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Credit scoring. Assessing credit quality of companies based on publically available data.

Student: Poletaev Vitalii

Supervisor: Alexander Sirotkin

Faculty: St. Petersburg School of Physics, Mathematics, and Computer Science

Educational Programme: Big Data Analysis for Business, Economy, and Society (Master)

Final Grade: 10

Year of Graduation: 2019

Machine learning and advanced analytics keep becoming popular and is implemented at many industries nowadays. It is worth noting that at some business fields specialists take responsibility for the results of the models they use. In this sense, they are more demanding toward interpretability of the model as well as for its quality. That is why, in credit scoring field people still use old approaches that were created decades ago. In this work, we have built two models: logit and random forest. Random Forest, as expected, showed us the better quality in terms of predictability. However, interpretation advantage of the logit was not so high, because we used contemporary approaches for explaining complex models. As a result, we got better model with nearly the same level of interpretability – that is the main value. Results are expected to be useful for corporate banking, insurance, and also for companies that are interested in assessing quality of its contractors.

Full text (added May 30, 2019)

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