Examinando por Materia "Logit model"

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    Evidencia empírica de los principales determinantes del altruismo a nivel mundial
    (Universidad EAFIT, 2018) Hurtado Montoya, Estefanía; García Rendón, Jhon Jairo
    In recent studies, a direct effect has been observed between altruism and economic progress, through the strengthening of human capital and the promotion of efficiency and productivity. This study identifies the socioeconomic characteristics that determine the altruistic behavior in 60 countries. The methodology used is based on the estimation of two ordered logits and data from the World Values Survey wave 6. Results suggest that all forms of capital (social and human) and economic resources as membership in unions and income have a direct and statistically significant impact when it comes to playing an active role in voluntary organizations.
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    Modelos de riesgo crediticio para reducir el riesgo de crédito en la cartera hipotecaria del Banco Unión
    (Universidad EAFIT, 2026-02-26) Córdoba Ruiz, Ana Patricia; Rojas Ormaza, Brayan Ricardo
    Banco Unión’s mortgage portfolio has increased its exposure to risk as a result of post-pandemic economic uncertainty, evidenced by higher unemployment levels, inflationary pressures, and sustained increases in interest rates. Within this context, the study examines credit risk models designed to enhance the predictive capacity of portfolio deterioration and to support decision-making in the management of mortgage portfolios. The analysis relies on a historical database provided by Banco Unión, which contains information on performing and impaired mortgage loans. This dataset includes macroeconomic variables, credit-specific attributes, and the sociodemographic and financial characteristics of borrowers. From a methodological perspective, a binary Logit model was estimated to assess the probability of default, analyzing the marginal impact of key factors such as income level, interest rate, macroeconomic conditions, and loan maturity. In addition, a Decision Tree model was implemented on the KNIME platform using AutoML techniques, following sample balancing procedures. The predictive performance of the models was evaluated using standard metrics in the financial sector: the area under the ROC curve (AUC), sensitivity (recall), and precision. The results show that the Logit model provides a strong explanatory framework for the determinants of credit risk, while the Decision Tree exhibits greater sensitivity in identifying impaired borrowers while maintaining high levels of precision. By combining both perspectives, Banco Unión gains a more robust analytical tool to anticipate portfolio deterioration, improve customer segmentation, and strengthen credit risk monitoring and mitigation strategies in a changing economic environment, thereby aligning statistical standards with operational and regulatory criteria.