We introduce a new approach to improve the performance of rating prediction models for multinational corporations. In this segment, the low number of defaults poses a challenge, as it prevents rating models to be constructed for individual industry sectors or regions. We show that reducing group-level heterogeneity in financial ratios results in a rating prediction model with better performance than both unadjusted models and models adjusted by including industry dummies or other simpler procedures. Our approach fills a gap in cases where a limited dataset does not permit the construction of separate models for individual industries or regions.