Leverage the power of AI / machine learning to uncover hidden profits in your portfolios.
Each renewal season, executive and underwriting teams across the reinsurance industry get together to determine what changes to make to their existing portfolios to achieve portfolio optimization and alignment with their overall goals and strategy. It can often take months to perform the manual and time-consuming feat of evaluating all of the options to balance risk vs reward possibilities and still not arrive at the optimal portfolio mix – until now!
Learn how Analyze Re machine-learning technology can be used to find improved portfolio returns.
The Analyze Re Portfolio Optimizer leverages AI/machine learning algorithms to automatically search through millions of potential portfolio combinations in a matter of minutes and returns a set of recommended portfolios known as the “pareto efficient frontier.”
These recommended hypothetical portfolios will improve returns and minimize risks in comparison to your initial, in-force portfolio.
Our primary goal is to aid decision making without removing any personal choice from the underwriter.
Within each of the recommended reinsurance portfolios the optimizer provides insights that reveal how to better allocate your share participations in each contract, so as long as you write an amount within the recommended range, you’ll be improving the profitability of your portfolio while also reducing risk!
Do you have objectives such as California Quake be no greater than 20% of your overall gross portfolio? Or perhaps you’ve set aggressive growth targets for the year and need to guarantee bringing in a higher amount of top-line revenue. Whatever your objectives are centered around—decreasing combined ratios, minimizing value at risk (VAR) or Tail Value at Risk (TVaR)—our portfolio optimizer gives you full control to set your underwriting guidelines and make decisions as you see fit.
Not all optimizers are equal, and our approach employs multi-objective optimization – not only can you enter multiple objectives and constraints, but our optimizer will consider all these criteria simultaneously.
The result is a better set of recommendations compared to optimizers that use linear solvers, which are forced to compromise and lose potential candidate portfolios.
Analyze Re technology supports full automation of the above process, so you can adapt to any change in the market and always stay one step ahead.
We begin the process by seeding data into the optimizer and displaying your initial portfolio (e.g., your in-force book using our real-time analytics to show you the starting metrics.
Next, we capture realistic objectives and constraints that the underwriter specifies and the optimizer will then go to work crunching the numbers and sifting through potential candidate portfolios for you to consider.
We then present the results back for review and allow for iterative changes to be made. This facilitates the fine tuning of the optimizer until we reach a point that produces a portfolio that can be underwritten. Our machine learning algorithms do the heavy lifting, but you retain full control of the decision-making.
Learn how one global reinsurer used Analyze Re’s portfolio optimizer to improve portfolio returns by over 10%.