Credit risk scoring model based on the discriminant analysis technique
Credit risk models are vitally important for organizations whose corporate purpose is to operate profitably in the loan or credit business. Technological developments have enabled the application of different statistical techniques to create functions that assist in measuring, and consequently in ma...
- Autores:
-
Guzman Castillo, Stefania
Garizabalo Davila, Claudia
Alvear Montoya, Luis Guillermo
Gatica, Gustavo
Rodriguez Heraz, Jaiver Dario
Medina Tovar, Freddy Alfonso
Andrade Nieves, Sheyla Tatiana
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2023
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/13807
- Acceso en línea:
- https://hdl.handle.net/11323/13807
https://repositorio.cuc.edu.co/
- Palabra clave:
- Cost-effectiveness
Credit risk
Disbursement
Financial entities
Discriminant analysis
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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| dc.title.eng.fl_str_mv |
Credit risk scoring model based on the discriminant analysis technique |
| title |
Credit risk scoring model based on the discriminant analysis technique |
| spellingShingle |
Credit risk scoring model based on the discriminant analysis technique Cost-effectiveness Credit risk Disbursement Financial entities Discriminant analysis |
| title_short |
Credit risk scoring model based on the discriminant analysis technique |
| title_full |
Credit risk scoring model based on the discriminant analysis technique |
| title_fullStr |
Credit risk scoring model based on the discriminant analysis technique |
| title_full_unstemmed |
Credit risk scoring model based on the discriminant analysis technique |
| title_sort |
Credit risk scoring model based on the discriminant analysis technique |
| dc.creator.fl_str_mv |
Guzman Castillo, Stefania Garizabalo Davila, Claudia Alvear Montoya, Luis Guillermo Gatica, Gustavo Rodriguez Heraz, Jaiver Dario Medina Tovar, Freddy Alfonso Andrade Nieves, Sheyla Tatiana |
| dc.contributor.author.none.fl_str_mv |
Guzman Castillo, Stefania Garizabalo Davila, Claudia Alvear Montoya, Luis Guillermo Gatica, Gustavo Rodriguez Heraz, Jaiver Dario Medina Tovar, Freddy Alfonso Andrade Nieves, Sheyla Tatiana |
| dc.subject.proposal.eng.fl_str_mv |
Cost-effectiveness Credit risk Disbursement Financial entities Discriminant analysis |
| topic |
Cost-effectiveness Credit risk Disbursement Financial entities Discriminant analysis |
| description |
Credit risk models are vitally important for organizations whose corporate purpose is to operate profitably in the loan or credit business. Technological developments have enabled the application of different statistical techniques to create functions that assist in measuring, and consequently in managing, exposure to credit risk; however, these models must be periodically reassessed and optimized to ensure that they fulfill their objectives. This study addresses problems that have been observed in the model for reading the credit history of customers of a company in the real sector, contributing to the design of a risk-scoring model using the discriminant analysis technique. |
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2023 |
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2023-03-17 |
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2024-11-25T16:17:57Z |
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2024-11-25T16:17:57Z |
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Guzmán-Castillo Stefania, Garizabalo-Davila Claudia, Alvear-Montoya Luis Guillermo, Gatica Gustavo, Rodriguez-Heraz Jaiver Dario, Medina-Tovar Freddy Alfonso, Andrade-Nieves Sheyla Tatiana, Credit Risk Scoring Model Based on The Discriminant Analysis Technique, Procedia Computer Science, Volume 220, 2023, Pages 928-933, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2023.03.127. |
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https://hdl.handle.net/11323/13807 |
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10.1016/j.procs.2023.03.127 |
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1877-0509 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
| identifier_str_mv |
Guzmán-Castillo Stefania, Garizabalo-Davila Claudia, Alvear-Montoya Luis Guillermo, Gatica Gustavo, Rodriguez-Heraz Jaiver Dario, Medina-Tovar Freddy Alfonso, Andrade-Nieves Sheyla Tatiana, Credit Risk Scoring Model Based on The Discriminant Analysis Technique, Procedia Computer Science, Volume 220, 2023, Pages 928-933, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2023.03.127. 10.1016/j.procs.2023.03.127 1877-0509 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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https://hdl.handle.net/11323/13807 https://repositorio.cuc.edu.co/ |
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eng |
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eng |
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Procedia Computer Science |
| dc.relation.references.none.fl_str_mv |
Alonso, L. Berggrun., (2008). Introducción al análisis de riesgo financiero. Colección Discernir‐Universidad ICESI, Cali (2008). Ayuga-Téllez, E.; Contato-Carol, M. L.; González, C.; Grande-Ortiz, M. A.; Velázquez, J. 2011. Applying multivariate data analysis as objective method for calculating the location index for use in urban tree appraisal. Journal of Urban Planning and Development, New York, v. 137, n. 3, p. 230-237. Fuente Fernández, S. (2011). Análisis de Correspondencias Simples y Múltiples.Económicas y Empres., pp. 58–59. Guzmán Castillo, C. Garizabalo, L. G. Alverar Montoya, and G. Gatica, “Variables of the study,” 2022. [Online]. Available:https://docs.google.com/document/d/1Je0zAHUcd3XdmaXNyRNa6OSWZSd2hNyD/edit?usp=shari ng&ouid=107499429849986370707&rtpof=true&sd=true. Ludovic Leal Fica, A., Aranguiz Casanova, M. A., & Gallegos Mardones, J. (2018). Análisis de riesgo crediticio, propuesta del Modelo Credit Scoring. Revista de la Facultad de Ciencias Económicas: Investigación y Reflexión, XXVI(1), 181-207. Mark Schreiner., (2002) “Ventajas y Desventajas del Scoring Estadístico para las Microfinanzas Nota sobre el autor Ventajas y Desventajas del Scoring Estadístico para las Microfinanzas 1 . ¿ Qué es scoring ?,” pp. 1–40. Mendoza Méndez, Rafael V., Dorantes Coronado, Ernesto Joel, Cedillo Monroy, José, & Jasso Arriaga, Xóchitl. (2017). El método estadístico de análisis discriminante como herramienta de interpretación del estudio de adicción al móvil, realizado a los alumnos de la Licenciatura en Informática Administrativa del Centro Universitario UAEM Temascaltepec. RIDE. Revista Iberoamericana para la Investigación y el Desarrollo Educativo, 7(14), 222-247. https://doi.org/10.23913/ride.v7i14.282 Mures, M.J.; García, A. Y Vallejo, M.E. Aplicación del análisis discriminante y Regresión Logística en el estudio de la morosidad en las entidades financieras. Comparación de resultados. Pecvnia, 2005, vol. 1, p. 75- 199. Pérez Ramírez, F. O., & Támara Ayús, A. L. (2012). Análisis discriminante como seleccionador de variables influyentes en el cálculo de la probabilidad de incumplimiento. Revista Ciencias Estratégicas, 20(27), 103-118. Quejada Pérez, R., Yánez Contreras, M., & Cano Hernández, K. (2014). Determinantes de la Informalidad Laboral: Un Análisis para Colombia. Investigación & Desarrollo, 22(1), 126-145. Superintendencia Financiera de Colombia. (2020). Circular básica contable y financiera. Obtenido de Circular externa 100 de 1995. Available: https://www.superfinanciera.gov.co/inicio/normativa/normativageneral/circular-basica-contable-y-financiera-circular-externa--de---15466 Torres Avendaño, G., (2005). El Acuerdo de Basilea: Estado del Arte del SARC en Colombia. AD-minister, (6), 114-134. [3] J. C. Ochoa, W. Galeano, and L. G. Agudelo, “Construcción de un modelo de scoring para el otorgamiento de crédito en una,” no. 16, pp. 191–222, 2010. Wu, Y., Li, X., Liu, Q. et al. (2022). The Analysis of Credit Risks in Agricultural Supply Chain Finance Assessment Model Based on Genetic Algorithm and Backpropagation Neural Network. Comput Econ 60, 1269–1292 |
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© 2023 The Authors. |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)© 2023 The Authors.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Guzman Castillo, StefaniaGarizabalo Davila, ClaudiaAlvear Montoya, Luis GuillermoGatica, GustavoRodriguez Heraz, Jaiver DarioMedina Tovar, Freddy AlfonsoAndrade Nieves, Sheyla Tatiana2024-11-25T16:17:57Z2024-11-25T16:17:57Z2023-03-17Guzmán-Castillo Stefania, Garizabalo-Davila Claudia, Alvear-Montoya Luis Guillermo, Gatica Gustavo, Rodriguez-Heraz Jaiver Dario, Medina-Tovar Freddy Alfonso, Andrade-Nieves Sheyla Tatiana, Credit Risk Scoring Model Based on The Discriminant Analysis Technique, Procedia Computer Science, Volume 220, 2023, Pages 928-933, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2023.03.127.https://hdl.handle.net/11323/1380710.1016/j.procs.2023.03.1271877-0509Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Credit risk models are vitally important for organizations whose corporate purpose is to operate profitably in the loan or credit business. Technological developments have enabled the application of different statistical techniques to create functions that assist in measuring, and consequently in managing, exposure to credit risk; however, these models must be periodically reassessed and optimized to ensure that they fulfill their objectives. This study addresses problems that have been observed in the model for reading the credit history of customers of a company in the real sector, contributing to the design of a risk-scoring model using the discriminant analysis technique.6 páginasapplication/pdfengElsevier B.V.Netherlandshttps://www.sciencedirect.com/science/article/pii/S1877050923006622?via%3DihubCredit risk scoring model based on the discriminant analysis techniqueArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Procedia Computer ScienceAlonso, L. Berggrun., (2008). Introducción al análisis de riesgo financiero. Colección Discernir‐Universidad ICESI, Cali (2008).Ayuga-Téllez, E.; Contato-Carol, M. L.; González, C.; Grande-Ortiz, M. A.; Velázquez, J. 2011. Applying multivariate data analysis as objective method for calculating the location index for use in urban tree appraisal. Journal of Urban Planning and Development, New York, v. 137, n. 3, p. 230-237.Fuente Fernández, S. (2011). Análisis de Correspondencias Simples y Múltiples.Económicas y Empres., pp. 58–59.Guzmán Castillo, C. Garizabalo, L. G. Alverar Montoya, and G. Gatica, “Variables of the study,” 2022. [Online]. Available:https://docs.google.com/document/d/1Je0zAHUcd3XdmaXNyRNa6OSWZSd2hNyD/edit?usp=shari ng&ouid=107499429849986370707&rtpof=true&sd=true.Ludovic Leal Fica, A., Aranguiz Casanova, M. A., & Gallegos Mardones, J. (2018). Análisis de riesgo crediticio, propuesta del Modelo Credit Scoring. Revista de la Facultad de Ciencias Económicas: Investigación y Reflexión, XXVI(1), 181-207.Mark Schreiner., (2002) “Ventajas y Desventajas del Scoring Estadístico para las Microfinanzas Nota sobre el autor Ventajas y Desventajas del Scoring Estadístico para las Microfinanzas 1 . ¿ Qué es scoring ?,” pp. 1–40.Mendoza Méndez, Rafael V., Dorantes Coronado, Ernesto Joel, Cedillo Monroy, José, & Jasso Arriaga, Xóchitl. (2017). El método estadístico de análisis discriminante como herramienta de interpretación del estudio de adicción al móvil, realizado a los alumnos de la Licenciatura en Informática Administrativa del Centro Universitario UAEM Temascaltepec. RIDE. Revista Iberoamericana para la Investigación y el Desarrollo Educativo, 7(14), 222-247. https://doi.org/10.23913/ride.v7i14.282Mures, M.J.; García, A. Y Vallejo, M.E. Aplicación del análisis discriminante y Regresión Logística en el estudio de la morosidad en las entidades financieras. Comparación de resultados. Pecvnia, 2005, vol. 1, p. 75- 199.Pérez Ramírez, F. O., & Támara Ayús, A. L. (2012). Análisis discriminante como seleccionador de variables influyentes en el cálculo de la probabilidad de incumplimiento. Revista Ciencias Estratégicas, 20(27), 103-118.Quejada Pérez, R., Yánez Contreras, M., & Cano Hernández, K. (2014). Determinantes de la Informalidad Laboral: Un Análisis para Colombia. Investigación & Desarrollo, 22(1), 126-145.Superintendencia Financiera de Colombia. (2020). Circular básica contable y financiera. Obtenido de Circular externa 100 de 1995. Available: https://www.superfinanciera.gov.co/inicio/normativa/normativageneral/circular-basica-contable-y-financiera-circular-externa--de---15466Torres Avendaño, G., (2005). El Acuerdo de Basilea: Estado del Arte del SARC en Colombia. AD-minister, (6), 114-134. [3] J. C. Ochoa, W. Galeano, and L. G. Agudelo, “Construcción de un modelo de scoring para el otorgamiento de crédito en una,” no. 16, pp. 191–222, 2010.Wu, Y., Li, X., Liu, Q. et al. (2022). The Analysis of Credit Risks in Agricultural Supply Chain Finance Assessment Model Based on Genetic Algorithm and Backpropagation Neural Network. Comput Econ 60, 1269–1292933928220Cost-effectivenessCredit riskDisbursementFinancial entitiesDiscriminant analysisPublicationLICENSElicense.txtlicense.txttext/plain; charset=utf-815543https://repositorio.cuc.edu.co/bitstreams/18427cc5-8538-4f91-8882-59cbb31d5585/download73a5432e0b76442b22b026844140d683MD51ORIGINALCredit Risk Scoring Model Based on The Discriminant Analysis Technique.pdfCredit Risk Scoring Model Based on The Discriminant Analysis Technique.pdfapplication/pdf586133https://repositorio.cuc.edu.co/bitstreams/12e62272-ad83-4bef-bc29-3801e4434218/downloadf94231a5b62ed4f675368c59c4f10010MD52TEXTCredit Risk Scoring Model Based on The Discriminant Analysis Technique.pdf.txtCredit Risk Scoring Model Based on The Discriminant Analysis Technique.pdf.txtExtracted texttext/plain48010https://repositorio.cuc.edu.co/bitstreams/42d00a10-eaa8-44c5-a59c-1046ac27d867/downloada3fbcefce649a7619f58bf590fe03e26MD53THUMBNAILCredit Risk Scoring Model Based on The Discriminant Analysis 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ara ejercer estos derechos sobre la Obra tal y como se indica a continuación:</p>
    <ol type="a">
      <li>Reproducir la Obra, incorporar la Obra en una o más Obras Colectivas, y reproducir la Obra incorporada en las Obras Colectivas.</li>
      <li>Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.</li>
      <li>Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.</li>
    </ol>
    <p>Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).</p>
  </li>
  <br/>
  <li>
    Restricciones.
    <p>La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:</p>
    <ol type="a">
      <li>Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).</li>
      <li>Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.</li>
      <li>Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.</li>
      <li>
        Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:
        <ol type="i">
          <li>Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.</li>
          <li>Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
        </ol>
      </li>
      <li>Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
    </ol>
  </li>
  <br/>
  <li>
    Representaciones, Garantías y Limitaciones de Responsabilidad.
    <p>A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Limitación de responsabilidad.
    <p>A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Término.
    <ol type="a">
      <li>Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.</li>
      <li>Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.</li>
    </ol>
  </li>
  <br/>
  <li>
    Varios.
    <ol type="a">
      <li>Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.</li>
      <li>Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.</li>
      <li>Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.</li>
      <li>Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.</li>
    </ol>
  </li>
  <br/>
</ol>
 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