How Data Science Can Help Insurers Understand Wildfire Risk · 2019-03-06 · May 3, 2018 AICP...

Proprietary & Confidential: Weather Analytics 2018

Wildfire AnalyticsHow Data Science Can Help Insurers Understand Wildfire Risk

May 3, 2018

AICP Conference


Today’s Conversation

• Why We’re Here▪ Lookback to the paradigm shift of 2017

• How Wildfire Risk is Changing▪ Ignition and propagation conditions for wildfire

▪ The role of development and climate change in wildfire

• How Data Analytics Support Smart Adaptation▪ Robust risk assessments through opportunistic use of technology

2Proprietary & Confidential: Weather Analytics 2018 2


Overview of the 2017 Wildfire Season



2017

Wildfire: a large, destructive fire that spreads quickly over woodland or brush

In 2017, California suffered its most destructive wildfire season in state history

• Acres burned: 1.3 million

• Structures destroyed: 10,000 +

• Insured losses: ~$14 billion

• Fatalities: 46

4Proprietary & Confidential: Weather Analytics 2018


More wildfires in winter months

December U.S. Wildfires (2000-2017)

5


More wildfires in winter months

Year-to-Date U.S. Wildfires (2000-2017)

6


Wildfires have significant downstream effects that are equally impactful to policyholders as damage caused by flames

• Wind-borne embers

• Near-surface smoke damage

• Business interruption

• Long-term health impacts

Proprietary & Confidential: Weather Analytics 2018 7


Trends Driving Wildfire Risk



What causes wildfires?

Most frequently, us.

• During 1992-2012, humans accounted for 84% of all wildfires and 44% of all acres burned

• Human-caused fire season 3x longer than lightning-caused fire season


Proprietary & Confidential: Weather Analytics 2018 Source: Anchorpoint

Historical fires


10


• Conditions required for ignition are simple: fuel, heat, and oxygen

• Conditions that drive wildfire growth are complex

o Topography, land cover, and weather are the major determinants of wildfire growth

• Topography: the steeper the slope, the more rapidly and intensely the fire will burn up-slope

• Land cover: the type of vegetation will affect how quickly wildfire spreads and how long the wildfire burns

• grasses fuel fast growth but short duration; old timber fuel slower growth but longer duration

• Weather: high winds, low humidity, drought all enable faster-moving wildfires

Wildfire growth

11

Proprietary & Confidential: Weather Analytics 2018 1 based on survey of Redfin housing and FEMA risk data

• Continued real estate development in the wildland-urban intermix—the exurban region where undeveloped land intermingles with housing

• Often this development occurs in landscapes prone to wildfire

• 7.7% of US homes—1.5 trillion USD in value—now at risk of wildfire damage1

• Climate change is driving more frequent extreme events that favor wildfire ignition and growth

• Rising temperatures dry vegetation faster

• Lightning occurs more frequently in hot weather

• Earlier springs prolong the fire season

How wildfire risk has changed

13


Minimal likely exposure

Intermix

Interface

Wildland



0

20

40

60

80

100

120

140

160

180

200

NV AZ UT CO ID TX NM WA OR MT CA WYChange

in N

um

ber

of

Hom

es

in W

UI

(%)

Real Estate Development in Wildland-Urban Interface Regions,1990-2010

Real Estate Development

15

Proprietary & Confidential: Weather Analytics 2018Proprietary & Confidential: Weather Analytics 2018

Increased frequency in rainfall extremes…

16


Increased frequency in rainfall extremes…

17


…and increased intensity of winds

18


How Data Analytics Support Smart Adaptation



• Local weather can drive drastic fluctuations in wildfire conditions

• These fluctuations often occur over the span of hours, leaving little time for organized response

• Accessing and disseminating reliable forecasts on local weather conditions is key to mitigating losses

• NOAA’s High Resolution Rapid Refresh (HRRR) produces quality, actionable information for stakeholders

o 3 km (1.9 mi) spatial resolution

o Next 18-hour outlooks updated in hourly releases

o Radar observations assimilated into hourly updates

High resolution weather forecasts

20


Area under mandatory evacuation noticeAlert Issued at 12/6/17, 11:00pm

As was the case during the Thomas Fire, the difference in time between forecasts and official alerts can often be more than 12 hours

• At 4am on December 6th, 50+ mph wind gusts were forecasted for later in the day at the northern perimeter of the fire

• At 11pm on December 6th, after conditions had already deteriorated, a mandatory evacuation was issued for the highlighted area

Access to hyper-local weather forecasts extends the critical decision window for first responders, enabling alerts and evacuation notices to be issued in real-time

Smarter Alerting


Proprietary & Confidential: Weather Analytics 2018Proprietary & Confidential: Weather Analytics 2018 22

22


• Intelligent wildfire risk assessments require gap-free historical data

• Blending ground-based weather stations and remote sensing instruments allows for a fuller picture of past events

• Insurance carriers can use this data to assess overall risk of a location, as well as identify how risk has changed over time

• Having gap-free historical data enables analytics teams to identify important underlying patterns

o I.e., identifying changes to vegetative biomass caused from changing precipitation patterns

High quality historical data

23


Extreme Temperature Index, 1980-1985

1980 1985 1990 1995 2000 2005 2010 2015

24



1980 1990 1995 2000 2005 2010 20151985

25



1980 1985 1995 2000 2005 2010 20151990

26



1980 1985 1990 2005 2010 20151995 2000

27



1980 1985 1990 1995 2010 20152000 2005

28



1980 1985 1990 1995 2000 2010 20152005

29



1980 1985 1990 1995 2000 2005 2010 2015

30


Extreme Temperature Index 1980-1985 compared to 2010-2014

1980-1985 2010-2014

31


Extreme Precipitation Events OverviewExtreme Rain Index 1980-1985 compared to 2010-2014

1980-1984 Average 2010-2014 Average

32


Plant Disease Susceptibility Index 1980-1985 compared to 2010-2014

1980-1984 Average 2010-2014 Average

33

Proprietary & Confidential: Weather Analytics 2018 Source: Deloitte Unitersity Press | DUPress.com

Cloud computing trends

Computing cost-performance (1992-2012)

34


The past few years has seen rapid pace of innovation in data analytics

35

Cloud computing and deep learning

• “Deep learning” is the technology behind facial recognition and self-driving cars

• Deep learning algorithms require large amounts of data

• Until recently, the value of deep learning remained theoretical, constrained by high computing costs and insufficient amounts of data

• Cloud computing resources are now a commodity

• Available data only continues to grow with advances in remote sensing

35


©2018 Weather Analytics, LLC; Proprietary & Confidential

Integration of pre/post-event imagery with deep learning

provides carriers with actionable risk outlooks and data

Big Data platforms reduce the latency in post-event reporting

Carriers can quickly identify and assess areas with high volume of

impending claims


Situational awareness: Canyon 2 fire

37


Extensive historical data enable risk

profiling and trend analyses

Bringing it all together

Remote sensing innovations empower

carriers with real-time wildfire

footprints

High-resolution weather forecasts give

highly actionable outlooks for

proactive risk mitigation

Deep learning programs have the potential to enhance all of the above

38


Data fusion and predictive modeling provides insurance carriers with powerful decision-making and risk assessment tools for writing and renewing policy in at-risk areas

However, many industry models rely on a narrow set of variables and/or focus on exposure management, resulting in limited capability for real-time response and forecasting


Data fusion enables more informed underwriting

39


Wildfire Analytics

In the midst of catastrophe, actionable and accurate data can make all the difference for both emergency responders and insurers.

Weather Analytics is committed to working with insurance carriers to enable actionable, data-driven decision-making before, during and after wildfires.

40


Questions?

weatheranalytics.com

[email protected]

How Data Science Can Help Insurers Understand Wildfire Risk · 2019-03-06 · May 3, 2018 AICP...

Documents

Transcript of How Data Science Can Help Insurers Understand Wildfire Risk · 2019-03-06 · May 3, 2018 AICP...