<![CDATA[This study develops and evaluates a hierarchical ensemble actuarial approach for motor claim reserving under Indonesia’s PSAKBI framework. The method integrates traditional actuarial techniques with modern machine learning models in a structured ensemble design to enhance predictive accuracy, reliability, and transparency. Using motor insurance claim data, the ensemble was compared against conventional single-model reserving practices. Results show that the proposed approach achieves lower prediction error (MSE = 220.3), accurate calibration (94.7% coverage), and more stable reserve estimates across accident years. Beyond statistical performance, the design emphasizes interpretability by tracing predictions to weighted contributions of base models, thereby avoiding black-box behavior. These findings highlight the practical relevance of hybrid ensemble reserving in regulated environments, offering a transparent and robust solution aligned with PSAKBI requirements. The study contributes to the literature by demonstrating how hybrid actuarial ensembles can balance methodological rigor, machine learning flexibility, and regulatory compliance in insurance reserving.]]>
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