Examinando por Materia "Libranza"

Mostrando 1 - 2 de 2
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    Publicación
    Análisis de la utilidad potencial del mercado colombiano a través de modelos de segmentación y customer life value para una empresa originadora de créditos de libranza
    (Universidad EAFIT, 2022) González Cano, Juan José; Montoya Cano, Jorge Esteban; Ochoa, Natalia
    Currently companies define their target market to have a greater focus on certain individuals and groups of the population, however, they fail to understand in depth what is the future economic benefit that these market niches represent, to understand if their business model is attractive from a financial point of view. This project is directly focused on the Colombian financial sector, seeking to make a direct contribution to the way in which companies in this sector analyze and define the economic potential of their target market, through the use of analytical and financial tools such as segmentation models and Customer Life Value analysis, resulting in the value that each niche can possibly represent in utility for the company, allowing it to outline a business strategy that ensures sustainability over time and in the market. Thanks to the comprehensive capabilities of the project team, segmentation techniques will be used to support different types of variables to find very homogeneous groups in their individuals, but very heterogeneous among them and thus get to know which clusters will lead the company to obtain a greater benefit.
  • No hay miniatura disponible
    Publicación
    Clasificación de créditos de libranza negociados en el mercado secundario colombiano, aplicando técnicas de aprendizaje supervisado
    (Universidad EAFIT, 2024) Gómez Betancur , Juan Camilo; Moreno Reyes, Nicolás Alberto
    Credit risk, exacerbated by events such as the 2008 financial crisis, remains a concern for both banking and non-banking entities. This study addresses the need to improve the classification of payroll loans in Colombia using both traditional and machine learning techniques. It highlights the superior effectiveness of supervised learning algorithms in credit risk classification, with the ultimate goal of developing a model capable of identifying loans with a higher probability of default. This would optimize the acquisition of payroll loans and strengthen investment portfolio.