IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v227y2013i2p358-366.html
   My bibliography  Save this article

Granting and managing loans for micro-entrepreneurs: New developments and practical experiences

Author

Listed:
  • Bravo, Cristián
  • Maldonado, Sebastián
  • Weber, Richard

Abstract

We present a methodology to grant and follow-up credits for micro-entrepreneurs. This segment of grantees is very relevant for many economies, especially in developing countries, but shows a behavior different to that of classical consumers where established credit scoring systems exist. Parts of our methodology follow a proven procedure we have applied successfully in several credit scoring projects. Other parts, such as cut-off point construction and model follow-up, however, had to be developed and constitute original contributions of the present paper. The results from two credit scoring projects we developed in Chile, one for a private bank and one for a governmental credit granting institution, provide interesting insights into micro-entrepreneurs’ repayment behavior which could also be interesting for the respective segment in countries with similar characteristics.

Suggested Citation

  • Bravo, Cristián & Maldonado, Sebastián & Weber, Richard, 2013. "Granting and managing loans for micro-entrepreneurs: New developments and practical experiences," European Journal of Operational Research, Elsevier, vol. 227(2), pages 358-366.
    • Handle: RePEc:eee:ejores:v:227:y:2013:i:2:p:358-366
    DOI: 10.1016/j.ejor.2012.10.040
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221712008053
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2012.10.040?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. White, Leroy & Smith, Honora & Currie, Christine, 2011. "OR in developing countries: A review," European Journal of Operational Research, Elsevier, vol. 208(1), pages 1-11, January.
    2. B Baesens & T Van Gestel & S Viaene & M Stepanova & J Suykens & J Vanthienen, 2003. "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 627-635, June.
    3. D. J. Hand & W. E. Henley, 1997. "Statistical Classification Methods in Consumer Credit Scoring: a Review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 523-541, September.
    4. Kim, Hong Sik & Sohn, So Young, 2010. "Support vector machines for default prediction of SMEs based on technology credit," European Journal of Operational Research, Elsevier, vol. 201(3), pages 838-846, March.
    5. Tong, Edward N.C. & Mues, Christophe & Thomas, Lyn C., 2012. "Mixture cure models in credit scoring: If and when borrowers default," European Journal of Operational Research, Elsevier, vol. 218(1), pages 132-139.
    6. Sohn, So Young & Kim, Hong Sik, 2007. "Random effects logistic regression model for default prediction of technology credit guarantee fund," European Journal of Operational Research, Elsevier, vol. 183(1), pages 472-478, November.
    7. Finlay, Steven, 2011. "Multiple classifier architectures and their application to credit risk assessment," European Journal of Operational Research, Elsevier, vol. 210(2), pages 368-378, April.
    8. Blochlinger, Andreas & Leippold, Markus, 2006. "Economic benefit of powerful credit scoring," Journal of Banking & Finance, Elsevier, vol. 30(3), pages 851-873, March.
    9. G Castermans & D Martens & T Van Gestel & B Hamers & B Baesens, 2010. "An overview and framework for PD backtesting and benchmarking," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 359-373, March.
    10. Setiono, Rudy & Baesens, Bart & Mues, Christophe, 2009. "A note on knowledge discovery using neural networks and its application to credit card screening," European Journal of Operational Research, Elsevier, vol. 192(1), pages 326-332, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Medina-Olivares, Victor & Calabrese, Raffaella & Dong, Yizhe & Shi, Baofeng, 2022. "Spatial dependence in microfinance credit default," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1071-1085.
    2. Xing, Jin & Chi, Guotai & Pan, Ancheng, 2024. "Instance-dependent misclassification cost-sensitive learning for default prediction," Research in International Business and Finance, Elsevier, vol. 69(C).
    3. Verbraken, Thomas & Bravo, Cristián & Weber, Richard & Baesens, Bart, 2014. "Development and application of consumer credit scoring models using profit-based classification measures," European Journal of Operational Research, Elsevier, vol. 238(2), pages 505-513.
    4. Carlos Serrano-Cinca & Begoña Gutiérrez-Nieto & Luz López-Palacios, 2015. "Determinants of Default in P2P Lending," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
    5. Calabrese, Raffaella, 2023. "Contagion effects of UK small business failures: A spatial hierarchical autoregressive model for binary data," European Journal of Operational Research, Elsevier, vol. 305(2), pages 989-997.
    6. Maldonado, Sebastián & Pérez, Juan & Bravo, Cristián, 2017. "Cost-based feature selection for Support Vector Machines: An application in credit scoring," European Journal of Operational Research, Elsevier, vol. 261(2), pages 656-665.
    7. Sun, Yue & Chai, Nana & Dong, Yizhe & Shi, Baofeng, 2022. "Assessing and predicting small industrial enterprises’ credit ratings: A fuzzy decision-making approach," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1158-1172.
    8. Stevenson, Matthew & Mues, Christophe & Bravo, Cristián, 2021. "The value of text for small business default prediction: A Deep Learning approach," European Journal of Operational Research, Elsevier, vol. 295(2), pages 758-771.
    9. Crittenden, Victoria L. & Crittenden, William F. & Ajjan, Haya, 2019. "Empowering women micro-entrepreneurs in emerging economies: The role of information communications technology," Journal of Business Research, Elsevier, vol. 98(C), pages 191-203.
    10. Luisa Roa & Alejandro Correa-Bahnsen & Gabriel Suarez & Fernando Cort'es-Tejada & Mar'ia A. Luque & Cristi'an Bravo, 2020. "Super-App Behavioral Patterns in Credit Risk Models: Financial, Statistical and Regulatory Implications," Papers 2005.14658, arXiv.org, revised Jan 2021.
    11. Óskarsdóttir, María & Bravo, Cristián, 2021. "Multilayer network analysis for improved credit risk prediction," Omega, Elsevier, vol. 105(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Adnan Dželihodžić & Dženana Đonko & Jasmin Kevrić, 2018. "Improved Credit Scoring Model Based on Bagging Neural Network," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(06), pages 1725-1741, November.
    2. Jiang, Cuiqing & Wang, Zhao & Zhao, Huimin, 2019. "A prediction-driven mixture cure model and its application in credit scoring," European Journal of Operational Research, Elsevier, vol. 277(1), pages 20-31.
    3. Huei-Wen Teng & Michael Lee, 2019. "Estimation Procedures of Using Five Alternative Machine Learning Methods for Predicting Credit Card Default," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 1-27, September.
    4. Dangxing Chen & Weicheng Ye & Jiahui Ye, 2022. "Interpretable Selective Learning in Credit Risk," Papers 2209.10127, arXiv.org.
    5. Richard Chamboko & Jorge M. Bravo, 2016. "On the modelling of prognosis from delinquency to normal performance on retail consumer loans," Risk Management, Palgrave Macmillan, vol. 18(4), pages 264-287, December.
    6. Juan Laborda & Seyong Ryoo, 2021. "Feature Selection in a Credit Scoring Model," Mathematics, MDPI, vol. 9(7), pages 1-22, March.
    7. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    8. Rais Ahmad Itoo & A. Selvarasu & José António Filipe, 2015. "Loan Products and Credit Scoring by Commercial Banks (India)," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 5(1), pages 851-851.
    9. Chen, Dangxing & Ye, Jiahui & Ye, Weicheng, 2023. "Interpretable selective learning in credit risk," Research in International Business and Finance, Elsevier, vol. 65(C).
    10. Lisa Crosato & Caterina Liberati & Marco Repetto, 2021. "Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default," Papers 2108.13914, arXiv.org, revised Sep 2021.
    11. Elena Ivona DUMITRESCU & Sullivan HUE & Christophe HURLIN & Sessi TOKPAVI, 2020. "Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds," LEO Working Papers / DR LEO 2839, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    12. José Willer Prado & Valderí Castro Alcântara & Francisval Melo Carvalho & Kelly Carvalho Vieira & Luiz Kennedy Cruz Machado & Dany Flávio Tonelli, 2016. "Multivariate analysis of credit risk and bankruptcy research data: a bibliometric study involving different knowledge fields (1968–2014)," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(3), pages 1007-1029, March.
    13. Rais Ahmad Itoo & A. Selvarasu, 2017. "Loan products and Credit Scoring Methods by Commercial Banks," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 7(1), pages 1297-1297.
    14. Dinh, Thi Huyen Thanh & Kleimeier, Stefanie, 2007. "A credit scoring model for Vietnam's retail banking market," International Review of Financial Analysis, Elsevier, vol. 16(5), pages 471-495.
    15. Agustin Pérez-Martín & Agustin Pérez-Torregrosa & Alejandro Rabasa & Marta Vaca, 2020. "Feature Selection to Optimize Credit Banking Risk Evaluation Decisions for the Example of Home Equity Loans," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
    16. Richard Chamboko & Jorge Miguel Bravo, 2020. "A Multi-State Approach to Modelling Intermediate Events and Multiple Mortgage Loan Outcomes," Risks, MDPI, vol. 8(2), pages 1-29, June.
    17. Akkoç, Soner, 2012. "An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish cred," European Journal of Operational Research, Elsevier, vol. 222(1), pages 168-178.
    18. Neuberg Richard & Hannah Lauren, 2017. "Loan pricing under estimation risk," Statistics & Risk Modeling, De Gruyter, vol. 34(1-2), pages 69-87, June.
    19. Weidong Guo & Zach Zhizhong Zhou, 2022. "A comparative study of combining tree‐based feature selection methods and classifiers in personal loan default prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1248-1313, September.
    20. Crone, Sven F. & Finlay, Steven, 2012. "Instance sampling in credit scoring: An empirical study of sample size and balancing," International Journal of Forecasting, Elsevier, vol. 28(1), pages 224-238.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:227:y:2013:i:2:p:358-366. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.