University of Twente Student Theses
Development of Financial Distress Prediction Model for the Watchlist Classification of Wholesale Banking Clients at ING
Chen, D.T. (2023) Development of Financial Distress Prediction Model for the Watchlist Classification of Wholesale Banking Clients at ING.
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| 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. |
| Item Type: | Essay (Master) |
| Clients: | ING, Amsterdam, Netherlands |
| Faculty: | BMS: Behavioural, Management and Social Sciences |
| Subject: | 50 technical science in general |
| Programme: | Industrial Engineering and Management MSc (60029) |
| Link to this item: | https://purl.utwente.nl/essays/95110 |
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