  Rapid development of advanced modelling techniques gives an opportunity to
develop tools that are more and more accurate. However as usually, everything
comes with a price and in this case, the price to pay is to loose
interpretability of a model while gaining on its accuracy and precision. For
managers to control and effectively manage credit risk and for regulators to be
convinced with model quality the price to pay is too high. In this paper, we
show how to take credit scoring analytics in to the next level, namely we
present comparison of various predictive models (logistic regression, logistic
regression with weight of evidence transformations and modern artificial
intelligence algorithms) and show that advanced tree based models give best
results in prediction of client default. What is even more important and
valuable we also show how to boost advanced models using techniques which allow
to interpret them and made them more accessible for credit risk practitioners,
resolving the crucial obstacle in widespread deployment of more complex, 'black
box' models like random forests, gradient boosted or extreme gradient boosted
trees. All this will be shown on the large dataset obtained from the Polish
Credit Bureau to which all the banks and most of the lending companies in the
country do report the credit files. In this paper the data from lending
companies were used. The paper then compares state of the art best practices in
credit risk modelling with new advanced modern statistical tools boosted by the
latest developments in the field of interpretability and explainability of
artificial intelligence algorithms. We believe that this is a valuable
contribution when it comes to presentation of different modelling tools but
what is even more important it is showing which methods might be used to get
insight and understanding of AI methods in credit risk context.
