["Review of Development Economics, EarlyView. ", "\nABSTRACT\nMeasuring inequality of financial opportunity is important for understanding disparities in financial resource allocation and their fairness implications. This study employs machine learning techniques to deliver a data‐driven assessment of financial opportunity inequality in China. The findings show that, based on conditional inference forest estimates, the inequality of opportunity is 0.359 in financial breadth, 0.478 in core financial depth, and 0.623 in comprehensive financial depth. In addition, FIOP follows a life‐cycle pattern, peaking at middle age and declining in later life. Opportunity tree analysis identifies mother's education, father's education, and household registration type as the primary factors for financial breadth, core financial depth, and comprehensive financial depth, respectively. Specifically, individuals with lower parental education levels and agricultural household registration face the greatest barriers to accessing financial resources.\n"]