Bayesian Nowcasting Data Breach IBNR Incidents

TL;DR

This paper introduces a Bayesian nowcasting model that improves the prediction and estimation of IBNR data breach incidents, addressing reporting delays and enhancing actuarial reserve calculations.

Contribution

The paper presents a novel Bayesian framework that effectively models reporting delays and heterogeneous effects for IBNR incident prediction.

Findings

Model outperforms existing methods in synthetic and empirical tests.

Enhanced accuracy in reserve estimation for IBNR incidents.

Addresses complexities of reporting delays in breach data.

Abstract

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.