Integrating Supervised and Unsupervised Machine Learning Algorithms for Profit-based Credit Scoring

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Mehrabi, Amir | 2021

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 54738 (44)
University: Sharif University of Technology
Department: Management and Economics
Advisor(s): Arian, Hamid Reza; Zamani, Shiva
Abstract:
In this study, we combined supervised and unsupervised machine learning algorithms, included the benefits of true identification of good borrowers and costs of false identification of bad borrowers, and then proposed a model for predicting the default of loan applicants with a profit-based approach. The results show that our proposed model has the best performance in profit measure in comparison with individual supervised models. In fact, we first divided the data into two train sets and one test set. We have constructed our model by training unsupervised models on the first train set and supervised models on the second train set. The results of implementing the model on the Australian and Taiwanese credit card data set indicate the superiority of the proposed model in maximizing profits over the using of each of the supervised methods individually. The proposed model results in the statistical indicators included ACC, MCC, Precision, Recall, Type One Error, and Type Two Error are also in an acceptable situation compared to the individual supervised models
Keywords:
Credit Scoring ; Unsupervised Learning ; Supervised Learning ; Profit-Based Credit Scoring ; Default Prediction

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