A comparative analysis of machine learning algorithms for predicting probabilities of default
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909659158282240 |
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| author | Cristescu, Adrian Iulian Giordano, Matteo |
| author_facet | Cristescu, Adrian Iulian Giordano, Matteo |
| contents | Predicting the probability of default (PD) of prospective loans is a critical objective for financial institutions. In recent years, machine learning (ML) algorithms have achieved remarkable success across a wide variety of prediction tasks; yet, they remain relatively underutilised in credit risk analysis. This paper highlights the opportunities that ML algorithms offer to this field by comparing the performance of five predictive models-Random Forests, Decision Trees, XGBoost, Gradient Boosting and AdaBoost-to the predominantly used logistic regression, over a benchmark dataset from Scheule et al. (Credit Risk Analytics: The R Companion). Our findings underscore the strengths and weaknesses of each method, providing valuable insights into the most effective ML algorithms for PD prediction in the context of loan portfolios. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_19789 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A comparative analysis of machine learning algorithms for predicting probabilities of default Cristescu, Adrian Iulian Giordano, Matteo Risk Management Machine Learning Applications Predicting the probability of default (PD) of prospective loans is a critical objective for financial institutions. In recent years, machine learning (ML) algorithms have achieved remarkable success across a wide variety of prediction tasks; yet, they remain relatively underutilised in credit risk analysis. This paper highlights the opportunities that ML algorithms offer to this field by comparing the performance of five predictive models-Random Forests, Decision Trees, XGBoost, Gradient Boosting and AdaBoost-to the predominantly used logistic regression, over a benchmark dataset from Scheule et al. (Credit Risk Analytics: The R Companion). Our findings underscore the strengths and weaknesses of each method, providing valuable insights into the most effective ML algorithms for PD prediction in the context of loan portfolios. |
| title | A comparative analysis of machine learning algorithms for predicting probabilities of default |
| topic | Risk Management Machine Learning Applications |
| url | https://arxiv.org/abs/2506.19789 |