How much is 1% A/E improvement worth to you?

For deferred annuities, minimizing hedge breakage is a key risk management objective.  Here is a simple example showing how a seemingly modest 1% improvement in actual-to-expected ratios can dramatically reduce hedge breakage, even for small- to medium-sized blocks.  How to do it?  By expanding on your own company's experience data to use relevant industry data and credibility theory to improve policyholder behavior models.

This is what we do.  Our work is not an expense, it is an investment in risk management with quantifiable benefits.  Let's discuss exactly how this can work for you.

Contact:

Timothy Paris

timothyparis@ruark.co

860.866.7786


Case study - modeling FIA GLIB income commencement

Download the case study here:  Ruark - case study - FIA income commencement using credibility theory and PA

Quantifying the benefits of using your company data, industry data, and credibility theory in a predictive analytics context.  This case study is focused on FIA GLIB income commencement but the approach works similarly well for other products, riders, and policyholder behaviors.  Our experience is that the financial benefits can be 1000x greater than the costs.  Let’s discuss exactly how this can work for you.

Contact:

Timothy Paris

timothyparis@ruark.co

860.866.7786


New VA and FIA mortality tables, splits for benefit type and durational anti-selection

I am very pleased to announce that we have released new industry mortality tables for variable annuity (VA) and fixed indexed annuity (FIA) products. Building on the industry studies and tables that we have produced since 2007, the new tables are derived from our 2018 studies of VA and FIA mortality and are an expansion of this work for specific VA rider types and for FIA. They include a table for VA contracts with lifetime withdrawal benefits (“RVAM-LB”); a table for VA contracts without living benefits (“RVAM-DB”); and a table for FIA (“RFIAM”) in aggregate. All are single-life mortality tables.

• The RVAM-LB table incorporates 34 million exposure years and 320,000 deaths on VA contracts with guaranteed lifetime withdrawal benefits (GLWB) or hybrid GMIB. The table is calibrated to experience in contract durations 3 and later, with select factors for the earlier durations. This reflects key findings from our 2018 study - GLWB and hybrid GMIB mortality is lower than average at issue and rises to an ultimate level over time.

• The RVAM-DB table incorporates 29 million exposure years and 523,000 deaths on VA contracts without living benefits. The table is a select-and-ultimate table with a 5-year select period. This reflects key findings from our 2018 study - VA contracts without living benefits, primarily with death benefit (DB) only, have higher mortality than average at issue and the magnitude of anti-selection varies by issue age.

• The RFIAM table incorporates 16 million exposure years and 265,000 deaths on FIA contracts, both with and without lifetime income riders. Similar to RVAM-LB, the RFIAM table is calibrated to experience in contract durations 3 and later, with select factors for the earlier durations reflecting lower mortality consistent with findings from our 2018 study.

These new tables are purpose-built for deferred annuities, and are demonstrably better than standard industry tables for VA and FIA valuation -- they reflect not only the effects of age and gender, but also differences by product type and contract duration which are important components of mortality anti-selection.

We are making the new tables immediately available, free of charge, to clients who have already purchased our respective 2018 VA and FIA mortality studies. New purchasers of the these studies will also receive the tables.

Detailed study results, including company-level analytics, benchmarking, and customized behavioral assumption models calibrated to the study data, are available for purchase by participating companies.

Please contact us if you would like to learn more.


EBIG Conference: using predictive analytics to model annuity policyholder behavior

Here is our presentation from session 2B of the EBIG Conference in November 2019.

It includes an exploration of drivers, cohorts, and dynamics for policyholder behavior for VA and FIA based on industry experience data, including changes in recent years and the emergence of long-term data in key areas.

Moreover, we discuss critical elements to developing a sustainable and coherent framework to translate this complex experience data into assumption models.


Ruark 2019 top news

  • How much is 1% A/E improvement worth to you? As you can see, we estimate that it can be hundreds of millions or even billions, but we recommend that you do the math for yourself. And yes, this 1%+ is what we can typically do using the data from our VA and FIA industry studies of policyholder behavior (over $1 trillion current account value) in a credibility-based predictive analytics framework. If you are aware of a better cost-benefit anywhere, we'd love to hear about it! We're trying to make it as easy as possible for you to fend off the budget hawks.

 

  • VM-21 exposure draft looks set to raise the bar on policyholder behavior data analysis and modeling assumptions. Fortunately, we've got you covered there too.

 

  • 2019 FIA and VA studies are still available for purchase, along with our most recent (triennial) 2018 mortality studies, if you have not done so already.

Other goodies --

  • Data gathering is in flight for 2020 FIA studies and we will turn to VA data after year-end. And we are working to bring some new data contributors aboard too. If you're not in yet, let's fix that.

 

  • As previously mentioned, we also plan to gather data in H1 2020 for a first ever GMIB post-annuitization mortality study. Longevity anti-selection? Comparisons to GLWBs or SPIAs? We want to know too, and the data is now emerging.

 

  • Looking ahead to 2020, our plans are here.  The industry studies feed into our more customized engagements which range from assumption review to development of predictive models and related assumptions to complete process management.

 

 

Please contact me if you would like to discuss.

 


Ruark: delighted to assist with the SOA Pri-2012 Private Retirement Plans Mortality Tables

While we still love our individual annuity work, we were delighted to assist the SOA in the compilation and processing of pension mortality data for the Pri-2012 mortality tables.

 


Policyholder Behavior is the focus at this SOA seminar

We will have a lot to do there -- 🔥case study on Fixed Indexed Annuity income utilization using credibility theory in a predictive analytics context, a review of policyholder behavior experience data findings across the industry including some newly emerging data and interrelationships in key areas, and much more.

Hope to see you there!  Bridging the Gap seminar on Nov 10, which leads into the Equity-Based Insurance Guarantees Conference Nov 11-12.


Unlocking -- feel like you've done this before?

My fellow actuaries, I think there is a better way.  Let's discuss "How to Get Real Results in Policyholder Behavior Modeling" at session 134 of the SOA Annual Meeting.


Join me at this ACTEX webinar - Data Analysis and Modeling for Long-Term Products

https://actexmadriver.com/orderselection.aspx?id=453145085

In this webinar, we will explore the difficulties of analyzing experience data for long-term products that are early in their lifecycle and translating this data to assumption models. These issues are endemic to any new product line and are evident across many large and important segments of the life and annuity landscape, including post-level term mortality and variable and fixed indexed annuity lifetime income guarantees. As a case study, we will utilize actual industry-level policyholder experience data from the US annuity market to explore the key drivers, interrelationships, and market segments. Following this, we will put ourselves in the position of an actuary working for a company in this market and analyze a company's experience data. Then we will develop and calibrate assumption models to this data, mindful of credibility limitations and risks of over-fitting data. Finally, we will show how relevant external data can be incorporated to refine the model, and how to quantify the cost-benefit of accessing this data and improving a company’s risk management.


Obstacles to annuity reinsurance deals?

I'll be speaking about how to deal with one of the biggies -- policyholder behavior assumptions -- at the 2019 SOA Reinsurance Seminar on Sep 24-25.  Hope to see you there.