CAT Modelling. Jeremy Waite Nicholas Miller. Institute of Actuaries of Australia

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2 CAT Modelling Jeremy Waite Nicholas Miller Institute of Actuaries of Australia This presentation has been prepared for the Actuaries Institute 2014 General Insurance Seminar. The Institute Council wishes it to be understood that opinions put forward herein are not necessarily those of the Institute and the Council is not responsible for those opinions.

3 Team Susan Ley, Munich Re, Convenor, Anu Agarwal, IAG Re Michael Barkhausen, Willis Re Andrew Hulme, AON Nicholas Miller, QBE Jeremy Waite, JLT Re Richard Yee, E&Y

4 Purpose of Note To assist Members who are required to provide commentary under APRA prudential standards or professional standards on estimates of liabilities or prescribed capital that rely to a material degree on the output of catastrophe models.

5 Purpose of Presentation To broadly cover note topics Read the note for the detail.. Read the references in the note for more detail Do a Phd in your specific peril engineering for even more detail. Then to go beyond the note Aim to keep it practical and pragmatic An RDS example And some real approach examples from QBE and IAG Cat modelling is a bottomless pit of detail The construction and interrogation of cat models is not an actuarial pursuit we are users of the outputs Applied common and business sense are needed to make catastrophe management decisions Actuaries can help in this process AA s are 3 rd line of defence Board is ultimately responsible

6 Actuaries and Cat models Information Note is intended to cover what an Actuary may consider in fulfilling the regulatory requirements as well as those of the professional standards:- PS300 (IoAA) GI Valuation PS305 (IoAA) FCR for GI GPS320 (APRA) Actuarial & Related Matters GPS116 (APRA) - Capital Adequacy ICRC Ultimate responsibility for the prudential management of capital of a general insurer rests with its Board of directors.

7 What is a Cat model Catastrophe models are developed by groups of scientists, engineers, mathematicians and actuaries working together to simulate catastrophic events. While most actuaries conceptually agree that catastrophe models may provide more realistic measures of catastrophic risk than those provided by analysing the latest twenty to fifty years of catastrophe losses, most actuaries are not experts in many of the underpinnings of these models. Actuarial Standards Board (2011): ASOP 38.

8 Is the modelling appropriate Not role of the Actuary to undertake all or any of the investigations How to assess Considerations when using results Data Quality & Governance

9 Vendor Model Availability in Australia Vendor Cyclone Earthquake Bushfire Flood Storm Hail AIR Yes Yes Yes No No No EQECAT Yes Yes No No No No Risk Frontiers Yes Yes Yes Yes No Yes RMS Yes Yes No No Combined - Sydney only How has Storm risk out of Sydney been considered? How s flood been modelled? Are cyclones more frequent than in the past (RMS model 2006 vintage) Any consideration given to ENSO and bushfire risk? Be prepared to step away from the model

10 Relevant Australian perils modelled?

11 Uncertainty - Causes? Uncertainty from model Scope Uncertainty from model input Uncertainty from model Scope Uncertainty from model Scope Uncertainty from model output

12 Uncertainty - Causes? The uncertainty is not only due to the model, but real uncertainty in the phenomena which despite the best efforts of the brightest scientists/engineers and big budgets of the vendor firms can t be quantified within the model

13 More Uncertainty - Causes? What can go wrong with Models Bad Physics Bad Assumptions Bad Data Bad Luck Too many Black swans Plate tectonics was only accepted in the early 1960 s by the geoscientific community Source: Stein, Geller, Liu 2012

14 Uncertainty Australian EQ real uncertainty Earthquake Epicentres in Australia 1841 to 2000 The Australian continent is completely within the Australian plate and consequently there are no major through going active faults like the Alpine fault in NZ or the San Andreas fault in Western USA. Source: McCue, K.F, 2001 Earthquake Epicentres in Australia and recent fault scarps, AGSO Geoscience Australia, Canberra

15 Realistic Disaster Scenarios Cyclone Earthquake Bushfire Flood Storm Hail 1 In 200 PML not an event What events of this scale are plausible Hence RDS (used at Lloyds since June 1994) Easy to Understand Board and investor-friendly Qualitative and Quantitative Testing Reinsurance arrangements Capital impact Satisfies APRA stress and scenario test Vertical Requirement Horizontal Requirement

16 Realistic Disaster Scenarios A specific RDS event typically has the following details:- A definition of the physical event, with a map showing the footprint or storm-track; The assumed industry insured loss (could be split by line of business e.g. Property Domestic, Commercial Industrial or include other classes of business if material (e.g. Marine)) Other details could be provided (e.g. where applicable, a catalogue of major infrastructure (i.e. ports) that may be affected by the event);

17 Event Details RDS - Example Peril Location RDS ID Cyclone Brisbane 1 Postcode at Centre Longditude Latitude Magnitude Category 3 Event Description Central Pressure 947hPa, South Westerly direction Cyclone Earthquake Bushfire Flood Storm Hail RDS example for a 1 in 200 cyclone (Australia return period) loss Market loss $13.7bn Event details and impact on XYZ insurer Here evidence of anti selection? Event Similar to Cyclone Daisy (1972, 965hPa) Loss Details Industry Loss XYZ Gross Loss Most costly CRESTA ID $ Bn $351,977,876 XYZ Market share of Loss XYZ Reinsurance Recoveries Most costly CRESTA Name 2.561% $ - Low % Damage <<< Relative RIsk >>> High % Damage XYZ Market share of Exposure XYZ Net Loss Most costly CRESTA Gross Loss 1.922% $351,977,876 $173,653,234 2 Brisbane

18 Dealing with Uncertainty in Models Recognise importance Identify sources Assess consequences Adjust or mitigate Alternative insights More than one model Scenarios Stress Testing Workshops Reverse Stress Testing Management Action

19 Dealing with Uncertainty Avoid Change risk profile portfolio Introduce new rating factors avoid anti selection Change mix of business Mitigate Use a larger PML than modelled Use conservative inputs Have more capital than minimums Management Actions Reduce Change cover introduce sub limits in direct policies Have event excess in direct policies Increase deductibles Transfer Buy reinsurance - VR Buy aggregate cover - HR Buy a longer return period than APRA minimum Add a $ load on the PML limit Buy a parametric cover Buy a cat bond

20 Does the note s perspective help Christchurch Model driven PML Only Unknown fault line (Hazard map) Model gave low probability (OEP curve) Liquefaction not included in model Etc Christchurch using alternate RDS Method IF Christchurch was a significant exposure AND a Christchurch RDS was selected, then could have quantified RDS impact of event of market return period size located at Christchurch on XYZ RDS- possible multiple large events in same location (not model driven) impact on XYZ RDS vary damage (sense testing) impact on XYZ Potentially leading to Action: Avoid, Reduce, Mitigate or Transfer

21 Would the note s perspective help Models do not replace common sense A fool with a tool is still a fool Note aims to help wider Actuaries & Clients perspective There is real uncertainty and model measurable uncertainty Alternate approaches can give wider perspective and lead to action RDS are accepted alternatives to model driven results Stress and scenario can give perspective and open up understanding Can lead to alternative action Actuaries are part of a process involving other expertise beyond their own Note aims to assist actuaries in carrying out their duties

22 QBE Example

23 QBE Background to QBE ANZO Multi-line insurer Wide geographic coverage Global structure License catastrophe models

24 Use of catastrophe models Data -consistent, accurate and complete Year Built Selection of Model -fit for purpose -research, expert opinion Construction No of Stories Results Analysis -gaps, limitations, sensitivity Wood Masonry Concrete Steel experience, underwriting, other approaches

25 Alternative methods Use of alternative methods: RDS Experience, internal models Exposure analysis Use of DFA tools

26 Questions