Island Life

There is a Mauritian insurer called Island Life. Best name ever for an insurance company.

I firmly believe in anthropogenic climate change. I am not an expert, but my reading has convinced me of the seriousness of the issues, the overwhelming evidence that it is humans at fault. Having young children makes me seriously concerned about our ability to remedy the mess we’re in for their sake.

Part of my very inwards focussed, selfish research was on the practical impact for where I live. Unfortunately for my cycling aspirations, this is current on the bottom slopes of Table Mountain.  Fortunately for my flood risk, the same.

The screen capture below is of an extreme, and hopefully not realistic, 30m rise in sea levels.  The website I used for visualising the impact of sea level rises makes no claims to accuracy, but it is interesting all the same.

Oranjezicht in Cape Town mostly stays above the water line even with 30m rise in sea level

Continue reading “Island Life”

Do Data Lakes hide Loch Ness Monsters?

I had a discussion with a client recently about the virtues of ensuring data written into a data warehouse is rock solid and understood and well defined.

My training and experience has given me high confidence that this is the right way forward for typical actuarial data.  Here I’m talking in force policy data files, movements, transactions, and so on.  This is really well structured data that will be used many times by different people and can easily be processed once, “on write”, stored in the data warehouse to be reliably and simply retrieved whenever necessary. Continue reading “Do Data Lakes hide Loch Ness Monsters?”

Credit Life regulations and reactions (3)

This is a short addition to parts 1 and 2.

The question as to whether the benefit payable under a credit life policy can or should include arrears payments.

The purpose of a credit life policy is to protect the policyholder, the lender, and the policyholder’s estate (not necessarily in that order) against death, disability or retrenchment. This is only effectively achieved if the entire amount owing under the credit agreement is paid off by the policy.

So as a starting point, it would make sense for arrears to be included. All the stakeholders in the arrangement want this.

Back to the legal stuff

What do the credit life regulations say? Continue reading “Credit Life regulations and reactions (3)”

Credit Life regulations and reactions (2)

In part 1 I discussed the implications of basing premiums on initial balance or declining balance for profitability and the threat of substitute policies.

In this post I want to discuss substitute policies again, talk about cover for self-employed persons and definitions of waiting periods.

What is a substitute policy

Substitute policies are one of the few drivers of real potential competition and therefore competitive markets for credit life in South Africa. That’s probably not the definition you were expecting but nevertheless it is true.

With some exceptions, credit life is not sold in a competitive or symmetrical environment and customers have little or no bargaining power.

 

A substitute policy is a policy from another insurer (not connected to the lender) that covers the same or similar benefits and legally must be accepted as a substitute for the cover required by the lender under the terms of the loan.

Historically, the rate of substitute policies was tiny. Often less than 1%. Lenders and their associated insurers weren’t exactly incentivised to make it an easy process. For smaller loans and therefore smaller policies, the incremental acquisition costs can be prohibitive.

Substitute policies are gaining momentum

I am aware of several players specifically targeting existing credit life customers and aiming to switch these customers to their own products.

This has been enabled through:

  • standardising of credit life policies
  • bulking of many different small credit life policies into a larger one that is more cost effective to acquire and administer
  • technology (digital / online especially but also call centres) that can moderate costs
  • the growing awareness of how profitable these policies often are for a standalone insurer, even at the various caps imposed.

Lenders may need to supplement revenue on high risk customers because interest rate caps apply, but the stand alone insurer is focussed on a reasonable underwriting result, not the level necessary to offset costs elsewhere.

What counts as a substitute policy / minimum prescribed benefits

A substitute policy simply needs to cover the minimum benefits from section 3 of the credit life regulations. This covers death, permanent disability, temporary disability and unemployment or loss of income.

These regulations can be difficult to interpret, but ultimately are clear: Continue reading “Credit Life regulations and reactions (2)”

ERP update – delayed response to a blog reader

I reader asked why so many practitioners use high Equity Risk Premiums in their valuations and fairness opinions.

In particular, he mentioned a specific assumption set he had seen including:

  • ERP of 6.8%
  • company specific risk premium of 4%

He also commented on how haphazard the use of risk premiums can be and referenced a few sources I’ve used myself.

The ERP of 6.8% does seem high. However, it really isn’t possible to comment on the specifics of the company specific risk premium without knowing the company.

Although I haven’t updated my research on this in a few years, in my own work I still generally stick with a range of 3% to 5% for an ERP, before considering company specific factors, liquidity, and so on. Historically / empirically estimated ERPs shouldn’t change frequently since the time series used is long. Another few years on a 20 year estimation period shouldn’t have much impact.

Why some practitioners persist in using too-high ERP estimates

This delves into the area of philosophy, but here are my top reasons (a post from 2011 also covers this): Continue reading “ERP update – delayed response to a blog reader”

ENID not Blyton

ENID is a term widely used, just generally not in South Africa. For some reason we didn’t import the term along with most of Solvency II.

This has nothing to do with the Famous Five. While it is most common in the general insurance space, it is relevant across the spectrum of risk management and assumption setting.

Events Not In Data or “ENID” is the forgotten cousin of “what to do with outliers in your data”.

Outliers and where to find them

Outliers are observed values substantially different from others in a sample. Some more formal definitions include:

“An outlier is an observation that lies an abnormal distance from other values in a random sample from a population”

“an outlier is an observation point that is distant from other observations”

Not these sort of outliers. Entertaining book though.


How to deal with outliers?

Simple question, complex answer. It depends a great deal on the context.

Ultimately you need to make the judgement call “are these outliers under- or over-represented in the data”. Continue reading “ENID not Blyton”

Why the last 7 years have felt rough

We’ve been in and out of recession. We’ve had more political drama than I’d like for a lifetime. We’ve had several lifetimes of obvious, unresolved corruption and fraud. We’ve had a volatile and depreciating currency by and large.

This graph brought it home a little to me:

You can see the sharp decline in South Africa’s GDP measured in USD terms over the period.

Egypt, despite an Arab Spring has not experienced the same precipitous decline. Nigeria’s recent troubles are now also clear.

Our USD GDP is below the point it was in 2010, offsetting a brief period after 2010 when it was still increasing. So maybe it’s more about the last 5 years than the last 7.

Of course, that is the wrong chart. GDP is affected by population growth and what we experience as individual citizens within a country is closer to GDP per capita. Continue reading “Why the last 7 years have felt rough”