing elements to move customers' data from one system to another. 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. Our proposed method is able to achieve accuracy of 91% compared to classical approaches in terms of detecting, identifying and classifying transaction to the right classification.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 paper, 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 91% compared to classical approaches in terms of detecting, identifying and classifying transaction to the right classification.

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