MASTER Assignment
Applications of Early Warning Systems for Customer Segmentation of Wholesale Baking Clients
Type : Master M-BIT
Period: May, 2023 - Oct, 2023
Student : Amato, A. (Alessandra, Student M-BIT)
Date Final project: October 17, 2023
Supervisors:
Abstract:
Wholesale Banking (WB) is evolving rapidly, and financial institutions are increasingly relying on Early Warning Systems (EWS) to detect financial distress proactively. For instance, ING has developed the Advanced Risk Integrated Application (ARIA) as its own EWS tool, primarily designed to detect ongoing negative changes. Nevertheless, ING aims at expanding ARIA's capabilities and introducing the identification of potential up-selling opportunities within the artifact. To achieve this, this study investigates on the implementation of a novel Customer Segmentation (CS) model, integrating early warning triggers, using two unsupervised methods, namley K-Means and DBSCAN. The interpretation of the clusters generated is performed through a number of analyses that explore the different segments from multiple aspects, such as the tightness and separation of the subgroups formed, the clusters densities, the descriptive statistics of the customers’ distribution within each segment and risk-reward analyses. In conclusion, the research contributes to obtaining a deeper understanding of the financial health of banks' WB clients, enabling them to deliver more targeted services based on each segment emerging needs.