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Comparison of Logistic Regression and Classification Trees to Forecast Short Term Defaults on Repeat Consumer Loans
This dissertation highlights the performance comparison between two popular contemporary consumer loan credit scoring techniques, namely logistic regression and classification trees. Literature has shown logistic regression to perform better than classification trees in terms of predictiveness and r...
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| Format: | Thesis |
| Language: | English |
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Department of Finance and Tax
2022
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| _version_ | 1867613686261088256 |
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| access_status_str | Open Access |
| author | Naicker, Keeland |
| author2 | Rajaratnam, Kanshukan |
| author_browse | Naicker, Keeland Rajaratnam, Kanshukan |
| author_facet | Rajaratnam, Kanshukan Naicker, Keeland |
| author_sort | Naicker, Keeland |
| collection | Thesis |
| description | This dissertation highlights the performance comparison between two popular contemporary consumer loan credit scoring techniques, namely logistic regression and classification trees. Literature has shown logistic regression to perform better than classification trees in terms of predictiveness and robustness when forecasting consumer loan default events over standard twelve-month outcome periods. One of the major shortcomings with classification trees is its tendency to overfit data eroding its robustness, making it vulnerable to underlying population characteristic shifts. Classification trees remains a popular technique due to its ease of application (algorithm machine learning basis) and model interpretation. Past research has found classification trees to perform marginally better than logistic regression with respect to predictiveness and robustness when modelling short term consumer credit default outcomes related to previously unseen new customer credit loan applications. This dissertation independently tested this finding on reloan consumer loan data, repeat customers who renewed loan facilities at a significant South African micro lender. This dissertation tests the finding if the classification tree technique would outperform logistic regression when modelling this new type of loan data. Credit scoring models were built and tested for each respective technique across identical data sets with the intent to eliminate bias. Robustness tests were constructed via careful iterative data splits. Performance tests measuring predictiveness and robustness were conducted via the weighted sums of squared error evaluation approach. Results reveal logistic regression to outperform classification trees on predictiveness and robustness across the designed uniform iterative data splits, which suggests that logistic regression remains the superior technique when modelling short term credit default outcomes on reloan consumer loan data. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/36027 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:40:05.556Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Department of Finance and Tax |
| publisherStr | Department of Finance and Tax |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/36027 Comparison of Logistic Regression and Classification Trees to Forecast Short Term Defaults on Repeat Consumer Loans Naicker, Keeland Rajaratnam, Kanshukan Corporate Finance and Valuations This dissertation highlights the performance comparison between two popular contemporary consumer loan credit scoring techniques, namely logistic regression and classification trees. Literature has shown logistic regression to perform better than classification trees in terms of predictiveness and robustness when forecasting consumer loan default events over standard twelve-month outcome periods. One of the major shortcomings with classification trees is its tendency to overfit data eroding its robustness, making it vulnerable to underlying population characteristic shifts. Classification trees remains a popular technique due to its ease of application (algorithm machine learning basis) and model interpretation. Past research has found classification trees to perform marginally better than logistic regression with respect to predictiveness and robustness when modelling short term consumer credit default outcomes related to previously unseen new customer credit loan applications. This dissertation independently tested this finding on reloan consumer loan data, repeat customers who renewed loan facilities at a significant South African micro lender. This dissertation tests the finding if the classification tree technique would outperform logistic regression when modelling this new type of loan data. Credit scoring models were built and tested for each respective technique across identical data sets with the intent to eliminate bias. Robustness tests were constructed via careful iterative data splits. Performance tests measuring predictiveness and robustness were conducted via the weighted sums of squared error evaluation approach. Results reveal logistic regression to outperform classification trees on predictiveness and robustness across the designed uniform iterative data splits, which suggests that logistic regression remains the superior technique when modelling short term credit default outcomes on reloan consumer loan data. 2022-03-10T10:08:54Z 2022-03-10T10:08:54Z 2021 2022-03-08T09:40:26Z Master Thesis Masters MCom http://hdl.handle.net/11427/36027 eng application/pdf Department of Finance and Tax Faculty of Commerce |
| spellingShingle | Corporate Finance and Valuations Naicker, Keeland Comparison of Logistic Regression and Classification Trees to Forecast Short Term Defaults on Repeat Consumer Loans |
| thesis_degree_str | Master's |
| title | Comparison of Logistic Regression and Classification Trees to Forecast Short Term Defaults on Repeat Consumer Loans |
| title_full | Comparison of Logistic Regression and Classification Trees to Forecast Short Term Defaults on Repeat Consumer Loans |
| title_fullStr | Comparison of Logistic Regression and Classification Trees to Forecast Short Term Defaults on Repeat Consumer Loans |
| title_full_unstemmed | Comparison of Logistic Regression and Classification Trees to Forecast Short Term Defaults on Repeat Consumer Loans |
| title_short | Comparison of Logistic Regression and Classification Trees to Forecast Short Term Defaults on Repeat Consumer Loans |
| title_sort | comparison of logistic regression and classification trees to forecast short term defaults on repeat consumer loans |
| topic | Corporate Finance and Valuations |
| url | http://hdl.handle.net/11427/36027 |
| work_keys_str_mv | AT naickerkeeland comparisonoflogisticregressionandclassificationtreestoforecastshorttermdefaultsonrepeatconsumerloans |