Incorporating internal and external data into short ...
Transcript of Incorporating internal and external data into short ...
12 October 2021
Siobhan Reddy and Dembe Netshipale
Incorporating internal and external data into short
term insurance pricingData Analytics
Agenda
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Introduction Data Cleaning
Example of
incorporating
external data
Data Visualisation
Internal &
External data
Conclusion
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Technical Sophistication is at the centre of insurers’
challenges
Technical Sophistication
Technical Sophistication is the Differentiating Factor
De-intermediation via
Online & Digital Channels
Breakthroughs in Modelling
Techniques (ML & AI)
Disruptive Players’ Entering
the Market
Insurers’ Focus for non traditional
insurers
Increased Agility due
to IT Flexibility and new platforms
Increased Customer Awareness
and Price Sensitivity
Why do we clean the data?
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Use the code: 8983 7786
Ingredient 1Clean information
The aim is to generate a clean dataset to work with in the Pricing Process. This is what the start looks like:
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Getting from
here…
…to here.
Mercedes Mercedes Benz
Mercedes Benz Mercedes Benz
mercedes Mercedes Benz
Merdeces Mercedes Benz
B.M.W. BMW
BMW BMW
bmw BMW
The transformation of the data is key
to achieve reasonable results.
Ingredient 2Clean policy dataset
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Exposure – days of cover per calendar year/365 (or 366)
01.01.2013
Start of cover
01.04.2013
End of cover
31.03.2014
01.01.2014 01.01.2015
Exp 2013 = 0.75 Exp 2014 = 0.25
Earned Premium = Exposure × Paid Premium
Polnum Start of Validity End of Validity Premium
465662 01/04/2013 31/03/2014 10.000
Polnum Exp Year Exposure Earned Premium
465662 2013 0.75 7.500
465662 2014 0.25 2.500
Policy life-time
The goal is to create one line per risk per calendar year:
Data AnalysisThe first step towards the technical excellence
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ResultsProcess
One-way analysis
Trend-way analysis
Detailed statistical description of the
variables available
Detailed analysis of the portfolio mix
distribution
Trend comparison between risk
premium and tariff premium
Policy Database
Claim Database
External database
Policy-claim Database
o Frequency
o Severity
o Risk Premium
o Average Premium
o Loss Ratio
o Discount
Pricing Sophistication requires…
Data Prediction Decision
making
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Do you know the difference between internal
and external data?
Please visit: www.menti.com
Use the code: 8983 7786
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Data is key for successful modelling
Internal
DataExternal
data
Policy data
Claims data
External Data – Structurede.g., Population Density
External Data – Unstructurede.g., claim description
Modelling Dataset
Prediction
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Leveraging structured internal information…
Customer
▪ Age
▪ Gender
▪ License’s age
▪ Marital Status
▪ Occupation
▪ Previous Company
▪ Tenure
▪ Private/Commercial
▪ Bonus Malus
▪ …
Vehicle
▪ Make
▪ Model
▪ Vehicle’s age
▪ Horse Power
▪ KW
▪ Cubic Capacity
▪ Fuel type
▪ Sum insured
▪ …
Claim’s History
▪ Number of Claims
▪ Paid Amount
▪ Cover Affected
▪ Reserve
▪ ALAE
▪ …
Territorial/Economical
▪ Zip-Code/Postal Code
▪ Region
▪ Payment Method
▪ Instalment
▪ …
Policy data Claims data
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…and external information to improve the pricing process
Data Enrichment
Vehicle Territorial/Economical Risk Contextualization Benchmark
▪ Road riskiness
▪ Accidents
▪ Traffic
▪ Road works
▪ Market prices
▪ Benchmark variables▪ Ranking
▪ Distance (Eur/ Percentage)
▪ …
▪ Financial – demographic Score
▪ Revenue information
▪ Geo – demographical info▪ Population Density
▪ Life Expectancy
▪ Crime Rates
▪ Cars per Inhabitant
▪ Injured per Accident
▪ …
▪ Technical characteristics ▪ Make
▪ Model
▪ Horse Powers
▪ KW
▪ Fuel type
▪ Value
▪ Max speed
▪ ADAS
▪ …
Application
Competitive Market Analysis
Behavioural Modelling
Rate making
Risk Network Analytics
Risk Modelling
Telematics
Fraud Identification
Data cleaning
Risk Modelling
Behavioural Modelling
Risk Modelling
Behavioural Modelling
Microzoning
Risk Network Analytics
Can you give an example of external
data source?
Please visit: www.menti.com
Use the code: 2154 4591
Marginal Gains in Risk AssessmentGetting a competitive advantage with external data
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Driver’s age
Vehicle make NCD
Region
Engine Size
Driver‘s
marital statusGarage type
Number of claims in
the last 3 years
Marginal Gains in Risk AssessmentGetting a competitive advantage with external data
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Driver’s age
Vehicle Brand Vehicle Value
NCD
Postcode Channel
Engine Size
Driver‘s
marital statusGarage type
Number of claims in
the last 3 years
Marginal Gains in Risk AssessmentGetting a competitive advantage with external data
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Driver’s age
Vehicle Brand Vehicle Value
NCD
Postcode Channel
Crime Rate Credit Score
External data allow for
marginal gains in risk
assessment and
market over-performing
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Risk modellingIllustrative granularity of motor products
Sophis
tication
Freq SevPropen
sityFreq Sev Freq Sev SevFreq
Product
Risk
Premium
Bodily
Injury
Material
DamageCollision
Fire &
Theft
TPL Own Damage
Comprehensive
ExcessAttritional
Cover
Peril
Policy data + Claims data
+ External data
GLM or ML modelling
Risk pricing at
policyholder level
Process
NatCatService: Loss events in Africa 1980
– 2013 Geographical overview
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Loss events
Selection of
catastrophes
© 2014 Münchener Rückversicherungs-Gesellschaft, Geo Risks Research, NatCatSERVICE – As at March 2014
* Losses in original values
Source: Munich Re, NatCatSERVICE, 2014
Floods 1997
Somalia
Fatalities: 2,000
Earthquake 1992
Egypt
Overall losses*: US$ 1,200m
Fatalities: 561
Floods 1988
Sudan
Overall losses*: US$ 66m
Insured losses*: US$ 2m
Fatalities: 8,000
Floods 2000
Southern Africa (esp. Mozambique)
Overall losses*: US$ 520m
Insured losses*: US$ 50m
Fatalities: 1,000
Floods 1987
South Africa
Overall losses*: US$ 520m
Insured losses*: US$ 250m
Fatalities: 487Wildfires 2008
South Africa
Overall losses*: US$ 430
Fatalities: 34
Drought 1990 – 1993
Southern Africa (esp. South Africa)
Overall losses*: US$ 1,400m
Floods Dec 2010 – Feb 2011
Southern Africa (esp. South Africa)
Overall losses*: US$ 435m
Insured losses*: US$ 5m
Fatalities: 198
Tornado 1990
South Africa
Overall losses*: US$ 380m
Insured losses*: US$ 115m
Fatalities: 2
Severe storms, floods 2012
South Africa
Overall losses*: US$ 250m
Insured losses*: US$ 140m
Fatalities: 11
Hailstorms, flash floods 2013
South Africa, Swaziland
Overall losses*: US$ 200m
Insured losses*: US$ 110m
Fatalities: 2
Limnic eruption Lake Nyos 1986
Cameroon
Overall losses*: US$ 25m
Fatalities: 1,746
Floods 2012
Nigeria
Overall losses*: US$ 500m
Fatalities: 363
Earthquake (series), tsunami 2003
Algeria
Overall losses*: US$ 2,500m
Insured losses*: US$ 10m
Fatalities: 2,200
Earthquake 1980
Algeria
Overall losses*: US$ 3,000m
Fatalities: 2,590
Floods, flash floods 2002
Morocco
Overall losses*: US$ 200m
Insured losses*: US$ 140m
Fatalities: 63Drought 2000
Morocco
Overall losses*: US$ 900m
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Case study: FISP – Smallholdings in Zambia
The
Expertise
▪ Unique set-up of a combined team of agriculture specialists, underwriters, structuring experts and data
analytics team each focusing on single aspects (trigger development, risk assessment, legal and regulatory
aspects, etc.)
▪ Remarkable track record with indemnity and non-indemnity transactions and thus experience with a wide
range of perils, trigger solutions and market & regulatory environments
▪ Collaboration with local reinsurer with expertise offering acts as domestication breaker
▪ Agricultural product that covers smallholding farmers through The Farmer Input Support Program (FISP)
▪ Pays out in the event of drought or excess rainfall which would result in a loss of crop and revenue
▪ The cover makes use of dekads (10-day periods) from which rainfall is observed
▪ Index developed using weather data from reputable independent data provider
▪ Indices calculated per dekad (10 days) based on rainfall per geographic region
▪ Maximum threshold for index – indicates excess rain
▪ Minimum threshold for index – indicates drought
The Index
The
Product
Data Visualisation
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illustrative
Select variable to be visualized
and respective modalities
Choose from a set of filters
Overview of the most relevant KPIsSelect the period of interest
Analyse the Reported Loss
Ratio in the different
portfolio segments - with and
without large losses
Understand claim frequencies
and severities
on peril or cover level
Understand the average
premiums and risk per segment
Data Visualisation
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illustrative
See the selected segment
Go back to the analysis
Understand the development
of average premiums and risk
Analyse the profitability over time
- with and without large losses
Understand developments of
claim frequencies
and severities
on peril or cover level
Overview of the most relevant KPIs of the selected segment
Conclusion
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• In modern systems internal and external data is the backbone. Therefore, insurer needs to
build up on this information
• Changing data and technical landscapes, combined with markets development are source
of new challenges for insurers
• It is therefore, key to be capable to make the most of this information to stay ahead in the
sophistication race
Thank you!
October 2021
Siobhan and Dembe
Your feedback matters
Please visit: www.menti.com
Use the code: 5569 9372