Examinando por Materia "Random forest"

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    Ítem
    Contribuciones desde un modelo predictivo para identificar el perfil de riesgo del defraudador interno en una entidad financiera de Colombia
    (Universidad EAFIT, 2018) Ruíz Galeano, Fabio Hernán; Torres Guerra, Idier Albeiro
    Financial services companies are a fundamental part of the country's economy, and their continued growth has made them more susceptible to fraud due to the lack of prevention and controls that mitigate the threats that go hand in hand with technological innovation, new products and consumption characteristics -- This problem generates negative economic and social impacts for both the company and the country -- That is why it was identified the need to develop the risk profile of the internal fraudster in a financial entity, which allows the frauds prevention and detection from the characterization of employees involved in fraudulent actions, to achieve this, the mixed investigation was applied, which allowed the collection of data on fraud materialized by employees, their numerical measurement and respective statistical analysis to test the hypothesis, as well as a literature review -- From the construction of the Random Forest model, and with the objective of finding employees with high probabilities of incurring in incorrect acts, the profile of the internal fraudster of the entity was identified, the employee who work in the branch network, who have atypical transactions and where the wage relationships Vs the discounts made to the employee do not keep a proportion -- Employees who hold operational positions which represent the 44.5% of the company, are the most likely to incur internal fraud, 66.4% of possible fraudsters are women; moreover, country’s regions that present greater alert are the central region with 43.6%, followed by Bogotá and Sabana with 28.7%
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    Publicación
    Modelo predictivo para optimizar el proceso de selección de aspirantes a becas talento en la Universidad EAFIT
    (Universidad EAFIT, 2025) Acosta Ospina, Juan Pablo; Tabares Betancur, Marta Silvia del Socorro
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    Publicación
    Optimización de un portafolio de inversión con acciones del Colcap aplicando técnicas de machine learning
    (Universidad EAFIT, 2024) Osorio Buitrón, Maribel; Rico Villareal, Juan David; Rojas Ormanza, Bryan Ricardo
    Based on the increases in the volumes of historical information on the shares of public companies, the question arises as to whether it is possible to create better optimized portfolios than those generated from traditional theory, making use of recent innovations in artificial intelligence and analysis of data from the last decade. For this reason, the present research aims to compare the traditional theory of portfolio optimization with the recent data analysis methodologies applied to Colcap. The methodology used is based on twelve previous investigations which had tested and demonstrated the performance of different machine learning models on stock exchanges around the world, such as S&P500, NASDAQ, DAX, SET and Colcap. From here, the best prospects were selected and applied to Colcap shares, to predict the future movement in the share price based on the historical behavior of certain significant variables and then compared with the traditional methodology. It was found that the best prediction model in the price movement applied to Colcap is the Random Forest, and the variables that best explain the future changes in the price of the shares of this exchange are the closing price of the share, the TRM index and the Colcap index. In addition, machine learning models managed to optimize portfolios with a smaller number of shares and higher returns backed by historical information.
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    Publicación
    RF-kNN: A Novel Ensemble Method for Improved Classification tasks
    (Universidad EAFIT, 2023) Muñoz Mercado, José Jorge; Almonacid Hurtado, Paula María; López Aguirre, Esteban