Normalized combined ratios22/3/2021 Financial Year 2020 results have now been released for the top 5 reinsurers and on the face of it, they don’t make pretty reading. The top 5 reinsurers all exceeded 100% combined ratio, i.e. lost money this year on an underwriting basis. Yet much of the commentary has been fairly upbeat. Commentators have downplayed the top line result, and have instead focused on an ‘as-if’ position, how companies performed ex-Covid.
We’ve had comments like the following, (anonymised because I don’t want to look like I’m picking on particular companies): "Excluding the impact of Covid-19, [Company X] delivers a very strong operating capital generation" “In the pandemic year 2020 [Company Y] achieved a very good result, thereby again demonstrating its superb risk-carrying capacity and its broad diversification.” Obviously CEOs are going to do what CEOs naturally do - talk up their company, focus on the positives - but is there any merit in looking at an ex-Covid position, or is this a red herring and should we instead be focusing strictly on the incl-Covid results? I actually think there is a middle ground we can take which tries to balance both perspectives, and I’ll elaborate that method below. Exposure inflation vs Exposure inflation18/2/2021 The term exposure inflation can refer to a couple of different phenomena within insurance. A friend mentioned a couple of weeks ago that he was looking up the term in the context of pricing a property cat layer and he stumbled on one of my blog posts where I use the term. Apparently my blog post was one of the top search results, and there wasn’t really much other useful info, but I was actually talking about a different type of exposure inflation, so it wasn’t really helpful for him.
So as a public service announcement, for all those people Googling the term in the future, here are my thoughts on two types of exposure inflation: An Actuary learns Machine Learning - Part 10 - More label encoding / Gradient Boosted Regression15/2/2021 An Actuary learns Machine Learning - Part 8 - Data Cleaning / more Null Values / more Random Forests6/2/2021 An Actuary learns Machine Learning – Part 4 – Error correction/data cleansing/Feature Engineering10/1/2021 FAQs about Lloyd’s of London16/11/2020 I sometimes get emails from individuals who have stumbled across my website and have questions about Lloyd's of London which they can't find the answers to online. Below I've collated some of these questions and my responses, plus some extra questions chucked in which I thought might be helpful.
A brief caveat - while I've had a fair amount of interaction with Lloyd's syndicates over the years, I have never actually worked within Lloyd's for a syndicate, and these answers below just represent my understanding and my personal view, other views do exist! If you disagree with anything, or if you think anything below is incorrect please let me know! Are Lloyd’s of London and Lloyds bank related at all? They are not, they just happen to have a similar name. Lloyd’s of London is an insurance market, whereas Lloyd’s bank is a bank. They were both set up by people with the surname Lloyd - Lloyds bank was formed by John Taylor and Sampson Lloyd, Lloyd’s of London by Edward Lloyd. Perhaps in the mists of time those two were distantly related but that’s about it for a link. Am ‘I’ really a Strange Loop?28/10/2020 ![]() I just finished reading ‘I am a strange loop’ by Douglas Hofstadter, and before I say anything else about the book, I’ll say that I really did want to like it. I’m a huge fan of his better known book ‘Godel, Escher, Bach’ for which Hofstadter won a Pulitzer Prize, I’m also very interested in the subject area – maths, logic, self-reference, cognitive science. However there were just too many things that rubbed me up the wrong way, in no particular order here were all the things I didn’t like about the book: Excess layer pricing16/9/2020 I had to solve an interesting problem yesterday relating to pricing an excess layer which was contained in another layer which we knew the price for – I didn’t price the initial layer, and I did not have a gross loss model. All I had to go on was the overall price and a severity curve which I thought was reasonably accurate. The specific layers in this case were a 9m xs 1m, and I was interested in what we would charge for a 6m xs 4m.
Just to put some concrete numbers to this, let’s say the 9m xs 1m cost \$10m The xs 1m severity curve was as follows: |
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