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Diseño de una estrategia para la gestión de cobranza, a través de Big Data Analytics en empresas de venta por catálogo
dc.contributor.advisor | García Cardona, Alejandra | |
dc.contributor.author | Mira Orozco, Lina María | |
dc.coverage.spatial | Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degrees | eng |
dc.coverage.spatial | Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degreesLong: 075 36 00 W degrees minutes Long: -75.6000 decimal degrees | |
dc.date.accessioned | 2021-06-01T15:42:33Z | |
dc.date.available | 2021-06-01T15:42:33Z | |
dc.date.created | 2020-12-15 | |
dc.identifier.other | T 0070 2020 | |
dc.identifier.uri | http://hdl.handle.net/11407/6378 | |
dc.description | Este trabajo del MBA presenta un diseño de la estrategia de gestión de cobranza a través de Big Data Analytics, en empresas de Venta por Catálogo. La metodología empleada para lograr el objetivo del trabajo consiste en una revisión sistemática de literatura y un análisis cualitativo, con el fin de identificar, describir, profundizar y finalmente divulgar la estrategia. Es así como el método de investigación se empleó de la siguiente forma: (1) Planear el protocolo de revisión; (2) Identificación y clasificación de literatura orientada al objeto de estudio; (3) Descripción de literatura de la evolución del objeto de estudio, y (4) finalmente, la entrega de resultados. Como resultado se diseña la estrategia de gestión de cobranza a través de Big Data Analytics en empresas de Venta por Catálogo, en tres categorías enmarcadas en el ciclo de vigencia del crédito: (1) Otorgamiento del crédito, (2) Seguimiento al Comportamiento del uso del crédito y (3) Recuperación del crédito. Asimismo, el trabajo es un buen ejemplo de cómo emplear estas estrategias en empresas orientadas al desarrollo del canal comercial, lo cual asegura crecimiento, pero al mismo tiempo protege la estructura financiera de la empresa, pues permite segmentar los perfiles de los clientes, y genera estrategias customizadas de acuerdo con el riesgo, de tal forma que minimice la probabilidad de pérdida de la empresa. (la probabilidad de pérdidas que podría tener la empresa). | |
dc.description.abstract | The present study of MBA seeks to design a collection management strategy using Big Data Analytics implemented in Direct Selling Companies. The methodology used to achieve the main objective is based on a systematic literature review and a qualitative analysis, in order to identify, describe, deepen and communicate the final strategy. The research method was applied in four core activities: (1) Planning the review protocol ; (2) Identification and classification of literature approach to the object of study; (3) Literature description of the evolution of the study and finally (4) Results. As a result, the strategy design model applied in Collections management through Big Data Analytics in Direct Selling companies, the model design was divided into three categories focused on the credit cycle (1) Credit Granting, (2) Credit Use Behavior and (3) Credit Recovery. Likewise, this study is a good example of how to use these strategies in companies oriented to the development of the commercial channel, which ensure growth, but at the same time protect the financial structure of the company, by segmenting customer profiles, generating personalized strategies according to the risk, thus minimizing the possibility of loss of the company. | |
dc.format.extent | p. 1-47 | |
dc.format.medium | Electrónico | |
dc.format.mimetype | application/pdf | |
dc.language.iso | spa | |
dc.publisher | Universidad de Medellín | spa |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0 | |
dc.subject | Gestión de cobranza | |
dc.subject | Riesgo | |
dc.subject | Probabilidad de incumplimiento | |
dc.subject | Probabilidad de pago | |
dc.subject | Herramientas de analítica | |
dc.subject | Big data analytics | |
dc.subject | Machine learning | |
dc.subject | Venta por catálogo | |
dc.title | Diseño de una estrategia para la gestión de cobranza, a través de Big Data Analytics en empresas de venta por catálogo | |
dc.rights.accessrights | info:eurepo/semantics/openAccess | |
dc.publisher.program | Maestría en Administración - MBA | |
dc.subject.lemb | Administración de créditos | |
dc.subject.lemb | Administración de ventas | |
dc.subject.lemb | Cobro de cuentas | |
dc.subject.lemb | Cuentas por cobrar | |
dc.subject.lemb | Minería de datos | |
dc.subject.lemb | Riesgo (Finanzas) | |
dc.subject.keyword | Collection management | |
dc.subject.keyword | Risk | |
dc.subject.keyword | Probability of default | |
dc.subject.keyword | Probability of payment | |
dc.subject.keyword | Analytical tools | |
dc.subject.keyword | Big data analysis | |
dc.subject.keyword | Machine learning | |
dc.subject.keyword | Direct selling | |
dc.relation.citationstartpage | 1 | |
dc.relation.citationendpage | 47 | |
dc.audience | Comunidad Universidad de Medellín | |
dc.publisher.faculty | Facultad de Ciencias Económicas y Administrativas | |
dc.publisher.place | Medellín | |
dc.type.hasversion | info:eu-repo/semantics/publishedVersion | |
dc.type.hasversion | info:eu-repo/semantics/acceptedVersion | |
dc.relation.references | "Addo, P. M., Guegan, D., & Hassani, B. (2018). Credit risk analysis using machine and deep learning models. Risks, 6(2), 1-20. https://doi.org/10.3390/risks6020038 | |
dc.relation.references | Ala'raj, M., Abbod, M., & Radi, M. (2018). The applicability of credit scoring models in emerging economies: an evidence from Jordan. International Journal of Islamic and Middle Eastern Finance and Management, 11(4), 608-630. https://doi.org/10.1108/IMEFM-02-2017-0048 | |
dc.relation.references | Alzeaideen, K. (2019). Credit risk management and business intelligence approach of the banking sector in Jordan. Cogent Business and Management, 6(1), 1-9. https://doi.org/10.1080/23311975.2019.1675455 | |
dc.relation.references | Anderson, R. (2007). The {Credit} {Scoring} {Toolkit}: {Theory} and {Practice} for {Retail} {Credit} {Risk} {Management} and {Decision} {Automation}. | |
dc.relation.references | Antoine, D., & Abdallah, N. (2006). MODELLING OF THE DEBTS COLLECTION PROCESS FOR SERVICE COMPANIES DELOFFRE Antoine | |
dc.relation.references | NAIT ABDALLAH Rabie | |
dc.relation.references | . Context, 349-354. https://doi.org/10.3182/20060517-3-FR-2903.00188 | |
dc.relation.references | Antonio, J., & Castro, M. (2014). Crédito y Cobranza. México D.F. | |
dc.relation.references | BCBS. (2011). Basel III: A global regulatory framework for more resilient banks and banking systems December 2010 (rev June 2011). Bcbs 189 (Vol. 2010). Retrieved from http://www.bis.org/publ/bcbs189.pdf | |
dc.relation.references | Bijak, K., & Thomas, L. C. (2012). Does segmentation always improve model performance in credit scoring? Expert Systems with Applications, 39(3), 2433-2442. https://doi.org/10.1016/j.eswa.2011.08.093 | |
dc.relation.references | Bülbül, D., Hakenes, H., & Lambert, C. (2019). What influences banks' choice of credit risk management practices? Theory and evidence. Journal of Financial Stability, 40, 1-14. https://doi.org/10.1016/j.jfs.2018.11.002 | |
dc.relation.references | Bumacov, V., Ashta, A., & Singh, P. (2017). Credit scoring: A historic recurrence in microfinance. Strategic Change, 26(6), 543-554. https://doi.org/10.1002/jsc.2165 | |
dc.relation.references | Butaru, F., Chen, Q., Clark, B., Das, S., Lo, A. W., & Siddique, A. (2016). Risk and risk management in the credit card industry. Journal of Banking and Finance, 72, 218-239. https://doi.org/10.1016/j.jbankfin.2016.07.015 | |
dc.relation.references | Cai, S., & Zhang, J. (2020). Exploration of credit risk of P2P platform based on data mining technology. Journal of Computational and Applied Mathematics, 372, 112718. https://doi.org/10.1016/j.cam.2020.112718 | |
dc.relation.references | Carta, S., Ferreira, A., Reforgiato Recupero, D., Saia, M., & Saia, R. (2020). A combined entropy-based approach for a proactive credit scoring. Engineering Applications of Artificial Intelligence, 87(October 2019), 103292. https://doi.org/10.1016/j.engappai.2019.103292 | |
dc.relation.references | Chamasrour, V., Fiorillo, C., & Goslin, D. (2012). Tendencias de cobranza y recuperación de cartera en el sector financiero a partir de la crisis Punto de vista sobre las prácticas para eficientar la labor de cobranza de las instituciones financieras Contenido. | |
dc.relation.references | Chen, H., Chiang, R. H. L., & Storey, V. C. (2018a). Business Intelligence and Analytics: From Big Data to Big Impact, 36(4), 1165-1188. | |
dc.relation.references | Chen, H., Chiang, R. H. L., & Storey, V. C. (2018b). Chen_Chiang_Storey_Business Intelligence and Analytics_from big data to big impact.pdf, 36(4), 1165-1188. | |
dc.relation.references | Chen, X., Wang, G., & Zhang, X. (2019). Modeling recovery rate for leveraged loans, (March). https://doi.org/10.1016/j.econmod.2019.04.006 | |
dc.relation.references | Chi, G., & Meng, B. (2019). Debt rating model based on default identification: Empirical evidence from Chinese small industrial enterprises. Management Decision, 57(9), 2239-2260. https://doi.org/10.1108/MD-11-2017-1109 | |
dc.relation.references | Chijoriga, M. M. (2011). assessment model Application of multiple discriminant analysis ( MDA ) as a credit scoring and risk assessment model. https://doi.org/10.1108/17468801111119498 | |
dc.relation.references | Cohen, A., & Costanzino, N. (2017). A General Framework for Incorporating Stochastic Recovery in Structural Models of Credit Risk. Risks, 5(4), 65. https://doi.org/10.3390/risks5040065 | |
dc.relation.references | Crespo, I., & Govindarajan, A. (2018). The analytics-enabled collections model. | |
dc.relation.references | Crittenden, V. L., & Albaum, G. (2015). The misplaced controversy about internal consumption: Not just a direct selling phenomenon. Business Horizons, 58(4), 421-429. https://doi.org/10.1016/j.bushor.2015.03.007 | |
dc.relation.references | Danstun, N., & Harun, M. (2020). The effect of credit collection policy on portfolio at risk of microfinance institutions in Tanzania. Studies in Business and Economics, 14(3), 131-144. https://doi.org/10.2478/sbe-2019-0049 | |
dc.relation.references | Davenport, T. H. (2006). Competing on analytics. Harvard Business Review, 84(1). | |
dc.relation.references | Davenport, T. H. (2014). How strategists use ""big data"" to support internal business decisions, discovery and production. Strategy and Leadership, 42(4), 45-50. https://doi.org/10.1108/SL-05-2014-0034 | |
dc.relation.references | De Paula, D. A. V., Artes, R., Ayres, F., & Minardi, A. M. A. F. (2019). Estimating credit and profit scoring of a Brazilian credit union with logistic regression and machine-learning techniques. RAUSP Management Journal, 54(3), 321-336. https://doi.org/10.1108/RAUSP-03-2018-0003 | |
dc.relation.references | Deloitte. (2016). A fresh perspective Collections strategies for the digital age. Retrieved from https://www2.deloitte.com/content/dam/Deloitte/za/Documents/financial-services/ZA_Digital-age_160916.pdf | |
dc.relation.references | Derbali, A., & Jamel, L. (2019). Dependence of Default Probability and Recovery Rate in Structural Credit Risk Models: Case of Greek Banks. Journal of the Knowledge Economy. https://doi.org/10.1007/s13132-017-0473-1 | |
dc.relation.references | Djeundje, V. B., & Crook, J. (2019). Dynamic survival models with varying coefficients for credit risks. European Journal of Operational Research, 275(1), 319-333. https://doi.org/10.1016/j.ejor.2018.11.029 | |
dc.relation.references | Duffy, D. L. (2005). Direct selling as the next channel. Journal of Consumer Marketing, 22(1), 43-45. https://doi.org/10.1108/07363760510576545 | |
dc.relation.references | Ferrell, L., & Ferrell, O. C. (2012). Redirecting direct selling: High-touch embraces high-tech. Business Horizons, 55(3), 273-281. https://doi.org/10.1016/j.bushor.2012.01.004 | |
dc.relation.references | Gambetti, P., Gauthier, G., & Vrins, F. (2019). Recovery rates: Uncertainty certainly matters. Journal of Banking & Finance, 106, 371-383. https://doi.org/10.1016/j.jbankfin.2019.07.010 | |
dc.relation.references | Gitman, L. J., Angel, M., Carrión, S., Castro, A. M., Antonio, J., Castro, M., & Contreras, S. (2007). Administración financiera Decimoprimera edición. Retrieved from https://profesorjulioraya.files.wordpress.com/2014/12/12020033.pdf | |
dc.relation.references | Grant, A., & Deer, L. (2019). Consumer marketplace lending in Australia: Credit scores and loan funding success. Australian Journal of Management, (September). https://doi.org/10.1177/0312896219883678 | |
dc.relation.references | Griva, A., Bardaki, C., Pramatari, K., & Papakiriakopoulos, D. (2018). Retail business analytics: Customer visit segmentation using market basket data. Expert Systems with Applications, 100, 1-16. https://doi.org/10.1016/j.eswa.2018.01.029 | |
dc.relation.references | Han, C., & Jang, Y. (2013). Effects of debt collection practices on loss given default. Journal of Banking and Finance, 37(1), 21-31. https://doi.org/10.1016/j.jbankfin.2012.08.009 | |
dc.relation.references | Harrison, D. E., & Hair, J. F. (2017). The Use of Technology in Direct-Selling Marketing Channels: Digital Avenues for Dynamic Growth. Journal of Marketing Channels, 24(1-2), 39-50. https://doi.org/10.1080/1046669X.2017.1346979 | |
dc.relation.references | Higginson, M., Jacques, F., & Rudisuli, R. (2019). Toward distinctive collections operations. McKinseys Boston Office. | |
dc.relation.references | Hindle, G., Kunc, M., Mortensen, M., Oztekin, A., & Vidgen, R. (2020). Business analytics: Defining the field and identifying a research agenda. European Journal of Operational Research, 281(3), 483-490. https://doi.org/10.1016/j.ejor.2019.10.001 | |
dc.relation.references | Hung, J.-L. L., He, W., & Shen, J. (2020). Big data analytics for supply chain relationship in banking. Industrial Marketing Management, 86(October), 144-153. https://doi.org/10.1016/j.indmarman.2019.11.001 | |
dc.relation.references | Izar, J. M., & Ynzunza, C. B. (2017). El impacto del crédito y la cobranza en las utilidades Credit and Collection Profits Impact, 13, 47-62. | |
dc.relation.references | Jalao, E. R. L. (2015). Developing the Manpower Complement for Business Analytics Service Professionals: A Case Study on the Challenges Faced by the Philippines. Procedia | |
dc.relation.references | Manufacturing, 3(Ahfe), 3494-3497. https://doi.org/10.1016/j.promfg.2015.07.661 | |
dc.relation.references | Jiang, C., Wang, Z., & Zhao, H. (2019). A prediction-driven mixture cure model and its application in credit scoring. European Journal of Operational Research, 277(1), 20-31. https://doi.org/10.1016/j.ejor.2019.01.072 | |
dc.relation.references | Kanapickiene, R., & Spicas, R. (2019). Credit risk assessment model for small and micro-enterprises: The case of Lithuania. Risks, 7(2), 1-23. https://doi.org/10.3390/risks7020067 | |
dc.relation.references | Krishnamoorthi, S., & Mathew, S. K. (2018). Business analytics and business value: A comparative case study. Information and Management, 55(5), 643-666. https://doi.org/10.1016/j.im.2018.01.005 | |
dc.relation.references | Leonard, K. J. (1995). The development of credit scoring quality measures for consumer credit applications. International Journal of Quality & Reliability Management, 12(4), 79-85. https://doi.org/10.1108/02656719510087346 | |
dc.relation.references | Liebman, L. H. (1972). A Markov Decision Model for Selecting Optimal Credit Control Policies. Management Science, 18(10), B-519-B-525. https://doi.org/10.1287/mnsc.18.10.b519 | |
dc.relation.references | Makuch, W. M., Dodge, J. L., Ecker, J. G., Granfors, D. C., & Hahn, G. J. (1992). Managing Consumer Credit Delinquency in the US Economy: A Multi-Billion Dollar Management Science Application. Interfaces, 22(1), 90-109. https://doi.org/10.1287/inte.22.1.90 | |
dc.relation.references | Maldonado, S., Peters, G., & Weber, R. (2018). Credit scoring using three-way decisions with probabilistic rough sets. Information Sciences, 507, 700-714. https://doi.org/10.1016/j.ins.2018.08.001 | |
dc.relation.references | Mili, M., Sahut, J. M., & Teulon, F. (2018). Modeling recovery rates of corporate defaulted bonds in developed and developing countries. Emerging Markets Review, 36(November 2017), 28-44. https://doi.org/10.1016/j.ememar.2018.03.001 | |
dc.relation.references | Moradi, S., & Mokhatab Rafiei, F. (2019). A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks. Financial Innovation, 5(1). https://doi.org/10.1186/s40854-019-0121-9 | |
dc.relation.references | Onay, C., & Öztürk, E. (2018). A review of credit scoring research in the age of Big Data. Journal of Financial Regulation and Compliance, 26(3), 382-405. https://doi.org/10.1108/JFRC-06-2017-0054 | |
dc.relation.references | Onay, C., Öztürk, E., Jiang, C., Wang, Z., Zhao, H., Tsao, Y. C., ? Li, W. (2019). Modelling repayment patterns in the collections process for unsecured consumer debt: A case study. European Journal of Operational Research, 26(1), 1-29. https://doi.org/10.1002/jsc.2165 | |
dc.relation.references | Óskarsdóttir, M., Bravo, C., Sarraute, C., Vanthienen, J., & Baesens, B. (2019). The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics. Applied Soft Computing Journal, 74, 26-39. https://doi.org/10.1016/j.asoc.2018.10.004 | |
dc.relation.references | Panyagometh, K. (2019). Impactof baseline population on creditscore's predictive power. Economics and Sociology, 12(1), 262-269. https://doi.org/10.14254/2071-789X.2019/12-1/15 | |
dc.relation.references | Pardo Cariillo, O. S., & Díaz Castro, J. (2020). Perfil de riesgo de crédito para una cooperativa en Villavicencio a partir de un modelo Logit. Revista Universidad y Empresa, 22(38), 237. https://doi.org/10.12804/revistas.urosario.edu.co/empresa/a.8266 | |
dc.relation.references | Pérez-Martín, A., Pérez-Torregrosa, A., & Vaca, M. (2018). Big Data techniques to measure credit banking risk in home equity loans. Journal of Business Research, 89(February), 448-454. https://doi.org/10.1016/j.jbusres.2018.02.008 | |
dc.relation.references | Pérez Rave, J. I. (2013). Revisión sistemática de literatura en Ingeniería como apoyo a la Consultoría basada en Investigación. Universidad, Ciencia y Tecnología, 17(66), 38-48. | |
dc.relation.references | Peterson, R. A., Crittenden, V. L., & Albaum, G. (2019). On the economic and social benefits of direct selling. Business Horizons, 62(3), 373-382. https://doi.org/10.1016/j.bushor.2018.12.002 | |
dc.relation.references | Rehman, M. H., Yaqoob, I., Salah, K., Imran, M., Jayaraman, P. P., & Perera, C. (2019). The role of big data analytics in industrial Internet of Things. Future Generation Computer Systems, 99, 247-259. https://doi.org/10.1016/j.future.2019.04.020 | |
dc.relation.references | Rialti, R., Zollo, L., Ferraris, A., & Alon, I. (2019). Big data analytics capabilities and performance: Evidence from a moderated multi-mediation model. Technological Forecasting and Social Change, 149(October), 119781. https://doi.org/10.1016/j.techfore.2019.119781 | |
dc.relation.references | Santoro, G., Fiano, F., Bertoldi, B., & Ciampi, F. (2019). Big data for business management in the retail industry. Management Decision, 57(8), 1980-1992. https://doi.org/10.1108/MD-07-2018-0829 | |
dc.relation.references | Shi, B., Zhao, X., Wu, B., & Dong, Y. (2019). Credit rating and microfinance lending decisions based on loss given default (LGD). Finance Research Letters, 30(March), 124-129. https://doi.org/10.1016/j.frl.2019.03.033 | |
dc.relation.references | Stanworth, J., Brodie, S., Wotruba, T., & Purdy, D. (2004). Outsourcing salesforces via self-employment: The case of direct selling in the UK. Journal of Small Business and Enterprise Development, 11(1), 50-59. https://doi.org/10.1108/14626000410519092 | |
dc.relation.references | Takahashi, M., Azuma, H., & Tsuda, K. (2015). A study on deliberate presumptions of customer payments with reminder in the absence of face-to-face contact transactions. Procedia Computer Science, 60(1), 968-975. https://doi.org/10.1016/j.procs.2015.08.136 | |
dc.relation.references | Tang, L., Cai, F., & Ouyang, Y. (2019). Applying a nonparametric random forest algorithm to assess the credit risk of the energy industry in China. Technological Forecasting and Social Change, 144(March 2018), 563-572. https://doi.org/10.1016/j.techfore.2018.03.007 | |
dc.relation.references | Thomas, L. C., Matuszyk, A., & Moore, A. (2012). Comparing debt characteristics and LGD models for different collections policies. International Journal of Forecasting, 28(1), 196-203. https://doi.org/10.1016/j.ijforecast.2010.11.004 | |
dc.relation.references | Thomas, Lyn C., Matuszyk, A., So, M. C., Mues, C., & Moore, A. (2016). Modelling repayment patterns in the collections process for unsecured consumer debt: A case study. European Journal of Operational Research, 249(2), 476-486. https://doi.org/10.1016/j.ejor.2015.09.013 | |
dc.relation.references | Tsao, Y. C. (2017). Managing default risk under trade credit: Who should implement Big-Data analytics in supply chains? Transportation Research Part E: Logistics and Transportation Review, 106, 276-293. https://doi.org/10.1016/j.tre.2017.08.013 | |
dc.relation.references | Velásquez, A. B. (2013). DISEÑO DE UN MODELO PREDICTIVO DE SEGUIMIENTO DE RIESGO DE CRÉDITO PARA LA CARTERA COMERCIAL, PARA UNA ENTIDAD FINANCIERA DEL VALLE DE ABURRÁ. | |
dc.relation.references | Yadi, L. I. U., Yuning, S., Jiayue, Y. U., Yingfa, X. I. E., Yiyuan, W., & Xiaoping, Z. (2019). Big-data-driven Model Construction and Empirical Analysis of SMEs Credit Assessment in China. Procedia Computer Science, 147, 613-619. https://doi.org/10.1016/j.procs.2019.01.205 | |
dc.relation.references | Zanin, M., Papo, D., Sousa, P. A., Menasalvas, E., Nicchi, A., Kubik, E., & Boccaletti, S. (2016). Combining complex networks and data mining: Why and how. Physics Reports, 635, 1-44. https://doi.org/10.1016/j.physrep.2016.04.005" | |
dc.rights.creativecommons | Attribution-NonCommercial-ShareAlike 4.0 International | |
dc.type.local | Tesis de Maestría | |
dc.type.driver | info:eu-repo/semantics/masterThesis | |
dc.identifier.reponame | reponame:Repositorio Institucional Universidad de Medellín | spa |
dc.identifier.instname | instname:Universidad de Medellín | spa |
dc.description.degreename | Magíster en Administración MBA | |
dc.description.degreelevel | Maestría | |
dc.publisher.grantor | Universidad de Medellín |
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