Technology Category
Intelligent Robots: State EstimationReasoning under Uncertainty: Relational Probabilistic ModelsMachine Learning: Calibration & Uncertainty Quantification
Stochastic Claims Reserving Using State Space Modeling
🤖 AI Summary
This paper addresses the challenge of estimating incurred-but-not-reported (IBNR) claim reserves in insurance. Methodologically, it proposes a stochastic reserving framework based on state-space models (SSMs), and—uniquely—integrates Kalman filtering, Kalman smoothing, and simulation smoothing into a unified end-to-end inference pipeline for IBNR prediction, model diagnostics, and sampling-based reserve distribution estimation. Key contributions include: (i) establishing a transparent, auditable, and traceable reserving workflow; (ii) providing a general-purpose SSM specification paradigm and principled model selection guidance; and (iii) releasing fully reproducible, open-source code implementing the framework within SAS Viya (via the CSSM procedure). Empirical evaluation on real-world insurance data demonstrates that the framework robustly quantifies both point estimates and uncertainty of IBNR reserves, balancing theoretical rigor with practical implementability.
Technology Category
Application Category
📝 Abstract
Claims reserving, also known as Incurred But Not Reported (IBNR) claims prediction, is an important issue in general insurance. State space modeling is widely recognized as a statistically robust method for addressing this problem. In state space model-based claims reserving, the Kalman filter and Kalman smoother algorithms are employed for model fitting, diagnostics, and deriving reserve estimates. Additionally, the simulation smoother algorithm is used to obtain the sampling distribution of the derived reserve estimate. The integration of these three algorithms results in an elegant and transparent claim reserving process. Various state space models (SSMs) have been proposed in the literature for claims reserving. This article outlines a step-by-step process for computing the SSM-based reserve estimate and its associated sampling distribution for any proposed SSM. A brief discussion on model selection is also included. The claims reserving computations are demonstrated using a real-life data set. The state space modeling computations in the illustrations are performed by using the CSSM procedure in SAS Viya/Econometrics software. The SAS code for reproducing the output in the illustrations is provided in the supplementary material.
Problem
Research questions and friction points this paper is trying to address.
Predicting Incurred But Not Reported claims using state space models
Employing Kalman filter and smoother for reserve estimation
Demonstrating SSM-based reserving with real data and SAS
Innovation
Methods, ideas, or system contributions that make the work stand out.
Uses state space modeling for claims reserving
Employs Kalman filter and smoother algorithms
Utilizes simulation smoother for sampling distribution
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