This study introduces machine learning (ML)
and deep learning (DL) models for predicting self-employment
default rates using credit information. Most of preceding
studies regarding corporate credit risk often focus on
bankruptcy prediction models which involve and target listed
companies, where they utilize financial information as main
variables and also use macro-economic information as
auxiliary variables. However, bankruptcy prediction models
are difficult to apply to cases where financial information is
insufficient, such as small-and-medium enterprise (SME) and
self-employment businesses. In addition, there hardly exist
studies on the prediction of corporate default rates by industry
and also very limited. We hereby used micro-level variables
that were processed by analysis of credit information such as
loans and overdue history of individual businesses in Korean
manufacturing sector during April 2014 through June 2019,
together with typical macro-economic ones, such that we reach
to achieve performance enhancement in predicting default
rates. We then evaluated the effect which the algorithms such
as Ridge, Random Forest (RF), and Deep Neural Network
(DNN) make on the performance of the proposed model, i.e.
default-rates prediction model for self-employment. In this
study, the DNN model is implemented for two purposes, where
it is a submodel for the selection of credit information variables,
and it also works for cascading to the final model that predicts
default rates by receiving the selected input variables. Each
consists of 2 and 3 hidden layers, respectively, and each layer
again consists of 5 nodes. The activation function, solver, and
learning rate were determined through hyper-parameter
tuning. As a result, when the credit information variable was
used together with the macro-economic variable, the prediction
performance was increased by 3.48% points (R2=0.981),
compared to the Ridge model using only macro-economic
variables, and the DNN performance of the final model was
increased by 4.74% points (R2=0.993).