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Big Data – Creating New Underwriting Opportunities VNAB 17th March 2015

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Agenda

• Introduction

• Increased competition

• Importance of accurate data

• A growth opportunity through increased insight

2

The latest in a long line of buzzwords

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Beyond the buzz

• Identify new opportunities, deliver new service

• Improve internal efficiency • Strategic overview, cross sell to existing

clients

• Consider the accuracy of data

Data types and sources

Meteorological data

Mixture of internal and external data sources

Property

Motor

Health

Census data

Real estate data - Kadaster

Credit history

Location data – mobiles / GPS

Social media – Facebook, Twitter, Google+

Sales records

Claims history

Probabilistic / historic modelling

HR records

Financials

• Partnerships and alliances • Access to external data • Skills and capabilities

Telematics

Wrist bands

4

Increased competition

5

Growth in reinsurer capital continues to out-pace demand, rising by 6% to a new high of USD575bn at September 30, 2014

$575bn of reinsurance capital chasing $320bn of demand Interest in directing capital towards Insurance arena increasing Additional parties such as Google+ entering insurance space

Competition increasing

17 22 19 22 24 28 39 50 62

368 388

321 378

447 428 466

490 513

6% -17% 18%

18%

-3% 11%

7%

6%

385 410

340

400

470 455

505 540

575

0

100

200

300

400

500

600

FY 2006 FY 2007 FY 2008 FY 2009 FY 2010 FY 2011 FY 2012 FY 2013 9M 2014

USD

(bill

ions

)

Traditional CapitalAlternative CapitalGlobal Reinsurer Capital

USD 23bn

USD6bn USD4bn

USD 29bn

Catastrophe Bonds

Sidecars Industry Loss Warranties

Collateralised Reinsurance

Alternative Capital

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Opportunity increasing

0

50

100

150

200

250

300

350

400

450

500

1980 1985 1990 1995 2000 2005 2010

USD

bill

ions

(201

4 pr

ices

)

Insured Loss (2014 USD) Economic Loss (2014 USD)Source: Impact Forecasting (all figures inflation-adjusted)

258 catastrophe events in 2014

USD 132billion economic loss USD 39billion insured loss

Increased risk understanding delivers new opportunities, such as Netherlands Flood

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Importance of accurate data

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Property opportunity Increasing risk understanding – which data? Physical and financial information related to a risk

General Information

Primary Risk Characteristics

Secondary Modifiers

Financial Information

Sums Insured

Geographic Location

Coverage Type

Occupancy Construction Year Built

Number of Storeys

Roof Shape and Construction

Building Shape

Deductibles Limits

Co-insurance

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Property opportunity Current quality – UK vs Netherlands

Sums Insured

Detailed Geographic

Location

Coverage Type

Detailed Occupancy

Construction Year Built

Number of Storeys

Roof Construction Building Shape

Deductibles Limits

Co-insurance

Sums Insured

Detailed Geographic

Location

Coverage Type

Detailed Occupancy

Construction Year Built

Number of Storeys

Roof Shape and Construction

Building Shape

Deductibles Limits

Co-insurance

What is driving this quality mis-match? Influence of internet buying? Impact of Agency underwriting data sources?

UK Netherlands

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Catastrophe modelling – Risk insight AIR (Applied Insurance Research) (Verisk Analytics business unit) founded in 1987; over 80 countries covered

EQECAT (acquired by CoreLogic in 2013) founded in 1994; over 90 countries covered

RMS (Risk Management Solutions) (DMGT group company) founded in 1988; over 80 countries covered

Impact Forecasting Aon Benfield subsidiary 50+ available models Originally, to cover gaps in peril coverage available from vendor models and emerging perils Now, the main stake is on transparency and bespoke solutions

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Property opportunity Importance of accurate location data

AAL

250

1000

LATLONG FLSTREET FL

FULL POSTCODE FL4 DIGIT FL

CRESTA FL

Influence of Geographic Resolution on River Flood Loss Ratio

AAL

250

1000

AAL

250

1000

LATLONG WSSTREET WS

FULL POSTCODE WS4 DIGIT WS

CRESTA WS

Influence of Geographic Resolution on Windstorm Loss Ratio

AAL

250

1000

Coordinate Street address

6 digit Postcode

4 digit postcode City CRESTA

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Client A Modelled losses for Germany river flood doubled in 2013 without any obvious change in exposure Investigation revealed – the same industrial risk was recorded with postcode resolution in 2012 data

and was refined to exact address in 2013 …

Real case – how important it is to get this right!

Actual risk is located here

2012

2013

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Inaccurate construction data could impact underwriting price, capital requirement and loss potential by 50%+ in above example

Property opportunity Importance of accurate construction data

-100%

-50%

0%

50%

100%

150%

NL_Wood frame NL_Masonry NL_Reinforced masonry NL_Reinforced concreteImpa

ct o

n Lo

ss E

stim

ate

Territory and Construction

Impact of Construction (NL Commercial) - 1 in 200 year loss

AIREQERMS v11RMS v9

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Height of property especially important for flood but can significantly impact windstorm loss potential as well

Property opportunity Importance of accurate height data

-100.00%

-80.00%

-60.00%

-40.00%

-20.00%

0.00%

20.00%

40.00%

NL_1 Storey NL_2 Storey NL_5 Storey NL_10 Storey NL_20 Storey

Impa

ct o

n Lo

ss E

stim

ate

Territory and Storeys

Impact of Storeys (NL Residential) - 1 in 200 year loss

AIREQERMS v11RMS v9

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Our objective is to support you in developing complete, accurate and appropriate exposure data

Benchmarking We will conduct benchmarking studies to advise on accuracy and quality of data compared to peers

Data enhancement We can further enhance the key data characteristics of your data, such as building height and size, using our access to third party building resolution dataset for the Netherlands. This data can help to further refine your windstorm and flood PML’s

Property - Increasing risk understanding through partnership

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Property – Benefits of increased insight

Combining enhanced data with catastrophe modelling will lead to more accurate original pricing, PMLs, reinsurance requirements and enhanced capital efficiency

Additionally we can identify locations that are under or over weight in your portfolio

Generate modelled burning costs (annual average loss) by peril by location to identify locations that are lowest or highest exposed to loss potential

Target current areas of low exposure and low loss potential for growth

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A growth opportunity through increased insight

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Property – Netherlands flood – a new opportunity for growth 1st market available probabilistic model Model characteristics Both probabilistic and scenario model

Overtopping and/or failure of primary defenses of 57 dyke rings

Covers dyke failure mechanisms such as overflow, wave overtopping, piping, inward macro instability, damage to cover and erosion of levee core, dune erosion and failure of hydraulic structures (overflow/overtopping, failure to close, seepage, structural failure)

Not considering precipitation or sewer system overload

Covers building, content, BI, motor hull

Modeling aspects NL heavily affected by man-made modifications Cannot be modeled in a ‘common’ way Small amount of historic loss data External cooperation with local experts vital (HKV and Deltares) and local

sources of information used

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Netherlands flood – Event set One million years of events simulated

Probabilistic and historic event modelling

Each event can be visualised and interrogated

Model can be bespoked to fit specific portfolio characteristics

Events producing floods are stored in the event set

Event ID Occurence DR1 DR2 DR3 DR4 DR5 DR6 DR7 … DR11 DR12330 5137_S1 2.08E-06 1 1 0 0 1 219 0 … 0 71

In event 330, there is flood in 5 dyke rings (1,2,5,6,12). In dyke ring 6 scenario 219 applies (water source is Wadden Sea), in dyke ring 12 scenario 71 applies (breach location near Holwerd). Right map shows affected postal codes.

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Netherlands flood – Visualising and adjusting for Uncertainty The Impact Forecasting model has 3 main views of potential flood extent by line of business

(including motor casco – Default, Optimistic and Pessimistic

Additional Uncertainty options can also be explored within the Elements platform to identify impact of data accuracy:

10 25 50 75 100 150 200 250 500 1000

Commercial Realistic Commercial Optimistic Commercial Pessimistic

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Big data - Conclusion

The influx of alternative capital and data rich companies such as Google+ will lead to increased competition and further pricing pressures Thinner margins means the ability to accurately price risks and identify profitable growth more important than ever

The insurance industry is sitting on a wealth of data and unparalleled market expertise The new capital is already harnessing data analytics but lacks underwriting and market expertise

Could the use of social media generated data be biased and / or less than accurate? Do specific types of people use and post data online?

Partnerships will be key to data enhancements, modelling and more accurate risk assessment Ability to draw down on outputs for use in targeting growth opportunities, improved pricing and more accurate capital and

reinsurance needs

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