Development of Proposals for the Modernization of the Scoring Process in the Bank XXX

Student: Sergachev Dmitrii

Supervisor: Nina Korovkina

Educational Programme: Business Informatics (Bachelor)

Final Grade: 8

Year of Graduation: 2018

The purpose of this work is to increase the efficiency of the banking business in terms of forecasting the default of customers through the study of machine learning algorithms. The correct definition of clients and the probabilities of their defaults are key in consumer and other types of lending in the banking industry, since correct definition of the client allows you to issue the most profitable loans. The study included the most famous machine learning algorithms, which return the probability - Gradient Boosting, Random Forest, KNeighbors, Logistic Regression. The criteria for estimating the obtained models will be the Gini coefficient, the stability of the time scale and the convergence of the forecast values with the actual ones.

Full text (added May 16, 2018)

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