Amato, Alessandra; Osterrieder, Jörg Robert; Machado, Marcos (2024). How can artificial intelligence help customer intelligence for credit portfolio management? A systematic literature review International Journal of Information Management Data Insights, 4(2), pp. 1-15. Elsevier 10.1016/j.jjimei.2024.100234
|
Text
1-s2.0-S2667096824000235-main.pdf - Published Version Available under License Creative Commons: Attribution (CC-BY). Download (1MB) | Preview |
In this era of Big Data and the advancement of sophisticated analytical techniques, the financial industry has the capacity to implement innovative technologies within their systems to derive crucial insights about their clientele and vigilantly monitor their activities. This landscape has seen the emergence and rise of two significant applications, namely, customer segmentation systems and early warning systems. Therefore, this study presents a systematic literature review on the automation of customer segmentation and early warning techniques with a focus on managing credit portfolio entities. The research delves into a multitude of scholarly articles from three distinct perspectives: charting the dominant trends within the literature, unpacking the overarching themes, and critically examining the integration of early warning signals within customer clustering applications. Furthermore, the review reveals a noticeable dearth of studies probing the synergistic application of these two systems. Despite their independent effectiveness in risk management and targeted marketing strategies respectively, an integrated approach holds potential for bolstering financial stability and tailoring customer service. Thus, this review stands as a significant academic contribution, advocating an integrated application of these systems within the financial industry. The findings provide a novel foundation for future research and practical applications, potentially redefining strategies within the financial sector.
Item Type: |
Journal Article (Review Article) |
|---|---|
Division/Institute: |
Business School > Institute for Applied Data Science & Finance Business School > Institute for Applied Data Science & Finance > Finance, Accounting and Tax Business School > Institute for Applied Data Science & Finance > Applied Data Science Business School |
Name: |
Amato, Alessandra; Osterrieder, Jörg Robert  0000-0003-0189-8636 and Machado, Marcos 0000-0003-1056-2368 |
Subjects: |
H Social Sciences > HG Finance |
ISSN: |
2667-0968 |
Publisher: |
Elsevier |
Funders: |
Organisations 0 not found.; Organisations 0 not found.; [7] Swiss National Science Foundation ; Organisations 101119635 not found.; Organisations 0 not found. |
Language: |
English |
Submitter: |
Yiting Liu |
Date Deposited: |
06 Aug 2024 14:50 |
Last Modified: |
06 Aug 2024 14:50 |
Publisher DOI: |
10.1016/j.jjimei.2024.100234 |
Related URLs: |
|
Uncontrolled Keywords: |
Early warning systems Customer segmentation Lending settings Unsupervised learning Systematic literature review |
ARBOR DOI: |
10.24451/arbor.22017 |
URI: |
https://arbor.bfh.ch/id/eprint/22017 |
Actions (login required)
 |
View Item |