This study examines the relationships among personality traits, ethical behavior, and ethical decision-making, comparing three high-stakes professions: accounting/auditing, medicine, and the judiciary. Using stratified random sampling, data were collected from 595 professionals in Iran (210 accountants and auditors, 220 physicians, and 165 judges) through the 60-item NEO-FFI, profession-specific ethical-scenario questionnaires developed on the basis of Rest's four-component model and Jones's moral-intensity model, and short scales measuring Rest's components and Jones's dimensions. Content validity (CVR/CVI) and reliability (Cronbach's alpha = 0.775-0.889) of the instruments were confirmed. Data were analyzed in SPSS using multiple regression, the Sobel test with bootstrap mediation analysis, hierarchical moderated regression, multi-group confirmatory factor analysis, and formal tests of coefficient equality across groups. Results show that conscientiousness, agreeableness, and openness positively, and neuroticism negatively, predict ethical behavior (R² = 0.253). Ethical behavior is the strongest predictor of ethical decision-making alongside conscientiousness (R² = 0.281) and partially mediates the effects of neuroticism, agreeableness, and conscientiousness. Moral sensitivity, moral judgment, and moral action (Rest), as well as temporal immediacy, magnitude of consequences, and probability of effect (Jones), significantly strengthen the behavior-decision link. Welch's robust tests with Games-Howell comparisons showed that physicians score significantly lower than accountants and judges on ethical behavior, ethical decision-making, and conscientiousness, with judges highest on ethical decision-making; nevertheless, measurement invariance of the NEO-FFI was established and formal multi-group tests indicated that the overall pattern of explanatory relationships is homogeneous across the three professions. The findings support designing shared, profession-sensitive interventions for strengthening professional ethics.Findings from previous studies indicate that intelligent systems can reduce human errors and improve the quality of financial decisions. Moreover, these technologies enable the processing of vast amounts of financial data within a short period, facilitating the identification of hidden patterns and trends that are often difficult to detect through traditional methods. Furthermore, the adoption of AI contributes to greater financial transparency, improved risk management, and enhanced organizational competitiveness in dynamic and complex business environments.