 Using tree-based models to predict credit riskNathan Coates, Robert Nydick and D.K. Malhotra International Journal of Business Intelligence and Systems Engineering, 2024, vol. 2, issue 1, 23-42 Abstract: Despite an increase in consumer bankruptcies, the consumer loan industry is increasingly competitive. Financial organisations may find that well-allocated credits are one of the most lucrative sources of income. However, a high degree of risk is associated with this type of banking activity because many incorrect judgements might force the lending institution into bankruptcy. The main goal of credit risk evaluation research is to develop classification rules that properly classify bank clients as either good credit or bad credit loan applicants. This study shows how to use tree-based algorithms, such as decision trees, random forests trees, boosted trees, and XGBoost, to lower the risk of bad loans and find the traits that can help differentiate between a good loan and a bad loan. This will allow loan officers to improve their scoring models by giving those traits more weight when deciding whether to extend loans to borrowers. Lending institutions can protect themselves from legal or regulatory problems by explaining the factors that led them to decide against lending to a potential borrower. Keywords: consumer loans; credit risk; decision trees; bootstrap trees; boosted trees. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbise:v:2:y:2024:i:1:p:23-42 Access Statistics for this article More articles in International Journal of Business Intelligence and Systems Engineering from Inderscience Enterprises Ltd |
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