Financial fraud detection across institutions faces a fundamental tension between the need for diverse training data and regulatory prohibitions on sharing sensitive records. Existing federated learning approaches suffer from performance degradation under non-IID distributions and substantial utility losses when uniform differential privacy is applied to inherently sparse fraud signals. To this end, this paper proposes HiFraud, a hierarchical federated framework featuring three key components: fraud-aware dynamic clustering with complementarity regularization to group institutions by fraud pattern similarity while preserving rare-type representation; star-chain knowledge transfer augmented by not-true-class distillation to propagate novel fraud patterns rapidly within clusters while mitigating catastrophic forgetting; and privacy-adaptive aggregation via Rényi differential privacy composition, calibrating noise intensity to distributional divergence and fraud rarity. Experiments on IEEE-CIS, PaySim, and Worldline datasets show that HiFraud achieves an area under the receiver operating characteristic curve (AUC-ROC) of 0.935 under $\mathrm{\epsilon }=2.3$, outperforming DP-FedAvg by 10.5% while reducing convergence from 49 to 30 rounds. The framework also suppresses membership inference attack success to 10.2%, detects emerging fraud patterns within 3 h inside clusters, and improves rare fraud type detection by 23.0% over uniform privacy baselines. These results demonstrate that hierarchical architectures can effectively reconcile detection performance, formal privacy guarantees, and rapid threat response in collaborative fraud detection.