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

14 October 2021 2

Introduction Data Cleaning

Example of

incorporating

external data

Data Visualisation

Internal &

External data

Conclusion

10/14/2021 4Global Consulting

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?

Please visit: www.menti.com

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:


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


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


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

10/14/2021Insurance pricing: where prediction meets the business 11

Do you know the difference between internal

and external data?




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


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


…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


Risk Modelling


Microzoning

Risk Network Analytics

Can you give an example of external

data source?



Marginal Gains in Risk AssessmentGetting a competitive advantage with external data

XX Août 2021 18

Driver’s age

Vehicle make NCD

Region

Engine Size

Driver‘s

marital statusGarage type

Number of claims in

the last 3 years


XX Août 2021 20

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


XX Août 2021 21

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


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

23

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)



Fatalities: 1,000

Floods 1987

South Africa



Fatalities: 487Wildfires 2008

South Africa

Overall losses*: US$ 430

Fatalities: 34

Drought 1990 – 1993

Southern Africa (esp. South Africa)


Floods Dec 2010 – Feb 2011

Southern Africa (esp. South Africa)



Fatalities: 198

Tornado 1990

South Africa



Fatalities: 2

Severe storms, floods 2012

South Africa



Fatalities: 11

Hailstorms, flash floods 2013

South Africa, Swaziland



Fatalities: 2

Limnic eruption Lake Nyos 1986

Cameroon


Fatalities: 1,746

Floods 2012

Nigeria


Fatalities: 363

Earthquake (series), tsunami 2003

Algeria



Fatalities: 2,200

Earthquake 1980

Algeria


Fatalities: 2,590

Floods, flash floods 2002

Morocco



Fatalities: 63Drought 2000

Morocco


24

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


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


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

21 October 2020Global Consulting 29

• 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

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