In recent
years, the expansion of Fintech has speeded the development of the online
peer-to-peer lending market, offering a huge opportunity for investment by
directly connecting borrowers to lenders, without traditional financial
intermediaries. This innovative approach is though accompanied by increasing
default risk since the information asymmetry tends to rise with online
businesses. This paper aimed to predict the probability of default of the
borrower, using data from the LendingClub, the leading American online
peer-to-peer lending platform. For this purpose, three machine learning methods
were employed: logistic regression, random forest and neural network. Prior to
the scoring models building, the LendingClub model was assessed, using the
grades attributed to the borrowers in the dataset. The results indicated that
the LendingClub model showed low performance with an AUC of 0.67, whereas the
logistic regression (0.9), the random forest (0.9) and the neural network
(0.93) displayed better predictive power. It stands out that the neural network
classifier outperformed the other models with the highest AUC. No difference
was noted in their respective accuracy value which was 0.9. Besides, in order
to enhance their investment decision, investors might take into consideration
the relationship between some variables and the likelihood of default. For
instance, the higher the loan amounts, the higher the likelihood of default.
The higher the debt to income, the higher the likelihood of default. While the
higher the annual income, the lower the probability of default. The probability
of default has a tendency to decline as the number of total open accounts
rises.
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