Author: Law, Kin Sang
Title: Knowledge driven decision analytics for corporate client relationship management
Advisors: Chung, Fu-lai Korris (COMP)
Degree: Eng.D.
Year: 2020
Subject: Banks and banking -- Computer network resources
Customer relations -- Management -- Data processing
Bank management
Hong Kong Polytechnic University -- Dissertations
Department: Faculty of Engineering
Department of Computing
Pages: xi, 116 pages : color illustrations
Language: English
Abstract: Although the corporate relationship manager seems to be the key enabler in commercial banking, the personal relationship sales model is not a sustainable model for the paradigm shift in digital financial markets. An efficient and effective decision-making process is one of the critical success factors of a bank. In this research, we propose an AI-driven customer relationship management (CRM) model, which adopts the latest technologies of artificial intelligence and machine learning to support the overall business decision-making by capturing and analyzing unstructured data from both internal data sources and the public domain. The proposed AI-driven CRM model represents a transformational change in integrating all business processes of the whole client journey from client onboarding to client exit. It also acts as a think tank to support the overall corporate client relationship management framework, facilitating the bank to implement knowledge driven decision analytics that articulates the bank's strategy effectively, embedding client knowledge in end­to-end processes, maximizing the technological advantages from artificial intelligence and machine learning, and being user-friendly so that the bank can have a consistent and sustainable competitive advantage. However, most of the corporate client documents processed in commercial banks are not well-structured and the traditional analysis approach does not consider the document structure, which carries rich semantic information. In order to benefit from the business knowledge embedded in the banking documents, we propose a flexible document structure-based text representation approach in predictive analytics of unstructured data to improve the performance in the corresponding document classification task. The proposed approach significantly outperforms the traditional whole document approach which does not take into considerations of the document structure. To further enhance the performance of document classification, different kinds of auxiliary information, including company website and industry reports, are captured, processed and incorporated. A mild increase in classification performance is achieved by incorporating industry information in the classification task. Other than classification, the document structure-based text representation approach is also applied in sentiment analysis and clustering to generate more insights for business planning and decisions. Furthermore, different use cases adopting the proposed methods and models are discussed, demonstrating the ways of applying predictive analytics to improve the corporate client relationship management process, including information retrieval of commercial lending documents to support industry-level sales planning, client relationship strategy formulation to improve customer engagement, clients' portfolio monitoring through business alert triggers to facilitate proper risk management, and document clustering to identify new business opportunities. With the proposed AI-driven CRM framework, knowledge can be effectively and efficiently used for business decisions and planning so as to improve the competitive advantage and substantiality of banks.
Rights: All rights reserved
Access: restricted access

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