Boot Camp on Reinsurance Pricing Techniques - Loss Sensitive Treaty Features

1. Boot Camp on Reinsurance Pricing Techniques -Loss Sensitive Treaty Features

2. 2 Table of Contents Loss Sensitive Feature defined Uses for Loss Sensitive Features Examples of Loss Sensitive Features Valuing Loss Sensitive Features in a treaty Questions?

3. 3 What is a Loss Sensitive Treaty Feature? A Loss Sensitive term or feature is a provision within a reinsurance contract that causes the ceded premium, ceded loss, or commission to vary based on the loss experience of that contract. Why have such a feature? Allows cedants to share in the ceded experience which could motivate them to care more about the results! Works to bridge the gap that may exist between the reinsurer’s loss pick and the cedant’s loss pick Such features are generally “worth something” and it’s the reinsurance pricing actuary’s role to figure out what that value is How will this feature add to or subtract from underwriting ratio? Does the feature make sense along with the rest of the deal structure? Can you present more than one structure option to the cedant that has the same value to the reinsurer?

4. Types of Loss Sensitive Features Features that cause ceded premium to vary based on loss experience: Reinstatement Provisions Swing Rated Contracts No Claims Bonus Features that cause ceding commission to vary based on loss experience: Profit Commission Sliding Scale Commission Features that cause ceded loss to vary based on loss experience: Annual Aggregate Deductibles (AADs) Loss Ratio Corridors Loss Ratio Caps 4

5. Which reinsurance structures might have these features? Pro Rata / QS Treaties Profit Commission Sliding Scale Commission Loss Corridor Loss Ratio Cap Excess of Loss (XOL) Treaties Profit Commission Reinstatements Swing Rating Provisions No Claims Bonuses (if anywhere, Cat XOLs) Annual Aggregate Deductibles Loss Ratio Cap 5

6. 6 Profit Commission Very common loss sensitive feature for both Quota Shares or XOLs. Cedant can receive a defined percentage of “profit” on the reinsurance contract, where profit is often defined as (Premium – Loss – Commission – Reinsurer’s Margin) “50 after 10” PC with ceding commission of 30%, has PC formula = 50% * (1-.3-.1-LR) = 50% * (.6 – LR) Therefore the cedant will achieve some sort of profit commission for any loss ratio result that is better than 60% What is the EXPECTED cost of the PC? Let’s assume our ELR is 60% - and we know no PC is paid at a 60% - does that mean that the expected cost of the PC is zero?

7. Profit Commission (continued) Answer: Just because there is no PC paid at the expected loss ratio, that seldom means the EXPECTED cost of the profit commission is zero Put another way, the cost of the PC at the EXPECTED loss ratio is not equal to the EXPECTED cost of the PC WHY? (And believe me, underwriters will want to know why) 60% is the EXPECTED loss ratio – that doesn’t mean we feel that every possible loss ratio outcome for this treaty is a 60% There is a probability distribution of potential outcomes AROUND that 60% expected loss ratio It is possible (and maybe even likely) that the loss ratio in any year could be far less than 60%, (where we would be paying on the PC) Therefore there is a COST to this feature, which we will work to calculate in the coming slides. Note a PC only goes one way – the cedant receives money when the deal is running profitably; the cedant does not pay out more money when the deal isn’t running profitably 7

8. Cost of PC calculation – specific extreme case EQ exposed California property QS If there’s no EQ, we expect a 40% Loss Ratio every year, every scenario Cat (EQ) ELR = 30% 90% chance of no EQ 10% chance of EQ where resulting LR = 300% Ceding commission = 30% PC terms are “50% after 10%” If there is NO EQ, we know the LR = 40%, so PC value = .5*(1-.4-.3-.1) = 10% If there IS an EQ, there is no PC. So what is our EXPECTED cost of PC? 0% PC, 10% of the time (yes EQ), plus 10% PC, 90% of the time (no EQ) OR….9% of Premium. Because of the “skewed” nature of Cat, PCs are not common. If you are going to have a huge loss every 10 years, the reinsurer wants to keep as much premium as possible the other 9 years 8

9. Cost of Loss Sensitive Feature: General Build Aggregate Loss Distribution Judgmentally select loss ratio outcomes and assign each a probability of happening Fit data to either an aggregate distribution (like a lognormal) or fit frequency data separately from severity data and combine Hardcore curve fitting is beyond the scope of this presentation Apply loss sensitive terms to each point on the loss distribution or to each simulated year Calculate a probability weighted average cost (or savings) of the loss sensitive arrangement 9

10. Valuing the cost of a PC of 50% after 10%, 30% Ceding Commission, 60% Expected LR 10

11. WHY NOT??? Your loss distribution – the loss ratios and the probabilities assigned to them – are what determines the value of your loss sensitive feature. In general, the more “skewed” your distribution is, the more apt you are to have to give money back to the cedant. You need a LOT of “better than average” scenarios to balance out the few AWFUL scenarios you may have. It’s those “better than average” scenarios that usually mean you are giving money back to the cedant. (Recall our “extreme” case earlier – 10% chance of getting crushed w/ EQ – 90% of the time you don’t). Since your expected loss sensitive value (cost or savings) is a function of your loss distribution, determining the appropriate distribution is important! (BUT NOT EASY!) 11

12. What if your loss distribution is shaped like this? 12

13. Or like this? 13

14. Other Loss Sensitive Features on QS’s Pro Rata / QS Treaties Profit Commission (already covered) Sliding Scale Commission Loss Corridor Loss Ratio Cap 14

15. Sliding Scale Commission A ceding commission is set at a “provisional” level at the beginning of a contract. This provisional ceding commission corresponds to a certain loss ratio in the contract “30 at a 60” The ceding commission INCREASES if the contract’s loss ratio is lower than the loss ratio that corresponds to the provisional, usually subject to a max commission The ceding commission DECREASES if the loss ratio is higher than the loss ratio that corresponds to the provisional, usually subject to a min commission As you can see, the ceding commission “slides” in the opposite direction as the loss ratio A slide is particularly useful when the reinsurer and the insurer’s loss picks differ “Put your money where your mouth is” 15

16. Sliding Scale Example Provisional Ceding Commission = 20% If the loss ratio is less than 65%, then the commission will increase by 1 point for each point decrease in loss ratio. The max commission will be 25% (at a 60%) This is said to slide “1 to 1” and is considered a “steep slide” If the loss ratio is greater than 65%, the commission will decrease by .5 points for each 1 point increase in loss ratio. The min commission will be 15% at a 75%. This is said to slide “1/2 to 1”  16

17. Valuing a Sliding Scale Commission 17

18. Loss Ratio Corridor A loss ratio corridor is a provision that forces the ceding company to retain losses that would otherwise be ceded to the reinsurance treaty Useful when there is a difference in LR pick, but not nearly as common as a slide For example, the ceding company could keep 100% of the losses between a 75% and 85% loss ratio – or a “10 point corridor attaching at 75%” If the gross loss ratio = 75%, then the ceded loss ratio = 75% (no corridor attaches) If the gross loss ratio = 80%, then the ceded loss ratio also = 75% Corridor “attaches” at 75% for 10 points Ceding company takes all losses between 75% and 80% (or 5 points) Therefore the ceded loss ratio still = 75% If the gross loss ratio = 85%, then the ceded loss ratio STILL = 75%! Corridor attaches and is fully exhausted for the full 10 points Therefore the ceded loss ratio still = 75% If the gross loss ratio = 100%, then the ceded loss ratio = ??? The corridor doesn’t have to be 100% assumed by the cedant – can be any % 18

19. Loss Ratio Cap This is the maximum loss ratio that could be ceded to the treaty Once losses hit or exceed the cap, that’s it – Example: 200% Loss Ratio Cap If the loss ratio before the cap is 150%, the ceded loss ratio is 150% If the loss ratio before the cap is 300%, the ceded loss ratio is 200% Useful for new / start up operations where the limit to premium ratio may be dreadfully imbalanced. New Umbrella program offering $10M policy limits but only plans on writing $3M in premium the first year May be the only way for such a reinsurance treaty to get placed – particularly on start up business - while the cap is generally high, at least the deal downside is limited… 19

20. Determining an Aggregate Distribution – 3 Methods Judgmentally select loss ratio outcomes and corresponding probabilities whose weighted average equals your expected loss ratio May not contain enough bad scenarios if you’re basing your points on experience May be the easiest to explain to underwriters Easy, but again, may not reflect enough bad outcomes – be careful! Fit statistical distribution to on level loss ratios Reasonable for Pro Rata (QS) Treaties Most actuaries use a Lognormal Distribution here Reflected skewed distribution of loss ratios Relatively easy to use Loss Ratios here are assumed to follow a lognormal distribution which means that the natural log of the loss ratios are normally distributed. Determine an aggregate distribution by modeling the frequency and severity pieces separately and either convolute them or simulate them together Typically used for excess of loss (XOL) treaties Lognormal doesn’t make sense if you can have zero losses Lognormal very likely not skewed enough anyway; XOL can be “hit or miss” (literally) 20

21. Judgmentally selected aggregate loss distribution 21

22. Lognormal Distribution: ELR = 60%, SD = 10% 22

24. Lognormal Distribution: ELR = 60%, SD = 20% 24

27. Lognormal Distribution: ELR = 60%, SD = 30% 27

29. Is the resulting distribution reasonable? Compare resulting distribution to historical results On leveled loss ratios should be the focus, but don’t completely ignore untrended ultimate loss ratios Sure, I see the rate action they’ve taken, and how trends play in, but how have they actually done, and how volatile have those results been? Do your on leveled loss ratios capture enough cat or shock loss potential? Do you think your historical results are predictive of future results? Show the distribution to your underwriters – see what they think – Ultimately, sometimes a judgmentally selected discrete distribution makes the most sense – and can be the easiest to explain to underwriters 29

30. Creating distributions when there’s cat exposure If you have a treaty with significant catastrophe exposure, you should consider modeling the noncat loss ratio separately from the cat loss ratio Noncat could probably be a straight forward lognormal Cat is probably MUCH more skewed – think early CA EQ example! Large chance nothing bad happens, small chance you get clobbered Combining the cat and noncat is easy to do during a simulation – particularly if you assume they are independent – you can simulate a noncat result, and then a cat result, and then just add them together at the end In the case of noncat and cat, it’s very difficult to find one distribution to address all your needs. Since a very skewed distribution generally leads to a higher loss sensitive cost, be careful not to underestimate the value – these things all add up! Even a high CV on your lognormal probably won’t help you out – sure, you get some high loss ratio outcomes which could represent your cat scenarios, but you get a ton of very good outcomes – probably too many – to weight back to your expected loss ratio. You could introduce a minimum loss ratio (truncated) – but that means you won’t weight back to your expected loss ratio without some adjusting. 30

31. What about process and parameter uncertainty? Process Uncertainty is the random fluctuation of results around the expected value just due to the random nature of insurance – not every year is going to be the same! Parameter Uncertainty is the fluctuation of results because our parameters used to determine our expected value are never going to be perfect. Are your trend, rate changes, and loss development assumptions reasonable? For this book, are past results a good indication of future results? Changes in mix of business? Changes in management or philosophy Is the book growing? Shrinking? Stable? Selected CV should generally be greater than what is indicated 5 to 10 years of data does not reflect a full range of possibilities Anything with cat exposure really emphasizes this – when was the last CA EQ? 31

32. Addressing Parameter Uncertainty: One Suggestion Instead of just choosing one Expected Loss Ratio, choose 3 (or more) Assign weights to the new ELRs so that they all weight back to your original ELR For example if your ELR is a 60%, maybe there’s a 1/3 chance your true mean is 50% and a 1/3 chance your true mean is 70% Simulate the true mean by randomly choosing between the 50%, 60%, and 70%. Once you’ve randomly chosen that’mean (either 50%, 60%, or 70%) then model using the lognormal based on that chosen mean and your selected CV Note the CV will handle your process variance You’re all set! 32

33. Loss Sensitive Features on XOLs Excess of Loss (XOL) Treaties Profit Commission Reinstatements Swing Rating Provisions No Claims Bonuses (if anywhere, Cat XOLs) Annual Aggregate Deductibles Loss Ratio Cap 33

34. Swing Rating Provisions Ceded premium is dependent on loss experience Reinsurer receives initial premium based on a provisional rate That rate swings up or down depending on the loss experience in accordance to the terms of the contract Typical Swing Rated Terms Provisional Rate = 10% Minimum Rate/Margin = 3% Maximum Rate = 15% “Losses Loaded” at = 1.1 Ceded Rate = Min/Margin + (Ceded Loss / SPI)x(1.1), subject to the max rate of 15% Common on medical malpractice XOLs - not really seen anywhere else Note this feature is an adjustment to PREMIUM – not COMMISSION 34

35. Swing Rating Example 35

36. Annual Aggregate Deductible The annual aggregate deductible (AAD) refers to layer losses that the cedant retains that would otherwise be ceded to the treaty These are the FIRST losses that get paid in a layer – similar to a loss corridor but an AAD is always the first losses Example: Reinsurer provides a $500,000 x $500,000 excess of loss contract. The cedant retains an AAD of $750,000 This means the cedant keeps the first $750,000 of layer losses Total Loss to Layer = $500,000? Cedant retains entire $500,000 (because AAD is $750,000) No loss is ceded to reinsurers Total Loss to Layer = $1M? Cedant retains entire AAD of $750,000 Reinsurer pays $250,000 If the cedant requests a $500,000 AAD for a treaty, should the actuary reduce his expected layer losses of $1M by $500,000? 36

37. Likely no! (But you knew that, right?) 37

38. No Claims Bonus A No Claims Bonus provision can be added to an excess of loss contract – it’s exactly what it sounds like Since any pro rata or QS contract is apt to have loss ceded to it – because these structures cover losses of all sizes – not just large losses – a no claims bonus doesn’t make sense Very binary – if there are no losses, cedant can receive a small % of premium back Not a typical feature to see – might see a small no claims bonus on Property Catastrophe XOLs – but only to the tune of a 10% bonus 38

39. Limited Reinstatement Provisions Many excess of loss treaties have reinstatement provisions. Such provisions dictate how many times the cedant can use the risk limit of the treaty. Reinstatements can be free or paid – but choosing to reinstate is almost always mandatory Just because it’s paid, doesn’t mean the cedant owes 100% of the reinsurance premium all over again – could be 1st @ 50%, 2nd @75%, etc Catastrophe treaties tend to have “1@100%” – or, you can reinstate the limit once for the full reinsurance premium Limited reinstatements are an implied treaty aggregate limit, or treaty cap. Treaty Aggregate Limit = Risk Limit x (1+ number of reinstatements) Example: $1M x $1M layer with one reinstatement After the cedant uses up the first $1M limit, they get a second limit Treaty Aggregate Limit = $1M x (1+1) = $2M Reinstatement can either be free or paid – contract will tell you so Reinstatement premium can simply be viewed as additional premium that reinsurers receive depending on loss experience 39

40. Limited Reinstatement Example 40

41. Valuing a Limited Reinstatement Provision 41

42. Rating on a Multi Year Block As you can see from all of these structures presented, each year’s results stand on their own. For a PC, the cedant can have a great year 1 and receive a large profit commission in return. But then maybe year 2 is awful – and no PC is paid. Over these two years the cedant may be thrilled, but the reinsurer could be in bad shape. To work to bridge that gap, loss sensitive features can be evaluated using the total treaty experience across multiple years instead. This allows for a smoothing of results – and a smoothing of profit commission paid. This is called rating on a Multi Year Block Modeling a multiyear block implies tightening up your loss distribution – 3 years of data will tend more towards your mean than just one year – law of large numbers kicking in – consider a lower standard deviation, etc. 42

43. Deficit / Credit Carryforward Provision Another way to effect some kind of loss sensitive smoothing, but for sliding scale commission deals is to use a Deficit or Credit Carryforward Provision If the loss ratio is SO good and the cedant receives the max ceding commission anyway, the amount that the loss ratio is better than the loss ratio at the max rolls into the next year’s calculation. This is a credit carryforward. If the loss ratio is SO bad and the cedant receives the min ceding commission anyway, the amount that the loss ratio is worse than the loss ratio at the min rolls into the next year’s calculation. This is a deficit carryforward. Similar to a multi year block, this provision works to smooth out loss sensitive results 43

44. Modeling Frequency and Severity Separately While a lognormal distribution is relatively easy to use, it is not usually appropriate for XOL treaties Does not reflect “hit or miss” nature of many excess contracts Understates the probability of zero loss May understate the potential of losses MUCH greater than the expected loss Modeling Frequency and Severity separately is more common for XOL Simulation (most common) Excel @ Risk Some broker products – MetaRisk, Remetrica Numerical Methods You can use a lognormal for a very “working” layer – meaning one where you expect A LOT of claims with great certainty 44

45. Common Frequency Distributions Poisson is an easy-to-use distribution to model expected claim count Poisson distribution assumes the mean (lambda) and variance of the claim count distribution are equal Discrete distribution – number of claims = 0, 1, 2, 3, etc… Despite the Poisson’s ease of use, Negative Binomial more preferred Same form as the Poisson expect that lambda is no longer considered fixed but rather has a gamma distribution around lambda Variance is greater than the mean (unlike Poisson where they are equal) Preferred over Poisson because it reflects a little more parameter uncertainty regarding the true mean claim count The extra variability of the Negative Binomial is more in line with historical experience 45

46. Common Severity Distributions Lognormal Mixed Exponential (currently used by ISO) Pareto Truncated Pareto – was used by ISO before moving to the Mixed Exponential CAVEAT: If you are fitting a severity distribution to actual claims, don’t forget about loss development! (Maybe use ISO curves instead of building your own) 46

47. Concluding Remarks There are many loss sensitive features available that can be used to make a reinsurance treaty acceptable to both the cedant and the reinsurer It’s up to the actuary to value the requested features and explain the results to underwriters Depending on the shape of your loss distribution, your loss sensitive feature’s expected cost or savings can vary greatly A little sensitivity testing on a range of distributions can go a long way! 47