The reporting delay in data breach incidents poses a formidable challenge for Incurred But Not Reported (IBNR) studies, complicating reserve estimation for actuarial professionals. This work presents a novel Bayesian nowcasting model designed to accurately model and predict the number of IBNR data breach incidents. Leveraging a Bayesian modeling framework, the model integrates time and heterogeneous effects to enhance predictive accuracy. Synthetic and empirical studies demonstrate the superior performance of the proposed model, highlighting its efficacy in addressing the complexities of IBNR estimation. Furthermore, we examine reserve estimation for IBNR incidents using the proposed model, shedding light on its implications for actuarial practice.

翻译：数据泄露事件的报告延迟给已发生未报告研究带来了严峻挑战，使精算专业人员的准备金估算复杂化。本研究提出了一种新颖的贝叶斯临近预报模型，旨在精确建模和预测已发生未报告数据泄露事件的数量。该模型利用贝叶斯建模框架，整合时间与异质性效应以提升预测精度。合成数据与实证研究证明了所提模型的优越性能，凸显了其在处理已发生未报告估算复杂性方面的有效性。此外，我们使用该模型对已发生未报告事件进行了准备金估算研究，揭示了其对精算实践的重要意义。