A Survey on Reinsurrance Contract Optimization Using Evolutionary and Swarm Computation

Risk transferring strategies are the primary concern of financial risk management. Insurer companies aim to protect themselves against potentially significant losses, usually, associated with natural catastrophes. The hedging is mostly facilitated by putting risk transfer contracts in the global reinsurance market, hence, hedging the risk to bigger reinsurance companies upon the payment of a premium in a similar way as occurs when clients want to protect their properties. Thus, the problem can be summarized as maximizing the hedged risk and, at the same time, maximizing the expected return. This issue is also known as Reinsurance Contract Optimization, in which given a set of parameters such as number of layers, limits, and deductibles, we aim to discover the percentage of shares or placements that maximize the risk and the expected return. Even though this problem is discrete, it cannot be solved by an enumeration algorithm because the search space can be large. In this context, evolutionary/swarm computation represents an interesting form of addressing it. Therefore, this paper presents how the reinsurance contract optimization has been tackled using evolutionary/swarm algorithms in both single and multiobjective approaches so far.