Work

Credit risk modeling - predicting customer loan defaults with machine learning models

Year: 2021

Type: dissertation

Abstract: The assessment of financial credit risk constitutes an important, yet challenging research topic across multiple disciplines. This paper evaluates the risk of customers not being able to repay their obligation on time by utilizing a variety of both parametric and non-parametric (supervised) machine learning models. These methods include Decision Tree, Random Forest, Ada Boost, XG Boost, and Support Vector Machine. In addition, as a benchmark classifier, the traditional credit-risk method, Logistic Regression, was used to perform a comparison. Random Forest and XG Boost outperformed the other methods constantly, provided that thorough data analysis, pre-processing, and model-training are performed.

Source: Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)

Author: Dornigg, Thomas

Language: English

Topic: Financial Distress and Bankruptcy Prediction

Subfield: Accounting

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: green

Funder: Fundação para a Ciência e a Tecnologia

Awards: G1516346500, G3257768443, G3821169523, Nova School of Business and Economics, Nova School of Business and Economics, G946021960

OpenAlex ID: W7046178390

Cites: 48

Cited by:

Related to: 10