The top management team, comprising directors, supervisors, and executives (DSEs), is the primary group responsible for corporate financial misconduct. By applying knowledge graph embedding (KGE) to DSE profiles, this paper constructs a novel fraud propensity indicator (PIFT) that captures semantic similarity across three dimensions(personal) basic traits, governance structure, and cross tiesfrom a holistic perspective. Empirical tests using Logit and binary Probit models reveal that Higher PIFTs are associated with a greater likelihood of financial violations, and that, compared to singledimension indicators, PIFT demonstrates superior predictive power. Further analyses show that nonindependent directors play a dominant role in driving PIFT’s effectiveness, that the proportion of fraudulent firms in the same industry strengthens PIFT’s impact, and that higher regional legal enforcement enhances the link between PIFT and fraud risk, while state ownership remains insignificant. This study establishes a new starting point for applying knowledge graph embedding in corporate finance research, following the standard empirical financial research paradigm.