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Dynamic optimization of multi-layered reinsurance treaties

Published: 13 April 2015 Publication History

Abstract

Risk hedging strategies are at the heart of financial risk management. As with many financial institutions, insurance companies try to hedge their risk against potentially large losses, such as those associated with natural catastrophes. Much of this hedging is facilitated by engaging in risk transfer contracts with the global reinsurance market. Devising an effective hedging strategy depends on careful data analysis and optimization. In this paper, we study from the perspective of an insurance company the Dynamic Reinsurance Optimization problem in which given a set of expected loss distributions (the result of running a Catastrophic Loss Model), a model of reinsurance market costs, and some general financial terms, our task is to evolve a set of complex multi-layered reinsurance contracts that define a Pareto frontier quantifying the best available tradeoffs between expected risk and returns for the insurer. Our approach to this reinsurance contract optimization problem is three fold. Firstly, we apply the Strength Pareto Evolutionary Algorithm 2 (SPEA2) meta-heuristic to guide the multi-objective search process. Secondly, we exploit equation reordering to minimize computation, aggressively pre-computation/caching methods, and discretization to efficiently evaluate individual solutions. Lastly, we apply High Performance Computing (HPC) techniques including shared memory parallelization, vectorization and data prefetching to accelerate the search process. As a result, our prototype Dynamic Reinsurance Optimizer is able to solve industrial sized problems on a single multi-core server in about 2 minutes for 7 layers and 4 minutes for 15 layers per run.

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Cited By

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  • (2016)Enhanced multiobjective population-based incremental learning with applications in risk treaty optimizationEvolutionary Intelligence10.1007/s12065-016-0147-09:4(153-165)Online publication date: 24-Oct-2016
  • (2016)Enhanced Multiobjective Population-Based Incremental Learning with Applications in Risk Treaty OptimizationApplications of Evolutionary Computation10.1007/978-3-319-31204-0_1(3-18)Online publication date: 15-Mar-2016
  • (2015)A new vector evaluated PBIL algorithm for reinsurance analytics2015 Latin America Congress on Computational Intelligence (LA-CCI)10.1109/LA-CCI.2015.7435960(1-6)Online publication date: Oct-2015

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cover image ACM Conferences
SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing
April 2015
2418 pages
ISBN:9781450331968
DOI:10.1145/2695664
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 13 April 2015

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Author Tags

  1. SPEA2
  2. dynamic reinsurance contract optimization
  3. high performance computing
  4. risk management
  5. treaty

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SAC 2015: Symposium on Applied Computing
April 13 - 17, 2015
Salamanca, Spain

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SAC '15 Paper Acceptance Rate 291 of 1,211 submissions, 24%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

View all
  • (2016)Enhanced multiobjective population-based incremental learning with applications in risk treaty optimizationEvolutionary Intelligence10.1007/s12065-016-0147-09:4(153-165)Online publication date: 24-Oct-2016
  • (2016)Enhanced Multiobjective Population-Based Incremental Learning with Applications in Risk Treaty OptimizationApplications of Evolutionary Computation10.1007/978-3-319-31204-0_1(3-18)Online publication date: 15-Mar-2016
  • (2015)A new vector evaluated PBIL algorithm for reinsurance analytics2015 Latin America Congress on Computational Intelligence (LA-CCI)10.1109/LA-CCI.2015.7435960(1-6)Online publication date: Oct-2015

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