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

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.