Incorporating Professional Judgement into Credit Scoring Models

Student: Machigin Vasiliy

Supervisor: Victor A Lapshin

Faculty: International College of Economics and Finance

Educational Programme: Financial Economics (Master)

Year of Graduation: 2020

In this paper, we investigate the potential of professional judgment to be incorporated into the credit scoring models. Using the sample of 41 US public companies in Oil & Gas industry and finely tuned machine learning models, we show that judgment adds meaningfully to the predictive ability of the scoring models that assign corporate credit ratings. Additionally, we compare the performance of the traditional method – the logistic regression – to that of machine learning models and find that machine learning models show better results, as measured by ROC-AUC-score.

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