Optimizing reinsurance portfolio

Summary

Reinsurance is critical against natural disaster risks that have increased in recent years. We have used an annealing machine to formulate a reinsurance portfolio utilizing the vast amount of data collected by insurance companies.

What we want to solve

The average consumer in day-to-day life carries various types of insurance for life, health, and property. Those insurance policies vary in target, coverage, and the amount of coverage, as well as the location of the property.

Insurance is based on mutual aid. The money paid in as premiums when purchasing insurance is used to pay for insurance benefits to those who need help. When you are in need of help from the insurance program, it is paid out of the money paid by others. Insurance companies are responsible for maintaining this insurance structure.

The occurrence of natural disasters or other catastrophes results in huge claims payments. It is thus difficult for a single insurance company to underwrite all the risks. For this reason, there is a reinsurance mechanism whereby other insurance companies underwrite a portion of the risks assumed by the customers. The reinsurance mechanism allows insurance companies to diversify risks in a well-balanced manner while maintaining coverage, amount of coverage, etc. Insurance companies are contributing to more robust social infrastructure by utilizing such a network of mutual support for each other's financial base.

For example, in the Great East Japan Earthquake, earthquake insurance for households amounted to more than 1.2 trillion yen, and more than 60% of this amount was paid from reinsurance.

The recent increase in severity and frequency of disasters and the increasing sophistication of the economy enormously expand the potential compensation amounts. Reinsurance thus has become an indispensable mechanism for addressing these issues.

The purpose of risk diversification in reinsurance is to increase underwriting capacity. Thus, reinsurance makes it possible to underwrite large risks that would be difficult for an insurance company to underwrite on its own due to its financial strength and other factors. By using reinsurance to diversify risks optimally, insurance companies can provide coverage that meets the needs of their customers.

In order to maximize the use of reinsurance, it is necessary to formulate a portfolio based on a variety of considerations. However, there are a vast number of factors to be considered, such as the attributes of the subject and the underwriting needs of the reinsurance market, and conventional calculation techniques cannot keep up when trying to solve optimization calculations while taking all of these factors into account.

Therefore, in practice, the optimization process is divided into separate areas to complete the process in a realistic amount of time, but it still takes a considerable amount of time.

In addition, each time the items to be considered change (e.g., changes in disaster risk), optimization calculations that require a significant amount of time are necessary again. So there is a need for greater efficiency through technological innovation.

Current Issues

  • The limited number of insurance policies that can be considered.
  • The inability to fully consider the overall relationship due to the calculations being dividing by area.
  • Different results caused by different division data methods.
  • Calculations take too long even with data division being used.

Toward a Solution

We will formulate a reinsurance portfolio that minimizes the risk assumed by the insurance company while providing coverage that meets customer needs by utilizing data held by insurance companies (e.g., probability of disaster, the extent of damage, etc.). Specifically, we will optimize the combination of in-house/diversified ratios for each type of insurance and subject items.

Information necessary for optimization calculation

  • Insurance premium income
    Expected insurance income according to given conditions (coverage, geography, etc.)
  • Disaster Risk
    Type, magnitude, area, extent, probability of occurrence, timing and interval of expected disasters

Objective of optimization

To minimize the risks assumed by insurance companies while providing coverage that meets customer needs.

Interactions to consider

  • In-house/diversified ratio of compensation amount
    Considering the total amount of compensation paid by the company and distributed among the companies does not exceed a certain amount.
  • Disaster Risk
    Considering risks at the target institution are not biased toward a specific regions or disasters.

Changes after introduction

The entire portfolio of tens of thousands of insurance policies can now be developed in a few days; in contrast, the optimization calculations themselves could have taken several years to perform.

The dramatic reduction in calculation time has made faster recalculations possible according to the situation, allowing for quicker response to new risks.

Since we can handle larger-scale problems, the vast amount of data held by insurance companies can now be leveraged to optimize their portfolios for more significant risks in the medium to long term.

Related Links

Sompo Japan Begins Practical Use of Pseudo-Quantum Computer in Insurance Underwriting Operations : Hitachi News Release (hitachi.co.jp)

Sompo Japan Insurance Inc. has agreed to begin practical use of CMOS annealing, which simulates a quantum computer developed by Hitachi, in its non-life insurance operations.

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