Supervised Statistical Methods to Identify Credit Acceptance Rate
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2021-04-10
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Universidad Eafit
Resumen
Since incorrect decisions can have detrimental effects on financial institutions, the possibility for these to forecast business failures becomes indispensable. In the financial domain, the focus of research problems rarely revolves around the identification of the clients who desist their credit offering, but rather on bankruptcy prediction and credit scoring. The general objective of this paper revolves around the implementation of supervised machine learning algorithms that will allow CrediOrbe, a credit company, to target customers whose profile assimilates those who desist their credit offering. Machine learning algorithms have been greatly studied as tools to aid decisions makers in the realm of finance. Performance measurements are calculated and analyzed through the use of statistical classification measurements. Suggestions for further research are provided