Improving PD models predictivity and reactivity with transactional info and ML

26 February 2021 - 26 February 2021
Improving PD models predictivity and reactivity with transactional info and ML
26 February 2021
Prometeia AI 4 Risk Expert Circle

 PD models suffer from a relatively low reactivity in detecting sudden structural breaks in the economic cycle, as traditional data sources do not represent an ideal solution to capture the clients’ creditworthiness evolution in a timely manner. In light of the unprecedented crisis generated by the Covid-19 pandemic, it is required to evolve the modeling framework in order to be able to cope with such a gap. In this 60-minute webinar, Prometeia proposes a solution - already being implemented in one of the major European FIs - that represents a powerful tool to improve both predictive capacity and reactivity, leveraging transactional information and Machine Learning techniques, both in case of structural breaks as well as normal circumstances. In the following fireside chat, eminent speakers from the global risk management community will discuss the hottest topics related to the adoption of these methodologies within the banking processes. Q&A will conclude the session.

11.30 - Use case presentation by Prometeia and Unicredit
Emanuele Giovannini, Head of Credit Rating Modeling, Unicredit Italy
Giangiacomo Sanna, Senior Specialist, Prometeia
11.50 - Fireside chat with:
Chiara Capelli, Head of Credit Risk Modeling, Unicredit Group
Sid Dash, Research Director, Chartis Research
Dmitri Kraynov, Head of Risk Modeling, Sberbank
Marco Stella, Partner, Prometeia
12.20 - Q&A session
If you can't make it live, register anyway: you will be mailed the video-recording shortly after the session.