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Predicting defaults in the automotive credit Industry : an empircial study using machine learning techniques predicting loan defaults

Bøe, Petter Telle
Master thesis
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URI
https://hdl.handle.net/11250/2686955
Date
2020
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  • Master Thesis [3376]
Abstract
This master thesis explore the potential of Machine Learning techniques in predicting default of

vehicle loan applicants. Usually, banks or other financial institutions utilize the Logistic

Regression algorithm to support their decisions-making process, however more advanced

methods has been proven to advance in classifying default predictions. The data set applied in

this are collected from several institutions, contained contract information, historical credit

information and status, and demografical information of more than 240 000 granted loan

applicants.

The results from four different machine learning techniques; Random Forest, Gradient Boostin

Machines, Support Vector Machines and Neural Networks, were compared to the benchmark

model; Logistic Regression. From the study, the Neural Network were found marginally better

than the Logistic regression. Notably, all models were trained and tested on identical data set,

however separated the fitting, validation and the testing in three data sets with similar features.

However, due to time- and computational constraints, the models was not fully exploited in

terms of tuning the hyperparameters.

The best performing model, Neural Network, achived an AUC of 0.6349, followed closely by the

Logistic Regression with an AUC of 0.6325. Based on the performance and knowledge of the

models, a conclusion that the Logistic Regression is the best, however the Neural Network has

the best potential in towards future research due when data qualty.

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