Probability of Default Modelling for Corporate Loans in Russia Based on External Data

Student: Khalilbekov Shuhratjon

Supervisor: Victor A Lapshin

Faculty: International College of Economics and Finance

Educational Programme: Financial Economics (Master)

Year of Graduation: 2021

Corporate loans default prediction is an essential part of credit risk management and recent crises highlighted the necessity of appropriate methodology for estimation of such risk. This research focuses on applied modelling of probability of default and provides evidence on possibility to create risk assessment tool based on external data which meets performance standards both in industry and literature. Results suggestthatdevelopedmodelshowspromisingresultscomparedtoindustrystate-of- the-art models and recent researches. In addition, more complex models LightGBM & Random Forest performed better than classic Logistic Regression - in case of absence of interpretability requirements complex model is more appealing to use.

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