Category Archives: insurance

South Africa ranks 2nd in financial inclusion study

The Brookings Financial and Digital Inclusion Project measures South Africa one place behind Kenya in terms of financial inclusion.

I’m still working my way through the full report, but Kenya’s score is a significant jump above South Africa and the closely contested positions below it. Is Kenya genuinely making such inroads or is this a function of the measures used?

The worst insurance policy in the world

Aviva in France is still dealing with having written the worst insurance policy in the world. From the sounds of things, they weren’t alone in this foible. It’s also hard to say as an outsider what the right or reasonable resolution to their current problem is, but here is the policy that they wrote.

  • Buy a policy
  • Choose what funds you want to invest in
  • Unit prices calculated each Friday
  • Allow policyholders to switch funds on old prices until the next week
  • Hope like hell policyholders don’t switch out of poorly performing funds into well performing funds with perfect information based on backwards, stale prices.

Inconceivable – and since I don’t know more than I read on this blog post, maybe the reality and liability is really quite different.

See the post from FT Alphaville on the man who could own Aviva France.

SA85-90 “combined” and more actuarial sloppiness

I know of far too many actuaries who think that the “average” SA85/90 table is an appropriate base for their insured lives mortality assumption.

It’s not.

It’s also a good example of “actuarial sloppiness”.

To be specific, it is equally inappropriate if your current experience is a reasonable fit for the combined SA85/90 table.

SA85/90 was graduated based on South African insured lives data from 1985 to 1990. This period is important because it’s generally felt to be the last period in South Africa where HIV/AIDS would not have had a significant impact on mortality. (Estimates differ, but 1985 is often taken as the starting point for the HIV epidemic in South Africa and even though there might have been some deaths within the first five years, it is inconceivable to have affected a significant portion of the population.)

SA85/90 came in two version, “light” and “heavy”. Somewhat disappointingly, no distinction was made between males and females. Light mortality reflected the typical, historical, insured life characteristics which was pretty much white males. If I recall correctly, “Coloured” and “Indian” males were also combined into the light table. “Heavy” mortality reflected the growing black policyholder base in South Africa.

For all the awkwardness of this racial classification, the light and heavy tables reflect the dramatically different mortality in South Africa based on wealth, education, nutrition and access to healthcare. Combining the results into a single table wasn’t reliable since there were significant differences in mortality AND expected changes in the proportions of the heavy and light populations in the insured populations into the future.

A combined table was still created at the time. I suspect Rob Dorrington may have some regrets at having created this in the first place or at least in not having included a clearer health warning directly in the table name. The combined table reflects the weighted experience of light and heavy based on the relative sizes of the light and heavy sub-populations during the 1985 to 1990 period. I think a safer name would have been “SA85/90 arbitrary point in time combined table not to be used in practice”.

There is no particular reason to believe that the sub-population that you are modelling reflects these same weights. Even for the South African population as a whole these weights are no longer representative. The groups, at least in the superficial sense we view any particular citizen as coming from distinctly one group, will fairly obviously have experienced different mortality but will also have experience different fertility and immigration rates.

Our actuarial pursuit of separating groups of people into smaller, homogenous groups should also indicate that in most cases the sub-population you are modelling will more closely reflect one or the other of these groups rather than both of them.

But even if, just for the sake of argument, your sub-population of interest does reflect the same mix at each and every age as baked into the combined SA85/90 table, then it would still be entirely inappropriate to use the table for all but the crudest of tasks. After all, there a reason for our penchant for homogenous groups. If you model your sub-population for any length of time, the mix will surely change as those exposed to higher mortality die at a faster rate than those with low mortality.

The first order impact would be that you would be modelling higher mortality over time than truly expected. Due to the relative mortality between the two populations differing by age, the actual outcome will be somewhat more complex than that and more difficult to estimate in advance. This is particularly important with insurance products where the timing of death is critically important to profitability.

So, just because you can get a reasonable fit to your experience of an age- or percentage-adjusted SA85/90 combined table does not mean you have an appropriate basis for modelling future mortality. It may not vastly different from a more robust approach, but it’s just sloppy.

LAC Seminar 2013 live tweeting complete list

A few people have mentioned that they found my “live blogging” or tweeting of the 2013 LAC Seminar in Cape Town and Joburg useful. I used the hastag #LACseminar2013. I’m repeating all of them here in case they’re useful in a slightly more long-lived medium of my blog. I didn’t cover all the sessions – below is all there is and yes, in reverse order for bizarre reasons I’m not going to go into now.

@23floor: Also, envisaged that product specifications, including commission, must be filed with regulator #LACseminar2013 #microinsurance

@23floor: And yes, a range of market conduct, board composition requirements are envisaged for micro insurance #LACseminar2013 #microinsurance

@23floor: Raw (cleaned and anonymize) data *might* be released publically. I would definitely support this. Data should be open #LACseminar2013

@23floor: PA90 understates mortality on average, but more under for males and only a little over for females #LACseminar2013 Continue reading LAC Seminar 2013 live tweeting complete list

Open mortality data

The Continuous Statistical Investment Committee of the Actuarial Society does fabulous work at gathering industry data and analysing it for broad use and consumption by actuaries and others.

I can only begin to imagine the data horrors of dealing with multiple insurers, multiple sources, multiple different data problems. The analysis they do is critically useful and, in technical terms, helluva interesting. I enjoyed the presentation at both the Cape Town and Johannesburg #LACseminar2013 just because there is such a rich data set and the analysis is fascinating.

I do hope they agree to my suggestion to put the entire, cleaned, anonymised data set available on the web. Different parties will want to analyse the data in different ways; there is simply no way the CSI Committee can perform every analysis and every piece of investigation that everyone might want. Making the data publicly available gives actuaries, students, academics and more the ability to perform their own analysis. And at basically no cost.

The other, slightly more defensive reason, is that mistakes do happen from time to time. I’m very aware of the topical R-R paper that was based on flawed analysis of underlying data. Mistakes happen all the time, and allowing anyone who wants to have access to the data to repeat or disprove calculations and analysis only makes the results more robust.

So, here’s hoping for open access mortality investigation data for all! And here’s thanking the CSI committee (past and current) for everything they have already done.

The Perfect Storm Part 1 – IFRS reporting under SAM

A client recently mentioned that they were concerned about the implication that the adoption of Solvency Assessment and Management (SAM) would have on insurance accounting under current IFRS4.

The apparent concern was that measurement of policyholder liabilities for IFRS reporting would change to follow SAM automatically.

Let me start out by saying this is categorically not the case. The adoption of SAM should not change IFRS measurement of insurance liabilities. In this post I’ll cover some of the technical details and common misconceptions of IFRS4 to demonstrate why this conclusion is so clear. Continue reading The Perfect Storm Part 1 – IFRS reporting under SAM