exposure management working group · 2020. 2. 20. · • earthquake factors lob, lob and zone: •...

Actuarial Society 2016 Convention 23 – 24 November 2016

Exposure Management Working Group

Bridget Bernon, Carla Fasana, Hannes van Rensburg


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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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

3


Recent Catastrophic Disasters

• New Zealand Earthquake



• Italian Earthquakes



• UK Floods and Brexit



• Japanese Quakes


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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0

50

100

150

200

1 2 3 4 5 6 7 8 9 10 11

Natural catastrophes vs springbok win %


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…

9

• 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


Interest for insurers

1. Natural perils impact property

• Frequency

• Severity

2. Exposure and Values – e.g. heat bubble

3. Time

4. Other impacts -

Infrastructure

10

“The only reason for time is that everything

does not happen at once.”

― Albert Einstein


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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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


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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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.


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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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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0

500

1000

1500

2000

2500

3000

0.00%

0.10%

0.20%

0.30%

0.40%

Claim Frequency vs Crime Rates

Exposure Claim Frequency


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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Market Data Providers

Subsidence Layer


SAM Non-Life Underwriting Risk SCR

19


SAM Catastrophe Risk

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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


Audience Poll

• Which method drives your Nat Cat capital charge?

A. Vertical earthquake

B. Vertical hail

C. Horizontal events

D. Don’t know


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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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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• 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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• 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




• 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




• Rescale Earthquake Factors LOB, LOB and Zone such that:

Market Factor * Exposure =

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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%


Costs of a 1-in-200 Year Hail Loss

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31



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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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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


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)

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

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


Hail – European losses

• Source: AIR, Munich Re.


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


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


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


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.

-

5,000

10,000

15,000

20,000

25,000


Earthquake scenario modelling


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


Contact details

Hannes van Rensburg

[email protected]

Bridget Bernon

[email protected]

Carla Fasana

[email protected]
mailto:[email protected]:[email protected]:[email protected]

exposure management working group · 2020. 2. 20. · • earthquake factors lob, lob and zone: •...

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