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Fuzzy Non-Payment Risk Management Rooted in Optimized Household Consumption Units

Author

Listed:
  • Gregorio Izquierdo Llanes

    (Faculty of Economics and Business, National University of Distance Education (UNED), 28040 Madrid, Spain)

  • Antonio Salcedo

    (Associated Center Las Tablas, National University of Distance Education (UNED), 28040 Madrid, Spain)

Abstract

Traditionally, business risk management models have not taken into consideration household composition for the purposes of credit granting or project financing in order to manage the risk of default. In this research, an improvement in the risk management model was obtained by introducing household composition as a new exogenous variable. With the application of generalized reduced gradient nonlinear optimization modeling, improved consumption units are determined according to the different types of household size and the age of their members. Estimated household economies of scale show a consistent pattern even in the year 2020, corresponding with the COVID-19 outbreak. Thus, an adjusted estimation of the household equivalized disposable income is obtained. Based on this more accurate equivalized income estimation, acceptable debt levels can be determined. The estimation of probabilities of default allows the household risk of default to be managed. In this way, a novel model is proposed by incorporating household composition into credit risk evaluation using fuzzy clustering and optimization techniques. Companies can assess the expected loss of a credit exposure through a model that can help them in the process of making evidence-informed decisions.

Suggested Citation

  • Gregorio Izquierdo Llanes & Antonio Salcedo, 2025. "Fuzzy Non-Payment Risk Management Rooted in Optimized Household Consumption Units," Risks, MDPI, vol. 13(4), pages 1-13, April.
    • Handle: RePEc:gam:jrisks:v:13:y:2025:i:4:p:74-:d:1632425
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    References listed on IDEAS

    as
    1. Ivan Mauricio Bermudez Vera & Jaime Mosquera Restrepo & Diego Fernando Manotas-Duque, 2025. "Data Mining for the Adjustment of Credit Scoring Models in Solidarity Economy Entities: A Methodology for Addressing Class Imbalances," Risks, MDPI, vol. 13(2), pages 1-16, January.
    2. Besharov, Douglas J. & Couch, Kenneth A. (ed.), 2012. "Counting the Poor: New Thinking About European Poverty Measures and Lessons for the United States," OUP Catalogue, Oxford University Press, number 9780199860586, Decembrie.
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