How can artificial intelligence help customer intelligence for credit portfolio management? A systematic literature review
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-11-01
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| Series: | International Journal of Information Management Data Insights |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667096824000235 |
| _version_ | 1797205986661892096 |
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| author | Alessandra Amato Joerg R. Osterrieder Marcos R. Machado |
| author_facet | Alessandra Amato Joerg R. Osterrieder Marcos R. Machado |
| author_sort | Alessandra Amato |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T08:59:50Z |
| format | Article |
| id | doaj.art-9582189e808b461c94f179c44faaec57 |
| institution | Directory Open Access Journal |
| issn | 2667-0968 |
| language | English |
| last_indexed | 2024-04-24T08:59:50Z |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Information Management Data Insights |
| spelling | doaj.art-9582189e808b461c94f179c44faaec572024-04-16T04:09:54ZengElsevierInternational Journal of Information Management Data Insights2667-09682024-11-0142100234How can artificial intelligence help customer intelligence for credit portfolio management? A systematic literature reviewAlessandra Amato0Joerg R. Osterrieder1Marcos R. Machado2University of Twente, Faculty of Electrical Engineering, Mathematics, and Computer Science, AE Enschede, 7500, NetherlandsUniversity of Twente, Faculty of Behavioural, Management and Social Sciences, Department of High-Tech Business and Entrepreneurship, AE Enschede, 7500, Netherlands; Bern Business School, Institute of Finance and Applied Data Science, Bern, 3005, SwitzerlandUniversity of Twente, Faculty of Behavioural, Management and Social Sciences, Department of High-Tech Business and Entrepreneurship, AE Enschede, 7500, Netherlands; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2667096824000235Early warning systemsCustomer segmentationLending settingsUnsupervised learningSystematic literature review |
| spellingShingle | Alessandra Amato Joerg R. Osterrieder Marcos R. Machado How can artificial intelligence help customer intelligence for credit portfolio management? A systematic literature review International Journal of Information Management Data Insights Early warning systems Customer segmentation Lending settings Unsupervised learning Systematic literature review |
| title | How can artificial intelligence help customer intelligence for credit portfolio management? A systematic literature review |
| title_full | How can artificial intelligence help customer intelligence for credit portfolio management? A systematic literature review |
| title_fullStr | How can artificial intelligence help customer intelligence for credit portfolio management? A systematic literature review |
| title_full_unstemmed | How can artificial intelligence help customer intelligence for credit portfolio management? A systematic literature review |
| title_short | How can artificial intelligence help customer intelligence for credit portfolio management? A systematic literature review |
| title_sort | how can artificial intelligence help customer intelligence for credit portfolio management a systematic literature review |
| topic | Early warning systems Customer segmentation Lending settings Unsupervised learning Systematic literature review |
| url | http://www.sciencedirect.com/science/article/pii/S2667096824000235 |
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