$${{\texttt {openIRM}}}$$: publicly accessible internal risk model of an artificial life insurer for analyzing and benchmarking actuarial methods in the solvency II setting

Abstract We introduce $${{\texttt {openIRM}}}$$ , the Internal Risk Model of an artificial life insurer, designed to allow an easy benchmarking of nested simulation techniques for Solvency Capital Requirement ( $${{\textrm{SCR}}}$$ ) estimation under Solvency II and other actuarial methods. $${{\texttt {openIRM}}}$$ integrates an economic scenario generator and a cash flow projection model, enabling the computation of the available capital (basic own funds) through both the direct and indirect method. Leveraging a two-factor Gaussian model for stochastic short rates and a generalized Black-Scholes model for stock dynamics, the framework supports policyholder investments via guaranteed minimum-income benefit contracts. We extend the asset-liability management model by Diehl et al. (EAJ 13(1), 2022), and prove the theoretical convergence of the direct and indirect method under appropriate assumptions. Calibrated using interest rate caps from 2016 to the end of 2023, $${{\texttt {openIRM}}}$$ allows estimation of available capital distributions and $${{\,\textrm{SCR}\,}}$$ dynamics for each trading day in that range. The source code of $${{\texttt {openIRM}}}$$ written in is publicly available on at https://gitlab.cc-asp.fraunhofer.de/itwm-fm-lv-public/openirm . We also provide standalone executables that, after installation, can be accessed via the command line interface or with the provided wrappers in , and .

Authors

Institutions

Publication Details

Journal
European Actuarial Journal
Published
2025-11-12
DOI
https://doi.org/10.1007/s13385-025-00435-6
Primary Topic
Insurance, Mortality, Demography, Risk Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

$${{\texttt {openIRM}}}$$: publicly accessible internal risk model of an artificial life insurer for analyzing and benchmarking actuarial methods in the solvency II setting

Maximilian Diehl, Mark-Oliver Wolf, Philipp Mahler, Benedict Nikolaus Meyer
European Actuarial Journal
Insurance, Mortality, Demography, Risk Management
article

$${{\texttt {openIRM}}}$$: publicly accessible internal risk model of an artificial life insurer for analyzing and benchmarking actuarial methods in the solvency II setting

Maximilian Diehl, Mark-Oliver Wolf, Philipp Mahler, Benedict Nikolaus Meyer
article en

Abstract

Abstract We introduce $${{\texttt {openIRM}}}$$ , the Internal Risk Model of an artificial life insurer, designed to allow an easy benchmarking of nested simulation techniques for Solvency Capital Requirement ( $${{\textrm{SCR}}}$$ ) estimation under Solvency II and other actuarial methods. $${{\texttt {openIRM}}}$$ integrates an economic scenario generator and a cash flow projection model, enabling the computation of the available capital (basic own funds) through both the direct and indirect method. Leveraging a two-factor Gaussian model for stochastic short rates and a generalized Black-Scholes model for stock dynamics, the framework supports policyholder investments via guaranteed minimum-income benefit contracts. We extend the asset-liability management model by Diehl et al. (EAJ 13(1), 2022), and prove the theoretical convergence of the direct and indirect method under appropriate assumptions. Calibrated using interest rate caps from 2016 to the end of 2023, $${{\texttt {openIRM}}}$$ allows estimation of available capital distributions and $${{\,\textrm{SCR}\,}}$$ dynamics for each trading day in that range. The source code of $${{\texttt {openIRM}}}$$ written in is publicly available on at https://gitlab.cc-asp.fraunhofer.de/itwm-fm-lv-public/openirm . We also provide standalone executables that, after installation, can be accessed via the command line interface or with the provided wrappers in , and .

European Actuarial Journal
Fraunhofer Institute for Industrial Mathematics (DE)
Openalex Percentile: Top 43%
Insurance, Mortality, Demography, Risk Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.