Examinando por Materia "Cartera hipotecaria"

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    ¿CFEN, indicador de impacto en la cartera hipotecaria en Colombia?
    (Universidad EAFIT, 2024) Alonso Piñeros, Jenniffer; Durango Gutiérrez, María Patricia
    The focal aim of this work is to measure the impact of integrating CFEN indicator on mortgage portfolio in Colombia during application period (2020-2024), through an econometrical model made for Davivienda bank in Colombia, by which is demonstrated the relationship between economic variables (Inflation, Monetary Policy Rate, GDP, Passive Deposit Rates and NSFR) and the accomplishment of above-mentioned indicator. This document demonstrates that, after pandemia, externalities influence the mortgage portfolio dynamism as well as mortgage VIS and not VIS. On the other hand, is concluded that increases on funding rates as consequence of CFEN implementation, are negative related with the comportment of disbursements within the mortgage portfolio over the period assessed for Davivienda Bank, as evaluated in this document.
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    Modelos de riesgo crediticio para reducir el riesgo de crédito en la cartera hipotecaria del Banco Unión
    (Universidad EAFIT, 2026) 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.