Multiple objective metaheuristics for feature selection based on stakeholder requirements in credit scoring

Alternative data is increasingly utilized for credit evaluation of financially excluded persons. However, requirements, such as reliability, which gain new importance when alternative data is employed for credit evaluation, have not been considered as part of the credit scoring process. This research proposes an approach for incorporating context-specific stakeholder requirements in the credit scoring process. Two hybrid heuristics are proposed for a feature selection process that simultaneously optimizes all requirements. The first is a multiple objective, non-dominated sorting, binary Grasshopper Optimization Algorithm. The second incorporates the selection, crossover, and mutation techniques of genetic algorithms for greater diversity. Both algorithms are fitted with objective functions obtained from stakeholder requirements for multiple objective feature selection. Empirical evaluation is conducted with stakeholder requirements and alternative data features collected from mobile, public geospatial, and satellite data sources. Their performance is compared against several existing algorithms, and they offer improved performance on specific metrics. The first algorithm outperforms the existing many-objective non dominated sorting genetic algorithm, NSGA-III, in terms of computational time, convergence, and spacing. Meanwhile, the second method results in greater spread for the same population size but has a lengthy computational time. Thus, stakeholder requirements are successfully incorporated into the feature selection process. This results in a better balance between objectives. These findings extend the research on hybrid metaheuristics for feature selection, as well as alternative data for credit scoring.