  Logistic Regression and Support Vector Machine algorithms, together with
Linear and Non-Linear Deep Neural Networks, are applied to lending data in
order to replicate lender acceptance of loans and predict the likelihood of
default of issued loans. A two phase model is proposed; the first phase
predicts loan rejection, while the second one predicts default risk for
approved loans. Logistic Regression was found to be the best performer for the
first phase, with test set recall macro score of $77.4 \%$. Deep Neural
Networks were applied to the second phase only, were they achieved best
performance, with validation set recall score of $72 \%$, for defaults. This
shows that AI can improve current credit risk models reducing the default risk
of issued loans by as much as $70 \%$. The models were also applied to loans
taken for small businesses alone. The first phase of the model performs
significantly better when trained on the whole dataset. Instead, the second
phase performs significantly better when trained on the small business subset.
This suggests a potential discrepancy between how these loans are screened and
how they should be analysed in terms of default prediction.
