lima primary insurance pricing · henry receives a proposal of 330 € he will renew he will not...

Massimo Cavadini – 14 October 2020

PRIMARY INSURANCE PRICING

where prediction meets the business

“The unprecedented availability of structured

and unstructured data, cloud computing and

machine learning algorithms are bringing the

pricing sophistication to the next level.”

Massimo Cavadini

Head of Actuarial Consulting and Data Analytics

Munich Re

Why pricing is so important now?

Technical Sophistication

Technical Sophistication is the Differentiating Factor

LIMA Programme 2020

De-intermediation via

Online & Digital Channels

Breakthroughs in Modelling

Techniques (ML & AI)

Disruptive Players’ Entering

the Market (e.g., Lemonade)

Insurers’ Focus on Technical Income

due to Interest Rates Decrease

Increased Agility due

to IT Flexibility and new platforms

Increased Customer Awareness

and Price Sensitivity

3

H1 2020 Pricing Survey

4iStock-663981890

We use GLMs for the majority

(50–80%) of covers

We combine traditional and machine

learning (ML) approaches for nearly

all risk and demand (renewals/new

business) models

We use Generalized Linear Models

(GLMs) for less than half of the covers

63%19%

19%

1Describe the statusof your technical sophistication

H1 – 2020 pricing survey

LIMA Programme 2020 5



We ran some pilots but it’s hard to

bring them to production level




business) models

Use ML in other projects (discounting,

customer lifetime value, etc.) other

than the others mentioned



Use some ML in demand models

Use some ML in risk models

63%19%

19%

16%

11%

27%

46%


2Describe your use of machine learning (ML)in pricing today







We manually import rating tables

through our IT system, and we would

like to move to API




business) models




We have an API connection between

our pricing tool and the rating

system/policy administration system



Use some ML in demand models We manually import rating tables

through our IT system, and we do

not see reason to change it


63%19%

19%

16%

11%

27%

46%49%

38%

14%



3Describe your level of automation of pricing ina rating engine system









like to move to API

It’s better to use professional

pricing software from a known

brand in combination with coding

abilities where needed




business) models







It’s better to hard-code all your

models in SAS, R, Python, etc.






We are considering switching to a

different pricing software provider


63%19%

19%

16%

11%

27%

46%49%

38%

14%

67%18%

15%




4Describe your use of proprietary software









like to move to API

It’s better to use professional

pricing software from a known

brand in combination with coding

abilities where needed

We feel our technical sophistication

is in line with our peers




business) models







It’s better to hard-code all your

models in SAS, R, Python, etc.

We should ramp up our sophistication

level because we’re lagging behind






We are considering switching to a

different pricing software provider

We are way ahead, and we can see

the benefit of our early move in

this space


63%19%

19%

16%

11%

27%

46%49%

38%

14%

67%18%

15%22%

27%

51%




4Describe your use of proprietary software

5What do you observe in your market/within your competitors



Prediction Decision making

Pillars

Pricing excellence framework

Data management Domain knowledge Deployment

Architrave Technical price

Using the past to predict the expected claim costs

Behavioral modelling

Customer buying behaviour and portfolio elasticity

Competitive market analysis

Taking into account market positioning to improve pricing adequacy

Rate calibration

Transforming the technical prediction in a commercial rate

Scenario testing

Assessing the impact of any rate change and the effect on the

expected volume and profitability

Portfolio steering & pruning

Identifying the loss/profit making segment and the strategy to

improve the profitability

▪ Data availability

▪ Storage infrastructure

▪ Business impact of analytical actions

▪ Regulatory environment

▪ Fast implementation and execution

▪ Synergy across software solutions


iStock-840166882

DATA MANAGEMENT 1

Leveraging structured internal information

LIMA Programme 2020

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

12

How to use 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

Demand modelling

Rate making

Risk Network Analytics

Risk Modelling

Telematics

Fraud Identification

Data cleaning

Risk Modelling

Demand Modelling

Risk Modelling

Demand Modelling

Microzoning

Risk Network Analytics


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


iStock-918855102

Prediction 2

The race of algorithms

Neural

Networks

1950

1972

1990

1995

1996

1999

GLMs

GAMs

Random

Forests

Penalized

regression

GBM

Time


Technical pricing is your “compass”

Profit

Loss?

or Commercial ViewRisk View

100

Technical

Price

Risk Loadings Expenses

90

120

Commercial PremiumActual Premium


LIMA Programme 2020

Choosing the right granularityS

oph

istication

Freq Sev Propensity Excess Freq Sev Freq Sev SevFreq

Product

Risk

Premium

Bodily Injury Material Damage Collision Fire & Theft

TPL Own Damage

Comprehensive

ExcessAttritional

Cover

Peril

18

Behavioural modelling: beyond the risk estimationPaul & Henry are twins …

HenryPaul

≠

=

=INSURANCE

ABOUT

PROFESSION

AGE 38

REGION Munich

VEHICLE BMW 120i F20

Successful Popstar Unemployed

AGE 38

REGION Munich

VEHICLE BMW 120i F20

300 €CURRENT PREMIUM

330 €RENEWAL OFFER

300 €CURRENT PREMIUM

330 €RENEWAL OFFER

Image: gmast3r / Getty Images LIMA Programme 2020 19

Renewal Scenarios

Scenario A

Scenario B

Insurance

+10%

+10%

Paulreceives a proposal of

330 €

Henryreceives a proposal of

330 €

He will renew

He will not renew

+15%

+5%

Paulreceives a proposal of

345 €

Henryreceives a proposal of

315 €

He will renew

He will renew

330 €EARNED PREMIUM

70%LOSS RATIO

99 €MARGIN

660 €EARNED PREMIUM

70%LOSS RATIO

198 €MARGIN

Paul and

Henry both

receive the

SAME offer

Paul and

Henry both

receive an

ADEQUATE

offer

Image: gmast3r / Getty Images LIMA Programme 2020 20

LIMA Programme 2020

Measuring the client and portfolio elasticity

… What is the probability

of converting/renewing?

How does the probability

of converting/renewing

change?

Demand Elasticity

If we offer a premium of

$200 …

Elasticity allows to measure and simulate the impact of a price change on the volume sold

e =∆% 𝑑𝑒𝑚𝑎𝑛𝑑

∆% 𝑝𝑟𝑖𝑐𝑒

21

Outcomes

LIMA Programme 2020

Competitive market analysis

▪ Competitors’ pricing

variables identified

▪ Marginal impact of each

pricing variables

▪ Implementable formula(s)

replicating competitors’

commercial premium

Profile ID Vehicle Brand Age Driver Region Price Competitor 1 Price Competitor 2

P0 BMW 35 Region 1 200 220

P1 BMW 20 Region 1 500 230

P2 BMW 35 Region 2 210 200

P3 … … … … …

Vehicle Brand Age Driver Region

Modality Coefficient Modality Coefficient Modality Coefficient

Audi 1 18–25 2.5 Region 1 1

BMW 1 26-50 1 Region 2 1.3

Nissan 0.8 >50 0.9 Region 3 1.1

Commercial Price Competitor 1 =

Base Premium × Coeff Vehicle Brand ×

Coeff Age ×Fixed Cost Region

Advanced Predictive Modelling Techniques

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Combining all the predictions…

LIMA Programme 2020

Risk Models

Behavioral Model +

CMA

Possible applications

▪ Price optimization at single

customer level

▪ Accurate definition of the renewal strategy

through an advanced system of caps and floors

▪ Identification of elastic customer to be retained

with specific marketing campaign

Impacts

▪ Reduction of the Loss Ratio

▪ Top-line growth (volume)

▪ Expense reduction and

CoR improvement

23

….to prepare the ground for the right decisions

LIMA Programme 2020

Reverse

engineering

Competitive

market analysis

so

ph

istica

tion

Rate

requirement

Risk models &

Technical price

Price elasticity

Segment

definition

Demand

modellingPrice

optimization

Impact Analysis

Scenario testing

Multi – year

projection

24

iStock-479446229

Decision Making 3

Understand the current status to set the right strategy

Exp

ecte

d L

oss R

atio

Expected retention

Bottom line driven strategy

Scenario 2

Top line driven strategy

Scenario 1

Current status


0

50

100

150

200

15 30 45 60 75 100

Expe

cte

dpro

fit(%

)

Percentage of clients (%)

LIMA Programme 2020

Pruning the portfolio with a forward-looking approach

The following 45% of clients contributes

with a further 70% of profits…

…which is destroyed by the remaining

40% of clients

The best 15% of clients generates

100% of profitability

15 60 100

27

Domain knowledge wins over algorithms

2 1

Algorithms

Domain

knowledge

Domain

expertise

Algorithms


iStock-528612314

Deployment 4

LIMA Programme 2020

Shifting the power from IT to the pricing department

Rating Tables

Scenario Testing tool

ML Algorithm

ClientPolicy Admin System

Versioned pricing

IT departmentPricing department

30

49% of the interviewed clients is manually

import rating tables through their IT system,

and we would like to move to API

TechneGCU’s service bundle to boost your technical excellence

Technical Price

Roadmap

New Business

Pricing Strategy

Augmented Renewal

Pricing Strategy

▪ We identify the main risk drivers, blending traditional methods with the most recent ML

techniques to enhance the segmentation of your portfolio.

▪ Thanks to our RE_smoother tool we improve the predictiveness of geographical

or vehicle variables, enriching your portfolio with external information

▪ TimeMapper: a tool which enables you to leverage advanced spatial analysis to improve

the risk modelling prediction for your property book, pivoting on Risk Network Analytics

▪ We help you define your commercial rate for new business, identifying the profitable

target clients

▪ Through the reverse engineering of the market premiums, we support you in defining your

strategic positioning compared to the competitors and set up your go-to rates for any market

▪ Modeling the conversion probability i.e. the elasticity of a customer at the point of sale, we

help you optimize your competitive positioning on aggregators and price comparison websites

▪ We work with you to develop an advanced renewal strategy to optimize

performance KPIs such as the expected profit or the expected volume

▪ Thanks to sophisticated behavioral models, we identify the client elasticity and

factor the propensity to renew within the final premium, if the market allows it

▪ IDIAL is a MR tool which helps you defining and setting cap and floor system based on

advanced analytics, forecasting the evolution of the portfolio over a multi-year horizon


TechneGCU’s service bundle to boost your technical excellence

Portfolio pruning

and steering

Pricing Sophistication

Assessment Tool

Pricing strategy &

Underwriting advisory

▪ We use predictive modelling to measure the expected profitability of the book, considering the

value of the single policy over a multi-year time horizon.

▪ We use leakage and cross-subsidy analysis to measure the “wellness” of the portfolio to

identify loss making and profit-making clients along the future history of the portfolio.

▪ We measure the economic benefit of pruning, selecting only risks which will not be profitable

over the time horizon, maximizing the efficiency of the impact.

▪ Thanks to the Pricing Sophistication Assessment Tool we benchmark your pricing processes

and methodology against your peers and the market

▪ Based on the outcome, we identify the areas of improvement and we help clients improving

the pricing governance and pricing flow

▪ We set up pricing and underwriting committee to help insurtech and digital players

in defining the best pricing strategy and portfolio steering

▪ Thanks to the MR platforms we provide our clients with the most advanced cloud-based

monitoring solution to follow the growth of the portfolio in real time and act quickly

▪ We helps insurers to build their pricing team, identifying the right profiles based

on the client needs


Thank you

Massimo CavadiniHead of Actuarial Consulting and Data Analytics

Professional experience

▪ Senior consultant advising clients on improving the portfolio

performance through the technical excellence

▪ Senior pricing analyst in the Global P&C of Generali Group

▪ Pricing analyst in the Global P&C of Allianz SE

Non project related experience

▪ Implemented an international network of pricing experts

▪ Building pricing capabilities and best practices

Clients/projects

• Leading the Actuarial and Data Analytics practice within the Global

Consulting Unit

• Pricing committee member – direct insurance companies

• Designing pricing strategies for traditional companies and start-ups

• Extensive experience in risk modelling, demand modelling and price

optimization in Europe, LATAM and MENA region.

• Coordinating ML and AI projects to improve and enhance the technical

performances.

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E-Mail

Tel

Mobile

LinkedIn

[email protected]

+49 (89) 3891 8805

+49 160 9642 6759

https://de.linkedin.com/in/massimo-

cavadini-2376b837

Team

Location

Start date

Languages

Actuarial Consulting & Data Analytics

Munich

July 2017

English, Italian

LIMA Programme 2020

Disclaimer/Important Notice

LIMA Programme 2020

© 2020 Münchener Rückversicherungs-Gesellschaft Aktiengesellschaft in Munich ("Munich Re"). All rights reserved.

This presentation is for information purposes only. It is not a binding offer. Any obligations between the parties can only be imposed by a binding written

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Munich Re has used its discretion, best judgement and every reasonable effort in compiling the information and components contained in this presentation. It may not be

held liable, however, for the completeness, correctness, topicality and technical accuracy of any information contained herein. Munich Re assumes no liability with

regard to updating the information or other content provided in this presentation or to adapting this to conform with future events or developments.

Image: used under licence from shutterstock.com

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lima primary insurance pricing · henry receives a proposal of 330 € he will renew he will not...

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