Chen, D.T. (2023) Development of Financial Distress Prediction Model for the Watchlist Classification of Wholesale Banking Clients at ING.
Abstract
An Early Warning System (EWS) is a tool that enables the monitoring of the credit portfolio to identify clients in financial distress. ARIA is the EWS used by ING to monitor their Wholesale Banking (WB) clients using a variety of early warning triggers based on internal data, news articles, and market data. However, the current triggers are limited in their predictive capabilities as they are backwards-looking and are only derived from a single variable. A new Watchlist (WL) trigger aims to incorporate the information of all the current triggers into a single model that can predict whether a client should be on a watchlist based on their credit risk. The aim of this research focuses on exploring how such a WL trigger by developing a financial distress prediction model using machine learning techniques.
Supervisors
UT Supervisors
- Dr. Marcos Machado
- Dr. Jörg Osterrieder
ING Supervisors
- Rui Santos