  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.
