Stella C. Dong, James R. Finlay
arXiv 11 Jan 2025 · Econometrics
arXiv:2501.06404 · PDF · DOI · OpenAlex · Extracted main text
Reinsurance optimization is critical for insurers to manage risk exposure, ensure financial stability, and maintain solvency. Traditional approaches often struggle with dynamic claim distributions, high-dimensional constraints, and evolving market conditions. This paper introduces a novel hybrid framework that integrates {Generative Models}, specifically Variational Autoencoders (VAEs), with {Reinforcement Learning (RL)} using Proximal Policy Optimization (PPO). The framework enables dynamic and scalable optimization of reinsurance strategies by combining the generative modeling of complex claim distributions with the adaptive decision-making capabilities of reinforcement learning. The VAE component generates synthetic claims, including rare and catastrophic events, addressing data scarcity and variability, while the PPO algorithm dynamically adjusts reinsurance parameters to maximize surplus and minimize ruin probability. The framework's performance is validated through extensive experiments, including out-of-sample testing, stress-testing scenarios (e.g., pandemic impacts, catastrophic events), and scalability analysis across portfolio sizes. Results demonstrate its superior adaptability, scalability, and robustness compared to traditional optimization techniques, achieving higher final surpluses and computational efficiency. Key contributions include the development of a hybrid approach for high-dimensional optimization, dynamic reinsurance parameterization, and validation against stochastic claim distributions. The proposed framework offers a transformative solution for modern reinsurance challenges, with potential applications in multi-line insurance operations, catastrophe modeling, and risk-sharing strategy design.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | McNeil, Alexander J. and Frey, Rüdiger and Embrechts, Paul (2015) Quantitative Risk Management: Concepts, Techniques and Tools | 1.000 | 12 | 4 | 100% |
| 2 | Hans Buehler and Lukas Gonon and Josef Teichmann and Ben Wood (2019) Deep Hedging | 1.000 | 11 | 3 | 100% |
| 3 | Asmussen, Søren and Albrecher, Hans (2010) Ruin Probabilities | 1.000 | 9 | 4 | 100% |
| 4 | Embrechts, P. and Klüppelberg, C. and Mikosch, T (2014) Quantitative Risk Management: Concepts, Techniques and Tools | 1.000 | 9 | 4 | 100% |
| 5 | Mario V. Wüthrich (2020) Machine Learning in Individual Claims Reserving | 1.000 | 9 | 3 | 100% |
| 6 | J. David Cummins and Mary A. Weiss (2008) Convergence of Insurance and Financial Markets: Hybrid and Securitized Risk-Transfer Solutions | 1.000 | 8 | 3 | 100% |
| 7 | Arne Sandström (2010) Handbook of Solvency for Actuaries and Risk Managers: Theory and Practice | 1.000 | 7 | 3 | 100% |
| 8 | Chris D. Daykin and Teivo Pentikäinen and Martti Pesonen (1994) Practical Risk Theory for Actuaries | 1.000 | 6 | 3 | 100% |
| 9 | Embrechts, Paul and Klüppelberg, Claudia and Mikosch, Thomas (1997) Modelling Extremal Events for Insurance and Finance | 1.000 | 6 | 3 | 100% |
| 10 | Embrechts, Paul and Klüppelberg, Claudia and Mikosch, Thomas (2013) Modelling Extremal Events for Insurance and Finance | 1.000 | 5 | 3 | 100% |
Showing the top 10 of 64 scored citations.