A Unified Micro-Model for Loss Reserves, IBNR and Unearned Premium Risk with Dependence, Inflation, and Discounting

TL;DR

This paper presents a comprehensive micro-level stochastic framework for joint modeling of loss reserves, IBNR, and unearned premium risk, incorporating dependence, inflation, and discounting to improve risk assessment and management.

Contribution

It introduces a unified micro-level reserving architecture with flexible dependence structures, enabling more accurate and dynamic risk measurement for insurance liabilities.

Findings

Framework produces forward-looking reserve and premium risk measures.

Closed-form moments and distribution approximations derived.

Case study demonstrates practical relevance and calibration on real data.

Abstract

This paper introduces a unified micro-level stochastic framework for the joint modeling of loss reserves (RBNS), incurred but not reported (IBNR) reserves, and unearned premium risk under dependence, inflation, and discounting. The proposed framework accommodates interactions between indemnities, expenses, reporting delays, and settlement delays, while allowing for flexible parametric dependence structures and dynamic financial adjustments. An Aggregate Trend Renewal Process (ATRP) is used as one possible implementation of the joint model for payments, expenses, and delays; however, the methodological contribution of the paper lies in the unified micro-level reserving architecture rather than in the ATRP itself. The framework produces forward-looking reserve and premium risk measures with direct applications to pricing, reserving, and capital management. We implement the framework using an aggregate trend renewal process at the individual claim level, which can be applied to the usual run-off triangle to obtain predictions for each accident-development year. Closed-form expressions for the first two raw and joint conditional moments of predicted payments are derived, together with approximations of their distribution functions. A detailed case study on medical malpractice insurance illustrates the practical relevance of the approach and its calibration on real-world data. We also investigate data heterogeneity, parameter uncertainty, distributional approximations, premium risk, UPR sensitivity to operational delays and inflation, and risk capital implications under alternative assumptions. The results highlight the advantages of unified micro-level modeling for dynamic liability and premium risk assessment in long-tailed lines of business.