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Actuarial Society 2016 Convention 23 – 24 November 2016
Exposure Management Working Group
Bridget Bernon, Carla Fasana, Hannes van Rensburg
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Actuarial Society 2016 Convention 23 – 24 November 2016
Agenda
1. Exposure management background
2. Geocoding and Geospatial Analysis
3. Peril Modelling approach
4. SAM – overview of catastrophe risk and impact of reinsurance
5. ORSA – validation of catastrophe risk
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Actuarial Society 2016 Convention 23 – 24 November 2016
Exposure management
• Managing the risk of concentration to a single loss event, peril or exposure
• In general refers to Location and/or Total Limits Insured
• Main mitigating tool is to know what you are exposed to….or use reinsurance
• We as working group aim was to understand the state of insurance market in
South Africa, both in terms of pricing and capital.
• Previous presentations:
• State of data of market, and impact of data quality on capital and pricing
• Overview of local catastrophe models vs. SAM parameters
Positives – major investment by the market to obtain geocoded data.
As capital impact is understood, will get more focus and modelling will improve
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Actuarial Society 2016 Convention 23 – 24 November 2016
Recent Catastrophic Disasters
• New Zealand Earthquake
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Actuarial Society 2016 Convention 23 – 24 November 2016
Recent Catastrophic Disasters
• Italian Earthquakes
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Actuarial Society 2016 Convention 23 – 24 November 2016
Recent Catastrophic Disasters
• UK Floods and Brexit
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Actuarial Society 2016 Convention 23 – 24 November 2016
Recent Catastrophic Disasters
• Japanese Quakes
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Actuarial Society 2016 Convention 23 – 24 November 2016
Correlation??
• Die wereld mag dalk‘n veiliger plek wees as die bokke weerbegin wen…
• http://twentytwowords.com/funn
y-graphs-show-correlation-
between-completely-unrelated-
stats-9-pictures/
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Natural catastrophes vs springbok win %
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Actuarial Society 2016 Convention 23 – 24 November 2016
Overview - Geospatial analysis• Domain of geospatial analysis:
• Space / Place. Surface of the Earth, upwards in analysis of topography, and downwards (e.g. groundwater and geology). Starts with capturing co-ordinates…
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• Scale. From the most local (millimeter e.gproperty boundaries) to the global, in the analysis of sea surface temperatures or global warming
• Time. Backwards - movement of continents Future - attempts to predict the tracks of hurricanes, the melting of the Greenland ice-cap, or the likely growth of urban areas.
• Methods of spatial analysis are robust and capable of operating over a range of spatial and temporal scales.
• Technology and Data – accessible and available
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Actuarial Society 2016 Convention 23 – 24 November 2016
Interest for insurers
1. Natural perils impact property
• Frequency
• Severity
2. Exposure and Values – e.g. heat bubble
3. Time
4. Other impacts -
Infrastructure
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“The only reason for time is that everything
does not happen at once.”
― Albert Einstein
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Actuarial Society 2016 Convention 23 – 24 November 2016
Geospatial smoothing in pricing
• Spatial smoothing means that data points are averaged with their neighbours.
• Absence of credible experience, reasonable assumption that neighbouring areas have
similar risk levels.
• Assign credibility factor based on ‘adjacency’ or ‘distance’. Different filters to determine signal vs. noise.
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Actuarial Society 2016 Convention 23 – 24 November 2016
Geospatial smoothing…continued
• Fit GLM (personal lines) or Multi Factor model (commercial lines) excluding area as rating factor
to isolate demographic factors – age, income, credit etc.
• Consider residual risk – plot residuals vs. spatial coordinates
• Look for clusters of points
• Apply smoothing filter / clustering techniques
• Re-fit model with ‘smoothed’ zones as factor
• Limitations:
• Adjacent areas can sometimes be quite different in nature
• Rivers and railways can separate regions which are dissimilar in underlying riskiness, despite
being adjacent
• Need to be able to group claims and exposure data into adjacent areas need to calculate
statistics…what is an appropriate ‘area’ for SA
Source: Casact.Org
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Actuarial Society 2016 Convention 23 – 24 November 2016
Challenges with data• ‘Problematic’ Postal code – not very helpful in SA context… ASSA
presentation. ‘A Hitchhiker’s Guide to Geo-Coding’ - Clive Hoggard• SA – 1,167 postal codes covering 16,151 suburbs vs UK 1.8m post codes which generally
represents a street, part of a street, a single address, a group of properties, a single property, a sub-section of the property
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Actuarial Society 2016 Convention 23 – 24 November 2016
Challenges with data
• No single concept of ‘AREA’ which gives all statistics needed –
• Exposure data – stats SA ‘place’ is similar to a suburb level
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Source: statssa.
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Actuarial Society 2016 Convention 23 – 24 November 2016
Challenges with data
• Crime statistics – on a Police Precinct level, not necessarily
matching exposure information.
• Within a precinct, there can be large deviation
http://www.crimestatssa.com/index.php
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Actuarial Society 2016 Convention 23 – 24 November 2016
Develop Peril Regions
• Once all risks are geocoded, then post code becomes less important.
• Use concept of “Risk Layers” per peril linked to data source describing risk factor.
• Output from Layer relativity can be input into pricing model as rating factor.
• Crime – model correlation of frequency with crime stat including other mitigating
factors (e.g. protections)
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3000
0.00%
0.10%
0.20%
0.30%
0.40%
Claim Frequency vs Crime Rates
Exposure Claim Frequency
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Actuarial Society 2016 Convention 23 – 24 November 2016
Lightning Layer:
• Number of Strikes per specified area
• Data provided in 10km x 10km blocks
• Number of strikes per year
• Dark red is more than 10 per year
• Usefulness for pricing –
• Need to incorporate Topography
• Interpolation of observations
• Include mitigating factors in your GLM
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Actuarial Society 2016 Convention 23 – 24 November 2016
Market Data Providers
Subsidence Layer
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Actuarial Society 2016 Convention 23 – 24 November 2016
SAM Non-Life Underwriting Risk SCR
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Actuarial Society 2016 Convention 23 – 24 November 2016
SAM Catastrophe Risk
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Actuarial Society 2016 Convention 23 – 24 November 2016
SAM Earthquake Risk
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• Very well reinsured risk
• Proportional reinsurance
• Cat XL
• Prior to 2016 Q1 CPR: Choose event based on maximum net exposure
• 2016 Q1 onwards CPR: Choose event based on max net exposure +
counterparty default risk
• Possible switch from 4 horizontal events and hail to Earthquake
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Actuarial Society 2016 Convention 23 – 24 November 2016
Audience Poll
• Which method drives your Nat Cat capital charge?
A. Vertical earthquake
B. Vertical hail
C. Horizontal events
D. Don’t know
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Actuarial Society 2016 Convention 23 – 24 November 2016
SAM Earthquake Risk - Factors
• 5 factors provided by the FSB
• Earthquake risk factors
• Overall market factor
• Factors per line of business
• Factors per line of business and cresta zone
• Correlation matrixes
• Across lines of business
• Per line of business, across cresta zones
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Actuarial Society 2016 Convention 23 – 24 November 2016
SAM Earthquake Risk – Factors
Calculation Methodology
• Compulsory data request sent to the industry in 2011
• Sum Insured by postal code - grouped into 19 new cresta zones
• Split by line of business
• Exposure data modelled by cat modellers to get an industry view of the
1-in-200 year loss
• Simulated 50 000 event years
• Formulas from Solvency ll QIS 5 were applied
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Actuarial Society 2016 Convention 23 – 24 November 2016
SAM Earthquake Risk – Factors
Calculation Methodology
• Overall market factor: Determine the 1 in 200 year loss and divide it by the total exposure across the industry.
• Limitations
• Based on data from 5 years ago
• Data from larger players in the market would impact the results of
the simulated losses to a large degree
• Assumes that the market is a good proxy for your business
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Actuarial Society 2016 Convention 23 – 24 November 2016
SAM Earthquake Risk – Factors
Calculation Methodology
• Earthquake Factors LOB, LOB and Zone:
• 1 in 200 year loss for LOB (r) divided by the exposure for LOB (r)
• Proportion the loss across each zone by talking the average loss
in zone (i) divided by the average loss in all zones
• Limitations
• Assumes that the market data by zone is a good proxy for your
business
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LOB LOB and Zone
Residential Property Residential Property, Zone 1
Commerical Property Residential Property, Zone 2
Contents Residential Property, Zone 3
Engineering |
Motor Residential Property, Zone 17
Residential Property, Zone 18
Residential Property, Zone 19
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Actuarial Society 2016 Convention 23 – 24 November 2016
SAM Earthquake Risk – Factors
Calculation Methodology
• Correlation matrices LOB, LOB and Zone: Calculate the correlation matrix per LOB, LOB and Zone
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LOB
Residential
Property
Commercial
PropertyContents Engineering Motor
Residential Property
Commerical Property
Contents
Engineering
MotorCresta Zones 1 2 3 ---- 17 18 19
1
2
3
|
17
18
19
Residential Property
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Actuarial Society 2016 Convention 23 – 24 November 2016
SAM Earthquake Risk – Factors
Calculation Methodology
• Rescale Earthquake Factors LOB, LOB and Zone such that:
Market Factor * Exposure =
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Actuarial Society 2016 Convention 23 – 24 November 2016
SAM Earthquake Risk – Factors
Calculation Methodology
Motor: • Factors were based on model output for EQ Residential LOB and were
rescaled
• Expert consultation concluded EQ motor loss would be R1 billion in a 1-
in-200 year event
• Limitations
• Not as scientific as modelling motor directly
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Line of Business Risk Factor
Residential Buildings 137%
Commercial Buildings 90%
Contents 43%
Engineering 94%
Motor 80%
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Actuarial Society 2016 Convention 23 – 24 November 2016
Costs of a 1-in-200 Year Hail Loss
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Actuarial Society 2016 Convention 23 – 24 November 2016
Costs of a 1-in-200 Year Hail Loss
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Actuarial Society 2016 Convention 23 – 24 November 2016
Costs of a 1-in-200 Year Hail Loss
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Actuarial Society 2016 Convention 23 – 24 November 2016
Validation techniques
• Scenarios – reconstruct events on time of day/area.
• Back testing – actual events vs percentiles modelled
• Historic events – change in exposure to today
• ASSA industry exposure vs loss modelled
• Benchmarking
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Actuarial Society 2016 Convention 23 – 24 November 2016
SAM Hail Risk – Back-testing the
results
0bn
2bn
4bn
6bn
8bn
10bn
12bn
14bn
16bn
10 20 200
Ind
ustr
y L
oss
Return Period
SAM Hail Loss 28 Nov 2013 Hail Storm
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Actuarial Society 2016 Convention 23 – 24 November 2016
Hail – SAM vs European
parameters
• EU companies model mainly on country level, i.e. put full exposure
without diversification between risk zones.
• SA parameters on first impression seems conservative vs EU parameters,
e.g. Munich Zone only one higher than Joburg.
• Property SI weight to Non-Motor property is 20%, SA is 30% (? Check this)
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25000
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35000
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EU Market SA
“The ships hung in the sky in
much the same way that
bricks don't.”
― Douglas Adams, The
Hitchhiker's Guide to the
Galaxy
https://www.goodreads.com/author/show/4.Douglas_Adamshttps://www.goodreads.com/work/quotes/3078186
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Actuarial Society 2016 Convention 23 – 24 November 2016
Hail – European losses
• Source: AIR, Munich Re.
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Actuarial Society 2016 Convention 23 – 24 November 2016
Hail – scenarios
• Picture show GIS plot of hail formation, not
impact
• Hail tracks – usually not by Cresta zone
• Follows a track in the direction of wind
• Time of day key impact on loss sizes
• Model of scenario vs exposure
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Actuarial Society 2016 Convention 23 – 24 November 2016
Hail Scenarios
Nov. 28 2013 hailstorm : Total Loss of ~ R2bn (Bloomberg Report)
Centurion Car Park Scenario
Number of cars 4,500
Average Sum Insured 170,000 Total SI 765,000,000
Average Claim 30,000 Total Claim Amount 135,000,000
N1 Traffic Jam
Highway Length 45km Average Car Length 4.5m
Length Affected 20km Space Between Cars 3m
Number of lanes 8 Total Vehicles Damaged 21,333
Average Claim 30,000 Total Claim Amount 640,000,000
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Actuarial Society 2016 Convention 23 – 24 November 2016
SAM EQ Risk – Back-testing the
results
0bn
5bn
10bn
15bn
20bn
25bn
30bn
35bn
40bn
45bn
10 20 200
Ind
ustr
y L
oss
Return Period
SAM Hail Loss Actual Losses
Orkney ~ 2014
Mozambique ~ 2006
Welkom ~ 1976
St Lucia ~ 1932
Tulbagh ~ 1969
Milnerton ~ 1809
http://www.google.co.uk/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&uact=8&ved=0CAcQjRxqFQoTCNijs6ixl8kCFYIKGgodZEcDUA&url=http://capetownsquake.blogspot.com/2011/05/cape-town-earthquakes-and-potential.html&bvm=bv.107467506,d.d2s&psig=AFQjCNHCMn_hI-pJrBVstbsTdYY825m3WQ&ust=1447847198121017http://www.google.co.uk/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&uact=8&ved=0CAcQjRxqFQoTCNijs6ixl8kCFYIKGgodZEcDUA&url=http://capetownsquake.blogspot.com/2011/05/cape-town-earthquakes-and-potential.html&bvm=bv.107467506,d.d2s&psig=AFQjCNHCMn_hI-pJrBVstbsTdYY825m3WQ&ust=1447847198121017
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Actuarial Society 2016 Convention 23 – 24 November 2016
Benchmarking Earthquake loss
rates
• Johannesburg parameterisation for property more severe than Italy, R10,148
vs R8,000 per R1m exposure
• No EQ loss for Motor in S2, for SAM motor damage factor of 60% of residential
property and 88% of commercial.
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15,000
20,000
25,000
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Actuarial Society 2016 Convention 23 – 24 November 2016
Earthquake scenario modelling
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Actuarial Society 2016 Convention 23 – 24 November 2016
ORSA – validation of EQ losses
• More difficult to validate, but consider size of loss from EQ epicentre vs within large Cresta zone.
• http://earthquaketrack.com
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Actuarial Society 2016 Convention 23 – 24 November 2016
Contact details
Hannes van Rensburg
Bridget Bernon
Carla Fasana
mailto:[email protected]:[email protected]:[email protected]