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Assessment of a credit scoring system for popular bank savings and credit

Author

Listed:
  • José Francisco Martínez Sánchez

    (Universidad Autónoma del Estado de Hidalgo, México)

  • Gilberto Pérez Lechuga

    (Universidad Autónoma del Estado de Hidalgo, México)

Abstract

The current banking system does not meet the needs of financial services, particularly credit to the poorest sectors of society, the banking presence are mainly located in cities and regions with important economic activity, to attend to these excluded sectors have created naturally without supervision and support of the authorities, financial institutions such as credit unions, cooperatives, popular financial companies, among others, called savings banks and loan altogether. However, most of these institutions are not recognized or supervised by the CNBV, which translates into risk for users of services to financial institutions, highlighting the inefficiencies in their lending processes, and decisions to accept or reject a credit application is based on knowledge, experience and judgment of the loan officer. This paper presents the evaluation of a credit scoring system1in terms of cost-efficiency for savings and loan institutions in specific for SOFIPO’s and in terms of cost–benefit to the service provider assessment of loan applications. As will be shown in the development of a system work of this nature stream lines the lending process at minimal cost and is a worthwhile investment for the provider of credit scoring services.

Suggested Citation

  • José Francisco Martínez Sánchez & Gilberto Pérez Lechuga, 2016. "Assessment of a credit scoring system for popular bank savings and credit," Contaduría y Administración, Accounting and Management, vol. 61(2), pages 391-417, Abril-Jun.
    • Handle: RePEc:nax:conyad:v:61:y:2016:i:2:p:391-417
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    Citations

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    Cited by:

    1. Ravindra HEWA KURUPPUGE, & Sisira Kumara NARADDA GAMAGE, & Alexandru Mircea Nedelea, 2017. "Credit Cards As A Determinant Of Social Capital: A Study Of Rational Behaviour Of Urbanised Consumers," EcoForum, "Stefan cel Mare" University of Suceava, Romania, Faculty of Economics and Public Administration - Economy, Business Administration and Tourism Department., vol. 6(1), pages 1-34, January.
    2. Hamid Reza Mohammadi Ojan & Mohammad Karimi & Ebrahim Kardgar & Mehdi Ahrari, 2018. "The Extraction of Influencing Indicators for Scoring of Insurance Companies Branches Based on GMDH Neural Network," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 22(2), pages 527-556, Spring.
    3. Mao, Junxuan & Zhu, Qianyu & Wachenheim, Cheryl J. & Hanson, Erik D., 2020. "A Credit Scoring Model for Farmer Lending Decisions in Rural China," International Journal of Agricultural Management, Institute of Agricultural Management, vol. 8(4), August.
    4. Anil Kumar & Suneel Sharma & Mehregan Mahdavi, 2021. "Machine Learning (ML) Technologies for Digital Credit Scoring in Rural Finance: A Literature Review," Risks, MDPI, vol. 9(11), pages 1-15, October.
    5. Malik, Arsalan Haneef & Rehman, Awais Ur & Khan, Mubashir Ali, 2020. "Disclosures relating to Covid-19 in the Malaysian banking industry: Theory and Practice," MPRA Paper 109224, University Library of Munich, Germany.

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