Preparing Analytics for IoT Revolution

Preparing Your
Analytics Divisions for
The IoT Revolution
Presentation by Hartford Steam Boiler
Inspection & Insurance
CDAO Fall Insurance Focus Day
November 18th, 2019
Image: Getty/Thinkstock© 2017 The Hartford Steam Boiler Inspection and Insurance Company. All rights reserved.

50
Explosive IoT Growth
© 2017 The Hartford Steam Boiler Inspection and Insurance Company. All rights reserved.
IOT
BILLION
Trillion will be spent on
IoT solutions over the
next five years
$6
Connected devices
by 2020
Source: “IDC’s Worldwide Internet of Things Taxonomy, 2015,” IDC, May 2015;
“Worldwide Internet of Things Forecast, 2015 – 2020,” IDC, May 2015.
Amount of Data Generated (in exabytes)
Source: Cisco white paper, Business Insider report
11/22/19 2
Only 10% of analytics leaders in insurance believe they are ready
for this seismic change - are you one of them?

The Good, Bad, and Ugly
Growth and Profit
§ Business Insight
§ Operational Efficiencies
Complex
§ Technology
§ Logistics and Scalability
§ Customer Experience
§ Onboarding
Cost
§ Capital Investment
§ Resources (people, IT systems, etc.)

4
© 2019 The Hartford Steam Boiler Inspection and Insurance Company. All rights reserved. HSB
Confidential and Proprietary
The Journey
Requirements to work with clients and maximize value

Technology
11/22/19 5
HSB’s Meshify IOT platform
Our custom combination of
hardware configuration kits,
monitoring software, and
algorithms, including Meshify,
allows you to be at the forefront of
IoT technology that works.
Additional Points:
Ø Data can also be obtained
through API’s
Ø 3rd party data
What is needed to obtain data used for analytics?

Logistics and Scalability
11/22/19 6
HSB engineers testing a connected water shutoff valve
From use case and sensor
placement to inventory and
program management, think of us
as a part of your IoT team with
engineers, developers, marketers,
and operations staff
How does it all go together?

Customer Experience
Collateral Website
Videos
We’ve accounted for the end-to-end
customer journey from the moment
of signup to install to alert
notification, including a customized
app and 24/7 support
If we build it will they like it?

Onboarding
11/22/19 8
HSB pilot planning workshop
Our IoT FastTrack onboarding
model allows you to start small and
lean, so you can grow your IoT
program in a stepwise fashion. We
want it to be easy and hassle free
How do we start the process?

9
The Rewards of the Journey
Unprecedented level of insight

Data & Analytics
11/22/19 10
Alert notification from HSB platform
We provide your customer with the
data-driven insights that allows
them to better understand their
business and insurance needs (and
for you to have that conversation
with them)
In addition to reducing risk:
Ø Insurer can gauge customer
behavior
Ø Insured can improve operational
efficiency
What do my clients get out of it?

Weather & Geospatial
Continuous Innovation
IoT Analytics Use Cases
11
Risk Modeling
Averting Losses
Detecting Equipment Issues
Energy Savings

12
Risk Modeling
Setting the stage for IoT

Water Damage Risk Modeling
13
Frequency and severity
combine to provide a total risk
estimate at the building,
location, and/or account level.
Uses statistical modeling and
machine learning techniques.
Can include or exclude CAT
events (client’s choice).
All relevant variables are used
in the models.
Freeze risk model is the same
as the no-freeze risk model, but
includes a freeze stress index
for each location, for each day
under freezing conditions.
Severity
Component
Magnitude of
losses ($)
§ Prefer at least 3
years of client’s
policy (exposure
and building
attributes) and
claims data
§ Risk scores can
be combined with
HSB’s EB
Location Risk
Scores
Water Damage
(no freeze)
Risk Model
Loss count
and exposure
Water Damage
w/t Freeze
Risk Model
Frequency
Component

Equipment Breakdown Location Risk Score
14
Location
Risk Score
Model
§ Incorporates a unique combination of
publicly available and proprietary data
§ Blends classic statistical models and
various machine learning techniques.
Credit Risk
Data
Census
Data
Engineering
Data
Geographic
Data
Location/
Claim
Data
Equipment Breakdown (EB) Risk Score has demonstrated a strong correlation with property/liability risk and so can be combined with the IoT risk models.
With the successful roll-out of EB, Water Damage (no-freeze) and Freeze Loss risk scores, we are expanding to other property perils such as Building Fire Risk as part of
our IoT sensor site selection process.
Assess
Location level risk (by
type/peril) by using enriched (26
million) location records and
combining advanced modeling
techniques with deep
engineering expertise.
Predicts
The likelihood that a location will
have an equipment breakdown
loss, which is strongly correlated
to property/liability risk.
Produces
Location level risk scores for
HSB and our client's UW, Loss
Control and other Engineering
Risk Management processes.
Exhibits
Great performance in prediction
accuracy and segmentation
power as demonstrated in both
HSB and their client's UW &
Engineering use cases.

15
Weather/Geospatial
Augmenting sensor
data with detailed
weather and
geospatial data

December 2017 Severe Cold Event
Temperature and Alerts
Note the high correlation between the alerts and daily average temperature.
BELOW 32F
(COLD)
ABOVE 32F
(WARM)
16

17
Losses Averted Analysis
Using data to identify
actions taken to
avoid loss

Dissecting the Data
18
BestWorst
Who is responding?
Does the time of day, week
or year impact response?
Does geography make a
difference?

Probable Save
Alert
Indoor Temperature
Outdoor Temperature
Temperature by Date/Time
Losses Averted Analysis
19
Temperature
Power
0
1
40
30
50
60
70

20
Detecting Equipment Issues
Monitor trends to
identify abnormal
operation

Identifying Abnormal Behavior
21
Using data to “train” the model to identify a possible failure event
Training period (blue)
Operational
monitoring (green)

22
Identifying Anomalies
Detail view of possible failure event

23
Energy Savings
Using data to
find opportunities
to save

Energy Savings Analysis
§ By comparing temperatures
(indoor vs. outdoor) at
different time periods, we can
identify potential opportunities
for energy savings
• Unnecessary
heating/cooling?
• Erratic heating/cooling?
§ We are piloting an “Energy
Opportunities Report” for
each insured location
• We typically know very
little about the location, so
use Analytics to make
inferences
24
Distribution of Energy Flux
Flux (Q, degF/hour)

25
IoT Analytics
Continuous Innovation

The Munich Re Data Lake
Over 500 Terabytes and Growing Every Day
22 November
2019
§ Munich RE has an entire team dedicated
to finding new sources of data…
§ Datasets relevant to IoT
• Sensor Readings and Metadata
• Claims Data
• Demographic Data
• Building Attributes
• NFIRS Fire Event Database
• Weather Simulation and Observation
Data
• ... And much more!
26

27
We continually innovate to improve our clients IoT experience
Conduct controlled
failure experiments to
build out a library of
equipment faults.
Using real-time and
forecast data to
enhance our risk
models.
Building models to
expand knowledge and
insight and develop
insurance based risk
solutions
Developing a
comprehensive
analytics and reporting
platform to deliver
additional benefits to
our clients.
Research and Development

Preparing Analytics for IoT Revolution

Recomendados

Recomendados

Más contenido relacionado

La actualidad más candente

La actualidad más candente (20)

Similar a Preparing Analytics for IoT Revolution

Similar a Preparing Analytics for IoT Revolution (20)

Más de Molly Alexander

Más de Molly Alexander (9)

Último

Último (20)

Preparing Analytics for IoT Revolution