Anticipating the downgrade and, eventually, the default of a client is key to controlling a bank normal cost of risk. In addition to the traditional risk framework, some banks currently use a prediction model based on machine learning to address this challenge.
This presentation aims to give an overview of the current state-of-art of the early warning framework.
- The key element is the Data
- Few challenges to overcome
- Model is good and by nature imperfect
- Best practices to implement an early warning framework